Forecasting for the Future: New science for improved weather, water, ocean and climate services - Abstracts of the Bureau of Meteorology Annual R&D Workshop, 25 November to 28 November 2019, Melbourne, Australia
Diana Greenslade, Leon Majewski, Linden Ashcroft, Jaclyn Brown, Christine Chung, Chantal Donnelly, Justin Freeman, Deryn Griffiths, Val Jemmeson and Ian Smith (editors)
November 2019
Bureau Research Report - 040
FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
Forecasting for the Future: New science for improved weather, water, ocean and climate services - Abstracts of the Bureau of Meteorology Annual R&D Workshop, 25 November to 28 November 2019, Melbourne, Australia
Diana Greenslade, Leon Majewski, Linden Ashcroft, Jaclyn Brown, Christine Chung, Chantal Donnelly, Justin Freeman, Deryn Griffiths, Val Jemmeson and Ian Smith (editors)
Bureau Research Report No. 040
November 2019
National Library of Australia Cataloguing-in-Publication entry
Author: Bureau of Meteorology Annual R&D Workshop, Forecasting for the Future: New science for improved weather, water, ocean and climate services (2019: Melbourne, Victoria)
Title: Forecasting for the Future: New science for improved weather, water, ocean and climate services - Abstracts of the Bureau of Meteorology Annual R&D Workshop, 25 November to 28 November 2019, Melbourne, Australia
ISBN: 978-1-925738-10-0 (E-Book)
Series: Bureau Research Report – 040
FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
Enquiries should be addressed to:
Diana Greenslade
Bureau of Meteorology GPO Box 1289, Melbourne Victoria 3001, Australia
Workshop Sponsors We would like to acknowledge and thank the following sponsors for their participation in this conference
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
TABLE OF CONTENTS
Foreword ...... 1 Seamless Model Development For Future Weather And Climate Services ...... 3 Sean Milton The Benefits Of Increased Model Resolution, Coupling And Improved Physics To Forecast Skill: A Case Study In Northern Queensland ...... 4 Matthew Hawcroft, Sean Milton, Tim Cowan, Sally Lavender and Alison Stirling On The Resonances And Teleconnections Of The North Atlantic And Madden-Julian Oscillations ...... 5 Gilbert Brunet, Yosvany Martinez, Hai Lin and Natacha Bernier Towards Seamless National Water Services – Past To Future ...... 6 Chantal Donnelly, Andrew Frost, Julien Lerat, Louise Wilson and Wendy Sharples Offshore Industry Future Needs ...... 7 Jan Flynn Rainfall Radar And Hydrology ...... 7 Urs Baeumer Using Bureau Of Meteorology Services To Predict Bushfire: An End-User Perspective ...... 8 Tim Wells Application Of Climate Data And Knowledge To The Electricity Sector ...... 8 Ben Jones Water Security In The Murray Darling Basin: The Role Of Climate Change ...... 8 Rob Vertessy What We've Learned From Observing Antarctic Weather And Climate ...... 9 Scott Carpentier, Jan Lieser, Phil Reid, Andrew Klekociuk, Ben Galton Fenzi and Tas Van Omen Importance Of Metrology - Why It Matters In A Non-Normal World ...... 10 Jane Warne Farmers’ Use Of Weather And Forecast Information In The Western Australian Wheatbelt . 11 Marit Kragt and Myrtille Lacoste Analysis Ready Data Validation And The Wonderful World Of Drones ...... 12 Mark Broomhall, Guy Byrne, Andrew Walsh And Medhavy Thankappan Validation Of Cloud Properties Derived From Himawari-8 Geostationary Satellite: Advancement And Challenges ...... 13 Yi Huang, Steven T. Siems, Michael J. Manton, Alain Protat, Leon Majewski and Hanh Nguyen Shasta For Modelling, Simulation, Analytics And AI For Weather And Climate ...... 14 Ilene Carpenter To Be Confirmed ...... 14 Ben Evans
FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
Life In A Post Exascale World ...... 15 Wendy Sharples, Klaus Görgen and Chantal Donnelly Upcoming HPC Upgrade And HPC Services Roadmap ...... 16 Bruce Arthur Remote Sensing And Data Assimilation For The Characterization Of The Terrestrial Water Cycle ...... 17 Sujay Kumar Steps: Radar Rainfall Nowcasts For Decision Support In An Age Of Uncertainty ...... 18 Alan Seed Lightning Nowcasting At Weatherzone - A Current And Future Perspective ...... 18 Rashmi Mittal Pyrocumulonimbus Firepower Threshold ...... 19 Kevin Tory, Jeffrey Kepert, David Wilke and Zachary Berry-Porter Present And Future Of Ocean Forecast In New Zealand — New Perspective Under The Moana Project ...... 20 Joao De Souza Developments In ACCESS-C And CE: A Service Focussed Perspective ...... 21 Gary Dietachmayer for the C3/CE3 Project Team Developments For Global Ocean Sea-Ice And Sub-Mesoscale Regional Ocean Forecasting .. 22 Gary Brassington, Pavel Sakov, Prasanth Divakaran, Justin Freeman, Mirko Velic, Helen Beggs, Peter Oke, Paul Sandery, Matt Chamberlain, Russel Fiedler, Andy Hogg, Andrew Kiss and Petra Heil Research And Operational Advances In Short Range Hydrological Forecasting In Australia .. 23 Aynul Kabir, Prasantha Hapuarachchi, Mohammed Hasan, Sophie Zhang, Jayaratne Liyanage, Patrick Sunter, Nikeeth Ramanathan, Fatemeh Mekanik, Mohammed Bari, Narendra Tuteja, Daehyok Shin, David Robertson, Durga Shrestha and James Bennett Developments In AUSWAVE ...... 24 Stefan Zieger and Diana Greenslade The National Blend Of Models: A Statistically Post-Processed Multi-Model Ensemble ...... 25 Jeffrey Craven Guidance Post Processing Present And Future ...... 26 Anja Schubert, Gary Weymouth, Thomas Gale, Tim Hume, James Canvin and Andrew Charles IMPROVER: A New Probabilistic Guidance Post-Processing System ...... 27 Gary Weymouth and Thomas Gale Automating The Warning Process At Deutscher Wetterdienst (DWD) – Lessons Learned ..... 28 Hans-Joachim Koppert and Franz-Josef Molé A Streamlined Forecast Process Utilizing Forecastbuilder And The Associated Culture Change ...... 29 Andrew Just To What Extent Can We Automate Routine Forecast Production? ...... 30 Michael Foley, Deryn Griffiths, Tom Pagano and Nick Loveday
FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
Value-Adding By Operational Meteorologists ...... 31 Jenny Sturrock Improving Client Decision Making ...... 32 James Crow And Matthew Boterhoven Risk Communication ...... 33 Matalena Tofa, Melanie Taylor, Katharine Haynes and Gemma Hope A New Convection Scheme For The UM: Idealised Sensitivity Experiments ...... 34 Sally Lavender, Michael Whitall, Alison Stirling and Rachel Stratton Impacts And Predictability Of Australian Wintertime Minimum Temperatures Driven By The MJO ...... 35 Guomin Wang and Harry Hendon Impact Of Southern Hemisphere Stratospheric Polar Vortex Weakening On Australian Climate Extremes ...... 36 Eun-Pa Lim, Harry Hendon, Ghyslaine Boschat, Debra Hudson, David Thompson, Andrew Dowdy and Julie Arblaster Challenges In Assessing Skill Of Seasonal Forecasts In Australia ...... 37 Andrew King, Debra Hudson, Eun-Pa Lim, Andrew Marshall, Harry Hendon, Todd Lane, Benjamin Henley, Tim Cowan and Oscar Alves Prediction Of The Extreme Conditions Associated With The Floods In Northern Queensland In ACCESS-S1 ...... 38 Tim Cowan, Matthew Wheeler, Oscar Alves, Sugata Narsey, Catherine De Burgh-Day, Morwenna Griffiths, Chelsea Jarvis, David Cobon and Matthew Hawcroft Seasonal Forecasting For Marine Management Around Australia In A Changing Climate ..... 39 Claire Spillman, Grant Smith, Alistair Hobday, Jason Hartog, Catherine De Burgh-Day and Paige Eveson Climate Outlooks—The Bureau's Weekly, Monthly And Seasonal Forecasting Services ...... 40 Jonathan Pollock, Robyn Duell, William Wang, Avijeet Ramchurn, Elise Chandler, Kevin Keay, Felicity Gamble, Catherine Ganter and Andrew Watkins Applying Real-Time ACCESS-S Forecasts For Water Availability Forecasting ...... 41 Andrew Schepen, David Robertson and Cuan Petheram Weather Your Way ...... 42 Christine Killip and Esteban Abellan Towards A Seasonal Landscape Forecasting Service For Australia ...... 43 Julien Lerat, Elisabeth Vogel, Chantal Donnelly, Sean Loh, Andrew Frost and Wendy Sharples Tailoring Seasonal Forecasts To Match The Requirements Of Australian Farming Systems .. 44 Patrick Mitchell and Jaclyn Brown Validation And Impact Of Seasonal Pasture Availability And Lamb Growth Predictions In Askbill ...... 45 Johan Boshoff Innovative Pathways For Delivering Actionable Weather And Climate Information ...... 46 Jaclyn Brown
FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
Operational Climate Services In Europe: The Case Of The Copernicus Climate Change Service ...... 47 Carlo Buontempo Climate Forecast Services ...... 47 David Jones An Overview Of The Bureau's Current Activities In Hydrological Projections And Their Links To Future Climate Services ...... 48 Louise Wilson, Chantal Donnelly, Pandora Hope, Wendy Sharples, Justin Peter, Elisabeth Vogel, Sean Loh, Jake Roussis, Margot Turner, Stuart Baron-Hay and Ulrike Bende-Michl Bushfires And Weather Extremes: Seamless Products From Seasonal Prediction To Climate Projections ...... 49 Andrew Dowdy Climate Services For Climate Risk ...... 50 Karl Braganza The Rate At Which We Will Experience Unprecedented High Temperature Extremes: Benefits And Limitations Of Reducing Greenhouse Gas Emissions ...... 51 Scott Power and Francois Delage Climate Change Projections For Northern Australian Rainfall ...... 52 Sugata Narsey Victorian Climate Projections 2019: Turning Projection Datasets Into Projections ...... 53 John Clarke, Marcus Thatcher, Michael Grose, Vanessa Hernaman, Craig Heady, Tony Rafter, Vanessa Round, Claire Trenham and Tim Erwin Diving Into Weather Observations For Non-Traditional Uses ...... 54 Simon Allen and Joel Lisonbee Climate Analysis Forecasting Ensemble (CAFE) System – Early Results From The CSIRO Decadal Climate Forecasting Project ...... 55 Richard Matear and the Decadal Climate Forecasting Team Plans For The Next Generation Of National And Regional Climate Projections For Australia 56 David Karoly Climate Services In The UK Met Office - Challenges And Solutions ...... 56 Chris Hewitt Integrating Climate Projections Into State Government Decision-Making ...... 57 Ramona Dalla Pozza, Clare Brownridge, Jacqueline Thurgood and Geoffrey Steendam National Climate Science Advisory Committee: Next Steps ...... 57 Chris Johnson Improving Climate Literacy Through Community And TV News ...... 58 Ailie Gallant, David Holmes, Remy Shergill, Stephanie Hall, Zoe Gillet, James Goldie and Steven Thomas
FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
Beach Erosion Early Warning System (EWS): A New National Research Initiative ...... 59 Ian Turner, Neil Carroll, Bruce Coates, Michael Cuttler, Diana Greenslade, Jeff Hansen, David Hanslow, Mitchell Harley, Michael Kinsela, Christopher Leaman, Fangjun Li, Ryan Lowe, Nashwan Matheen, Neal Moodie, Craig Morrison, Nathaniel Plant, Kristen Splinter, Hillary Stockton, Adrian Turnbull and Stefan Zieger Wave Overwash Warning At A Social Media Blackspot ...... 60 David Hanslow, Michael Kinsela, Hannah Power, Caio Stringari and Murray Kendall The Impact Of Damaging Winds On Residential Buildings ...... 61 Harald Richter, C. Arthur, D. Wilke, S. Martin, M. Wehner, M. Dunford, E. Ebert, J. Sexton and C. Mooney Future Warnings Policy ...... 62 Carla Mooney, Shannon Panchuk, Megan O'Donnell, Brenda Mackie and Richard Hammond Meteorological Data And Machine Learning: A View From The Customer ...... 63 Will Dubyak Machine Learning And Environmental Modelling In An Exa-Scale World ...... 63 Peter Steinle Machine Learning Applications For Weather/Climate ...... 64 Huidong Jin Using Machine Learning To Improve Operational Wave Forecasts ...... 65 Jeff Hansen, Chen Wu, Phil Watson and Diana Greenslade5 To Be Confirmed ...... 66 Werner Scholz Machine Learning At Monash University And Potential Applications In Meteorology ...... 66 Ann Nicholson Enhancing Digital Content With Machine Learning And Augmented Reality ...... 66 Justin Freeman To Be Confirmed ...... 66 Matthew Greensmith
FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
FOREWORD
The Bureau of Meteorology's Annual Research and Development workshop 2019, the 31st workshop in the series, has the theme "Forecasting for the Future: New science for improved weather, water, ocean and climate services".
Environmental forecast services range from routine daily weather forecasts for the public, to seasonal outlooks and climate projections aimed at informing decisions by agriculture and water managers. Emergency managers rely on highly customised services during times of extreme weather such as fires, floods, heatwaves and tropical cyclones. Industry specific advice on the influences of climate variability and change enables society to deal with the challenges posed by our unique climate, both today and in the future.
In order to meet the increasing demands of customers and deliver greater impact and value, service providers are transforming the way in which they work. This transformation process requires significantly enhanced capability in science and technology. Key advances that enhance our abilities to forecast from hourly to decadal scales include:
• developments in Earth system modelling and high performance computing, • improving our ability to use an increasing diversity and number of observations, • extended forecast automation at all time scales, • the application of machine learning, • the development of impact-based forecasting, • improvements in the communication and use of uncertainty information, and • closer engagement with social and economic sciences.
This year the workshop aims to bring together experts from across these fields to discuss ways we can work together to provide the Australian and international community with improved services and improved decision-making abilities, resulting in greater impact and value.
The workshop is organised around a number of themes.
The opening session on Day 1 is Seamless Forecasting, dealing with challenges such as seamlessly transitioning forecasts and services between synoptic and seasonal time-scales. A session on Applications follows, where we hear from some of our key customers about working together to deliver greater impact and value. The second half of the day includes sessions dedicated to critical infrastructure: Observations and High Performance Computing.
Day 2 starts with Nowcasting – the challenge of using observations and/or models to provide knowledge of present conditions and very short-term forecasts. Then we move into several sessions around Short- range forecasting, covering developments in our numerical modelling systems techniques to post- process model output, improve forecast automation and communicate forecasts to users.
The first half of Day 3 relates to Seasonal Forecasting, with talks covering physics and model development. We also hear from internal and external groups on value-adding to our seasonal predictions to deliver greater impact and value. We then move into sessions on Climate, and some of
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the major climate projects within Australia and overseas, with a focus on developing and communicating climate services.
Day 4 includes a session on Impact Forecasting, and the challenge of moving beyond forecasting what the weather (or ocean) will be, to forecasting what it will do. The workshop sessions conclude with Machine Learning – a technique gaining a reputation as an efficient and effective way to improve forecasts and the forecast process.
We are pleased to welcome a number of international and national experts who have been invited to give keynote presentations relating to the theme. Keynote speakers include:
• Dr Carlo Buontempo, ECMWF • Dr Ilene Carpenter, Cray Inc. • Scott Carpentier, Bureau of Meteorology • Jeff Craven, U.S. National Weather Service • Dr William Dubyak, US Automobile Association • Dr Jan Flynn, Woodside Energy Ltd. • Dr Ailie Gallant, Monash University • Matthew Greensmith, Amazon Web Services • Andrew Just, U.S. National Weather Service • Prof David Karoly, CSIRO • Hans-Joachim Koppert, Deutscher Wetterdienst • Dr Sujay V Kumar, NASA Goddard Space Flight Center • Dr Sean Milton, Met Office • Prof Ann Nicholson, Monash University • Dr Joao de Souza, Metocean Solutions • Dr Kevin Tory, Bureau of Meteorology • Prof Ian Turner, UNSW
The workshop is sponsored by the Bureau of Meteorology, CSIRO, Cray, National Computational Infrastructure (NCI) and Copernicus Climate Change Service. We would like to thank these sponsors for their generous support of the workshop and in particular, our Gold Sponsor, Cray.
As Co-convenors of the workshop organising committee, we would also like to thank the other members of the committee: Linden Ashcroft (University of Melbourne), Jaclyn Brown (CSIRO), and from the Bureau of Meteorology: Christine Chung (replacing Aurel Moise), Chantal Donnelly, Justin Freeman and Deryn Griffiths. Workshop administration was ably provided by Val Jemmeson with assistance from Sandra Marriot and Tony Baldwin, and Ian Smith is also thanked for his assistance with this book of abstracts.
Diana Greenslade and Leon Majewski Science to Services, Bureau of Meteorology Workshop Co-convenors
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SEAMLESS MODEL DEVELOPMENT FOR FUTURE WEATHER AND CLIMATE SERVICES
Sean Milton
Met Office, Exeter, UK
There is a growing societal and economic demand for timely and accurate predictions of environmental, weather and climate related risk. Future services must cover a wide range of products and sectors from extreme weather and environmental hazards a few days to a week ahead, to sub-seasonal and seasonal prediction of circulation anomalies, regime changes and associated high impact weather/hazards, right up to advice to governments on future climate projections. A key link in the chain from science to service is the development of accurate and efficient coupled numerical models of atmosphere, ocean, land, sea- ice and other earth-system processes that underpin weather and climate services. This presentation discusses the opportunities and challenges of a seamless/unified approach (Brown et. al., 2012) to developing models for future weather and climate services, and draws on experiences from the model development programmes and service provision at the Met Office.
Seamless model development potentially brings many advantages in developing modelling systems that can encompass a wide range of physical phenomena and are evaluated and tested across a range of space and time scales exploiting the synergies between weather and climate (Senior et. al., 2010). However, there are also many challenges in managing differing requirements for model resolution, earth system complexity, ensembles design and data assimilation at different timescales. Some key challenges and opportunities discussed will include (1) responding to technological change in designing the next generation of models for exascale computing and exploiting growing opportunities in data science, cloud computing and machine learning, (2) moving global predictions towards the km scale, while (3) continuing to develop techniques to understand and correct robust model systematic errors that still limit weather and climate predictions, (4) developing affordable earth system complexity at weather prediction timescales, drawing on experience gained from seasonal and climate prediction. This includes running km scale coupled regional models and research into developing aerosols, chemistry and air quality capabilities. Finally, given the enormous challenges associated with developing seamless prediction systems this activity must be done in collaborative partnerships.
References
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., Shelly, A., 2012: Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey. Bulletin of the American Meteorological Society 93, 1865–1877.
Senior, C.A. et. al., 2010: Synergies between NWP & GCMs. Chapter in The development of atmospheric GCMs: Complexity synthesis and computation. Eds. Leo Donner, Richard Somerville and Wayne Schubert. Cambridge University Press.
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THE BENEFITS OF INCREASED MODEL RESOLUTION, COUPLING AND IMPROVED PHYSICS TO FORECAST SKILL: A CASE STUDY IN NORTHERN QUEENSLAND
Matthew Hawcroft1,2, Sean Milton2, Tim Cowan1,3, Sally Lavender1,2 and Alison Stirling2
1CACS, University of Southern Queensland, Toowoomba, Australia 2Met Office, Exeter, UK 3Bureau of Meteorology, Melbourne, Australia
Improving multiweek predictability of extreme weather events gives the capacity for decision makers to take precautionary action to mitigate the impact of the incoming event. Spatially detailed forecast skill at lead times beyond a few days remains challenging in many situations, making these improvements extremely difficult to deliver. Here we introduce an evaluation of extended ensemble forecasts based on the Met Office numerical weather prediction (NWP) system, assessing where additional skill may be sourced from increased model resolution, improvements in model physics and through using a coupled NWP system. In doing so, we use a process-based framework to assess how and why these benefits are yielded.
In this work, we apply this analysis framework to evaluate prediction skill to a quasi-stationary monsoon depression situated over northeast Australia earlier this year which caused devastating floods, killing over 500,000 head of cattle in northwest Queensland, and inundating over 3,000 homes in the coastal city of Townsville (Cowan et al., 2019). We show that although this event appeared to have relatively low prediction skill at lead times beyond a few days, clear improvements in forecast skill can be seen from the coupled model and we demonstrate why this is the case.
References
Cowan, T., Wheeler, M.C., Alves, O., Narsey, S., de Burgh-Day, C., Griffiths, M., Jarvis, C., Cobon, D.H. and Hawcroft, M.K. 2019: Forecasting the extreme rainfall, low temperatures, and strong winds associated with the northern Queensland floods of February 2019, Weather and Climate Extremes. https://doi.org/10.1016/j.wace.2019.100232.
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ON THE RESONANCES AND TELECONNECTIONS OF THE NORTH ATLANTIC AND MADDEN-JULIAN OSCILLATIONS
Gilbert Brunet1, Yosvany Martinez3, Hai Lin2 and Natacha Bernier2
1Science and Innovation, Bureau of Meteorology, Australia 2Science and Technology, Environment and Climate Change Canada 3Meteorological Service of Canada, Environment and Climate Change Canada
The key to better prediction of seasonal to sub-seasonal (S2S) variability and weather regimes in a changing climate lies with improved understanding of the fundamental nature of S2S phase space structure and associated predictability and dynamical processes. The latter can be decomposed into a finite number of relatively large-scale discrete-like Rossby waves with coherent space-time characteristics using Empirical Normal Mode (ENM) analysis. ENM analysis is based on principal component analysis, conservation laws and normal mode theories. These modes evolve in a complex manner through nonlinear interactions with themselves and transient eddies and weak dissipative processes. Within this atmospheric dynamic framework, we will discuss the teleconnections and the 35- day wave resonance of the North Atlantic Oscillation.
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TOWARDS SEAMLESS NATIONAL WATER SERVICES – PAST TO FUTURE
Chantal Donnelly1, Andrew Frost2, Julien Lerat3, Louise Wilson4 and Wendy Sharples4
1 Water Investigations, Bureau of Meteorology, Brisbane, Australia 2 Water Investigations, Bureau of Meteorology, Sydney, Australia 3 Water Investigations, Bureau of Meteorology, Canberra, Australia 4 Water Investigations, Bureau of Meteorology, Melbourne, Australia
The Bureau of Meteorology's national water resource modelling capability is nearing the production of seamless water services across Australia, from the past, through forecasting time-scales to projections of future climate impacts. Currently, the Australian Landscape Water Balance (www.bom.gov.au/water/landscape/) provides national, gridded, historical to near-real time daily predictions of water balance variables: soil moisture, actual and potential evapotranspiration, runoff and deep drainage. Behind this service lies the Australian Water Resource Assessment Landscape model (AWRA-L), a national, gridded water balance model co-developed by the Bureau of Meteorology and CSIRO. Here, we show how this service has been extended to provide near seamless services across time-scales.
Seamless weather and climate risk modelling have been identified as a grand challenge of priority for the meteorological community and its customers. In the meteorological and climatological community, numerical weather prediction models (NWPs), general and regional circulation models (GCMs, RCMs) are run using different domains, resolutions, physics schemes, inputs, assimilation data and initial conditions for historical reanalysis, short-term, medium and seasonal forecasting, and decadal climate projections. Each of these model variations is subject to different model biases, making the bringing together of historical analyses with forecasting and projections difficult for users. However, the nature of hydrological modelling, where: (i) the same model is often used for all time-scales, historical through to projections, (ii) model calibration is performed using reference forcing data sets, and (iii) the forcing data from NWPs, GCMs or RCMs is post-processed (bias-corrected and downscaled) to match the same reference forcing data, results in a temporally comparable system across historical, prediction and future projection time-scales.
The functionality of AWRA-L has recently been extended to provide ensemble short-term and seasonal forecasts and projections of future climate impacts on the water cycle, thus progressing towards providing near seamless water information for Australia. This talk will give an overview of the current and soon to be released services, how they can be used across time-scales by our customers, and future planned research towards more seamless services.
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OFFSHORE INDUSTRY FUTURE NEEDS
Jan Flynn
Woodside
NO ABSTRACT
RAINFALL RADAR AND HYDROLOGY
Urs Baeumer
Department of Transport and Main Roads, Queensland Government, Brisbane, Australia
Current practice by industries in developing flow estimations is to calibrate hydrologic models against recorded rainfall and recorded stream flow data. A downfall of this approach is that historic rainfall is only captured at rainfall stations and not over the entire catchment. An innovative approach is to use the gridded precipitation estimates based on rainfall radar which is provided by the Bureau of Meteorology as part of their Rainfields product. Following the 2019 severe weather event in North Queensland, which damaged the Alice River bridge on Hervey Range Road, west of Townsville, the Department of Transport and Main Roads undertook a case study to assess the suitability of the available Rainfields data. This presentation will provide insights into the potential and suitability of current rainfall radar products in hydrologic assessments.
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USING BUREAU OF METEOROLOGY SERVICES TO PREDICT BUSHFIRE: AN END-USER PERSPECTIVE
Tim Wells
Bushfire Management, Country Fire Authority, Melbourne, Australia
Tim Wells regularly performs the Fire Behaviour Analyst role in Victoria to help with fire-agency bushfire readiness and response. He has also performed the role in New South Wales, Queensland and Canada. This presentation will start with a brief overview of the Fire Behaviour Analyst role in Victoria, followed by discussion about which Bureau of Meteorology products and services are most-used and most-valued by the analysts. The presentation will conclude with a summary of some key weather related challenges or questions that fire agencies are currently grappling with, including a wish list of future improvements.
APPLICATION OF CLIMATE DATA AND KNOWLEDGE TO THE ELECTRICITY SECTOR
Ben Jones
Australian Energy Market Operator
NO ABSTRACT
WATER SECURITY IN THE MURRAY DARLING BASIN: THE ROLE OF CLIMATE CHANGE
Rob Vertessy
Bureau of Meteorology, Melbourne Australia
NO ABSTRACT
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WHAT WE'VE LEARNED FROM OBSERVING ANTARCTIC WEATHER AND CLIMATE
Scott Carpentier1, Jan Lieser1, Phil Reid2, Andrew Klekociuk3 Ben Galton Fenzi3 and Tas Van Omen3
1Antarctic Meteorology, Bureau of Meteorology, Hobart, Australia 2Science and Innovation, Bureau of Meteorology, Australia 3 Australian Antarctic Division, Kingston, Australia
The unique, remote, pristine and often hostile Antarctic environment presents many challenges to established weather and climate observing practices. These challenges have flow on effects that limit downstream monitoring and predictive services skill.
The presentation will provide a brief and sweeping update on the more unique and interesting in-situ and remote systems used to observe the Antarctic ice sheet and shelves, sea ice, atmosphere and Southern Ocean. We will consider interesting things discovered from our efforts as well as highlight the impact that monitoring gaps have on prediction capabilities for the Antarctic and beyond; and the significance this has for decision makers.
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IMPORTANCE OF METROLOGY - WHY IT MATTERS IN A NON- NORMAL WORLD
Jane Warne
Bureau of Meteorology, Melbourne, Australia
Metrology, is the science of measurement and helps us to understand the idiosyncrasies of this world. We often assume that the world is normal, statistically, and well behaved but reality it is far from normal. All measurement are approximations for the knowledge we seek. This paper examines two examples of how we can improve our understanding of key climatic indicators through a better understanding of the measurement process and the elimination or minimization of measurement bias.
Sea- Level measurement The earliest sea-level measurements in modern times were taken using tide staffs. Over time this evolved to float gauges that recorded the sea-level on automated rotating drums (Dusto 2014). In the last forty to fifty years the predominant method of measuring sea-level has been to use time of flight measurement of sound to determine the distance of the water surface from fixed point. While these are a vast improvement on historical measurements, they are not without issues. This presentation will examine some of the issues that arise from the use of these instruments and impacts they have on longer term sea-level estimates.
Temperature measurement One of the most talked about indicators of climate change is temperature. The reliable and consistent estimation of air temperature is more difficult than is often assumed. The air temperature reported is an estimate of a physical representation of the thermal component of kinetic energy of the atmosphere, in reality it is a complex blend of the instruments used, the screen used house them and the surrounding environment. Each time we change the design of the sensor, use a different screen or change the way in which we determine the average temperature, we create a discontinuity in the temperature record.
Here we will present an outline of a large-scale experiment that is designed to better understand the influencing factors on temperature measurement in the real world. The aim is to gather sufficient information from a variety of environments and situations that would, in the long term, allow transition to "contactless" temperature measurement and a reliable thermodynamic estimations of the "real" temperature of the atmosphere.
References
Dusto, A., 2014: Reading Between the Tides 200 Years Measuring Global Sea-Level, NOAA, https://www.climate.gov/news-features/climate-tech/reading-between-tides-200-years-measuring- global-sea-level.
Moldover, M. R., R. M. Gavioso, R. M., Mehl, J. B., Pitre, L., de Podesta, M. and Zhang, J. T, 2013: es Acoustic gas thermometry, Metrologia, 51(1) https://doi.org/10.1088/0026-1394/51/1/R1
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FARMERS’ USE OF WEATHER AND FORECAST INFORMATION IN THE WESTERN AUSTRALIAN WHEATBELT
Marit Kragt and Myrtille Lacoste
UWA School of Agriculture and Environment: Agricultural and Resource Economics, University of Western Australia, Perth, Australia
Evaluating the economic benefits from investments in weather and forecast information is hindered by a lack of knowledge about how broadacre farmers use this information. It remains unclear which weather and forecast products farmers use, and which features are considered most important. Little is also known about how the available weather and forecast information impacts the management decisions of farmers, for instance how different forecast horizons impact farming practices along the year. This study is the first to address these gaps. The study was commissioned by the Australian Government Bureau of Meteorology in Western Australia to investigate the benefits of investments in new weather radars in the WA wheatbelt to the agricultural industry. Three new weather radars were installed in the wheatbelt between 2015-2017 near Newdegate, South Doodlakine, and Watheroo. These new radars have Doppler capability, which is expected to generate significant benefits to regional industries. Primary data from 51 farmers was collected using in-depth interviews in 3 locations of the Western Australian (WA) wheatbelt between July and October 2017. The interviews focused specifically on the ways in which weather and forecast information is being used by WA farmers.
Results From the detailed interviews with farmers, we gained an understanding of the practices that are likely to be impacted by improved weather monitoring and forecasts, and how different forecast horizons impact farming practices. We found that the majority of farmers have great confidence in the competence of the Bureau of Meteorology. Most farmers access multiple weather products, of which about half is provided by the Bureau. Farmers’ choices were justified by ease-of-use, performance, requirements for specific features, and the need to build an ‘overall picture’ by comparing several perspectives. Generally, farmers had greater confidence in short-range forecasts, which impacted their practices more than long-term forecasts. The practices most impacted by weather conditions and forecasts were general planning, spraying, and sheep management; the least was harvesting.
Conclusion The results of this study provide information for the Bureau of Meteorology’s investment decisions around funding new weather products. We provide recommendations on how the Bureau could improve extension of their services, including raising awareness of flagship products, investigating the product features most desired by farmers, and workshops to explain how different types of weather and forecasting products could be used in agricultural decision-making.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
ANALYSIS READY DATA VALIDATION AND THE WONDERFUL WORLD OF DRONES
Mark Broomhall, Guy Byrne, Andrew Walsh and Medhavy Thankappan
Digital Earth Australia, Geoscience Australia, Symonston ACT, Australia
Digital Earth Australia (DEA), primarily with the Landsat data archive and continuing data captures of Landsat and Sentinel-2 missions, has indexed medium resolution satellite data over Australia into a data cube, providing a platform for the development and production of time-series data products. The backbone of these products is the DEA Analysis Ready Data (ARD) archive. The promise of ARD sees users freed to undertake analysis and develop advanced products without having to first (geometrically and radiometrically) correct the data themselves to make it appropriate for their needs. According to some definitions, ARD is as simple as atmospherically corrected surface reflectance products but the DEA model goes much further and corrects the data to remove illumination and view effects of terrain (slope and shadow). The corrected products can be seamlessly used in time-series applications without the need for users to de-trend these data themselves. DEA uses these data to generate a number of products such as water observations from space and fractional land cover. The calibration and validation team is tasked with validation of the surface reflectance and ARD generated by DEA.
Stage 1 Validation A nationwide field calibration campaign was commenced in 2018 as a collaborative effort between GA, CSIRO and University groups. Using portable spectrometers, field measurements coincident with the time of overpass of Landsat-8 and Sentinel-2 (A and B) satellites were taken. These data were captured using multiple transect lines within 100 x 100 m blocks. Spectra collected in these areas were spatially averaged to match up with pixels from each satellite. These data were convolved to match the band characteristics of the satellite sensors and processed to match the products derived from the satellite data. In this way, direct pixel comparisons between surface and satellite data can be performed.
Stage 2 Validation or the Wonderful World of Drones The next stage of the validation campaign will be to collect surface data over sites that are neither flat nor homogeneous to test the ARD products over more complex surfaces. The requirement to capture data over surface targets such as trees, areas of significant slope, water or wetlands will require different retrieval methods. The use of drones provides significant versatility in both the measurements that can be taken, the scale at which measurements can be taken and the places in which these measurements can be taken. As well as sampling the same 100 x 100 m area using transect methods, drones make it possible to rapidly collect data at various view angles so that a validation of the correction model can be performed.
This talk will briefly introduce ARD production, describe the stage 1 field validation program and results, then discuss the technology and plans for the stage 2 validation program.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
VALIDATION OF CLOUD PROPERTIES DERIVED FROM HIMAWARI- 8 GEOSTATIONARY SATELLITE: ADVANCEMENT AND CHALLENGES
Yi Huang1, 2, Steven T. Siems2, 3, Michael J. Manton3, Alain Protat4, Leon Majewski4 and Hanh Nguyen4
1School of Earth Science, The University of Melbourne, Melbourne, Australia 2The ARC Centre of Excellence for Climate Extremes (CLEX), the University of Melbourne, Melbourne, Australia 3School of Earth, Atmosphere and Environment, Monash University, Melbourne, Australia 4Bureau of Meteorology, Melbourne, Australia
The advent of the Himawari-8 geostationary satellite represents a major revolution in spaceborne weather monitoring capacities over the Asia-Oceania region. As the science community starts to explore this new data source, validation studies are essential for quantifying retrieval errors and guiding improvements of retrieval algorithms. Among the retrieved cloud properties, accurate representations of cloud-top height (CTH) and cloud- top temperature (CTT) are critical for determining the impact of clouds on the Earth’s radiation budget. This study presents an evaluation of CTH and CTT retrievals provided by the Australian Bureau of Meteorology using the Advanced Baseline Imager (ABI) Cloud Height Algorithm (ACHA). The evaluation targets a large sector of the remote Southern Ocean where challenging conditions are commonly encountered but operational networks and in-situ reference measurements that can be used to evaluate satellite retrievals are extremely sparse. The retrieved CTH and CTT are evaluated using active shipborne radar-lidar observations derived from the “Clouds, Aerosols, Precipitation Radiation and atmospherIc Composition Over the southeRn oceaN” (CAPRICORN) experiment in 2016 and observations from the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud product. Results of the comparisons show that the Himawari-8 CTH (CTT) retrievals agree reasonably well with the shipborne and CALIOP estimates, with the Himawari-8 CTH (CTT) generally falling between the CTHs observed by CALIOP and the shipborne radar-lidar estimates. However, major systematic biases are also identified. These include (i) a low (warm) bias in CTH (CTT) for warm cloud types, (ii) a cold bias in CTT for supercooled liquid water cloud types, (iii) a lack of CTH at ~3 km that does not have a corresponding gap in CTT, (iv) a tendency of misclassifying some low- / mid-top clouds as cirrus and overlap cloud types, and (v) a saturation of CTH (CTT) around 10 km (-40ºC). Various challenges that underpin these biases are also explored. Although the exact causes of these errors remain elusive, the cloud type misclassification may exacerbate the uncertainties of passive cloud remote sensing, particularly for deep convective and corresponding deep outflow (anvil and cirrus clouds), with a lack of information related to their longwave cloud forcing that is strongly dependent on CTT. The radiative effect of these uncertainties can be significant compared to the cloud effect itself and, therefore, other retrieved cloud properties may also be uncertain.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
SHASTA FOR MODELLING, SIMULATION, ANALYTICS AND AI FOR WEATHER AND CLIMATE
Ilene Carpenter
Cray, a Hewlett Packard Enterprise Company, USA
This talk will discuss some challenges facing the weather, water and climate communities, the technology trends affecting high performance computing and storage, and how these trends and challenges intersect. I will introduce the new Cray Shasta system and explain how Shasta is designed to improve reliability and the reproducible performance needed for our operational weather forecasting customers while also enabling the emerging diverse HPC, analytics and AI workloads.
TO BE CONFIRMED
Ben Evans
National Computational Infrastructure
NO ABSTRACT
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
LIFE IN A POST EXASCALE WORLD
Wendy Sharples1, Klaus Görgen2, and Chantal Donnelly3
1Water Program, Bureau of Meteorology, Melbourne Australia 2Jülich Supercomputing Centre, Jülich Research Centre, Jülich Germany 3Water Program, Bureau of Meteorology, Brisbane, Australia
Exascale computing systems will be capable of a quintillion calculations per second – one exaflop or 1000 petaflops – making them a million times more powerful than normal desktop computers (Kettani, 2017). For comparison, the Australian national facility’s supercomputer, Raijin, has a peak performance of 2 petaflops. With the first exascale computer scheduled to come online in 2021 (https://press3.mcs.anl.gov/aurora/) they are a game changer when it comes to solving intractable problems in fields such as energy security (https://www.exawind.org/), health care (https://www.humanbrainproject.eu/), earth system modeling (https://www.exascaleproject.org/project/e3sm-mmf-cloud-resolving-climate-modeling-earths-water- cycle/) and astrophysics (https://www.exascaleproject.org/project/exasky-computing-sky-extreme- scales/).
What are the challenges and opportunities? The road to exascale computing is paved with technical challenges such as scalability, power consumption limitations and big data. To overcome these challenges, high performance computing system hardware and architecture is becoming more complex and heterogeneous. Designing applications to get the most out of the heterogeneous hardware architectures, memory hierarchies and complicated interconnectivity, requires specialist knowledge and a co-design approach (Kogge & Shalf, 2013). At the same time, this opens the door for many new innovative software libraries and frameworks to be developed.
What does exascale mean for the Bureau? As exascale becomes the new normal, supporting software ecosystems will significantly increase in complexity. In order to use these systems efficiently, substantial investment is needed to redesign code for these new architectures and ecosystems (Fu et al. 2017). To take advantage of the opportunities presented by this next frontier in computing, the Bureau will need to weigh up whether to invest early in co-design or wait until established frameworks and libraries are in place.
References
Fu H, Liao J, Ding N, et al., 2017. Redesigning CAMSE for peta–scale climate modeling performance and ultra–high resolution on Sunway TaihuLight. Proc. Int Conf for High Performance Computing, Networking, Storage, and Analysis, 1-12.
Kettani, H., 2017: Towards exascale computing. Proc. 2017 Int Conf on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 78-80.
P. Kogge and J. Shalf, 2013: Exascale Computing Trends: Adjusting to the "New Normal" for Computer Architecture. Computing in Science & Engineering, 15(6), 16-26.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
UPCOMING HPC UPGRADE AND HPC SERVICES ROADMAP
Bruce Arthur
Bureau of Meteorology, Melbourne Australia
This talk will provide an update on the Bureau’s Supercomputer upgrade and the roadmap for the Bureau’s HPC services. The Bureau is preparing for the delivery of new Cray computing and data storage systems which will enable key capabilities for the successful implementation of the new operations model and customer enhancements under the Public Services Transformation (PFST) Program
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
REMOTE SENSING AND DATA ASSIMILATION FOR THE CHARACTERIZATION OF THE TERRESTRIAL WATER CYCLE
Sujay Kumar
Hydrological Sciences Lab, NASA Goddard Space Flight Center, Greenbelt, USA
The Earth's land surface is characterized by tremendous natural heterogeneity and human engineered modifications, both of which are significantly challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. This presentation describes the successes and challenges of incorporating land remote sensing information within models through data assimilation.
In the past two decades, for example, remote sensing observations of the land surface (e.g. soil moisture, snow cover, terrestrial water storage, land surface temperature, vegetation, among others) have become available from a number of satellite instruments and platforms. At NASA, a comprehensive land data assimilation environment called Land Information System (LIS; lis.gsfc.nasa.gov) has been developed to enable the effective synthesis of these remote sensing observations with modeled estimates. LIS has been utilized for the assimilation of these remote sensing retrievals both serially and concurrently, over continental and global domains. These assimilation studies have demonstrated the beneficial impact of remote sensing measurements both for improving the representation of water, energy and carbon processes as well as downstream applications (e.g. weather forecasting, drought/flood monitoring etc.). Despite these advancements, there are significant challenges related to assimilation strategies, limitations in model formulations, and observational data processes, that limit the potential utility of the remote sensing measurements. These issues are particularly challenging over the land surface, where the impacts of natural heterogeneity and human management are complex and difficult to characterize accurately. I will describe results from recent studies that highlight the limitations of the data assimilation strategies when unmodeled processes dominate the observed signals. Many of these limitations can be attributed to the legacy of the land surface models, which have essentially limited the observability of the modeled outputs (e.g. soil moisture). Along with improved data assimilation strategies, use of advanced data fusion techniques and fundamental changes to the model representations are necessary for the realizing the full information content of remote sensing data through data assimilation.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
STEPS: RADAR RAINFALL NOWCASTS FOR DECISION SUPPORT IN AN AGE OF UNCERTAINTY
Alan Seed
Bureau of Meteorology, Melbourne Australia
The Short Term Ensemble Prediction System (STEPS) is designed to generate large ensembles of rainfall nowcasts that are suitable for use in applications that require rainfall with an appropriate structure in space and time as forcing data. This talk will begin with a discussion on the sources of errors in rainfall nowcasts. Thereafter, the stochastic models that are used in STEPS to generate the ensembles will be introduced, and the various configurations that are running at the Bureau will be explained.
LIGHTNING NOWCASTING AT WEATHERZONE - A CURRENT AND FUTURE PERSPECTIVE
Rashmi Mittal
Weatherzone, North Sydney, Australia
Lightning can have devastating effects on people, plant, equipment and energy networks. Severe storms can magnify the impacts and damage therefore their timely alerting is of great importance. Weatherzone, in partnership with Earth Networks, owns the most accurate lightning detection system in Australia. This system (WZTLD) enables real-time lightning tracking and warning, as well as various other lightning-derived functions including dangerous thunderstorm alerts (DTAs), PulseRad and lightning proximity alerting. All of these functions are based solely on total lightning detection (i.e. the detection of both IC and CG lightning). (DTAs) provide advanced notice of severe weather moving into a given area over the next 30 - 45 minutes. A DTA is issued when there is a high frequency of lightning detected by the WZTLD. High rates of total lightning activity serve as a precursor of the potential for severe weather activity. The DTA also provides an outline of the current active lightning area and a vector suggesting the direction and speed of the severe lightning activity. However, the nowcasting capability of DTAs is limited since it only works well for severe thunderstorm cases and only for time frames less than an hour.
In this talk we will briefly describe the DTA functionality and a case study. Also, various approaches that combine lightning data with radar, PulseRad, satellite or high resolution NWP data will be presented with the aim of extending the nowcasting capabilities from 45 minutes to a few hours.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
PYROCUMULONIMBUS FIREPOWER THRESHOLD
Kevin Tory1, Jeffrey Kepert1, David Wilke2 and Zachary Berry-Porter2
1Bureau of Meteorology, Melbourne, Australia 2Bureau of Meteorology, Sydney, Australia
In favourable atmospheric conditions, suitably large and hot fires can produce pyrocumulonimbus (pyroCb) cloud in the form of deep convective columns with many similarities to conventional thunderstorms. PyroCb may be accompanied by strong inflow, dangerous downbursts, lightning strikes and tornadoes. These in turn may enhance fire spread rates and fire intensity, cause sudden changes in fire spread direction, and the lightning may ignite additional fires. PyroCb conditions are not well understood and can be very difficult to forecast.
Recently, a method for determining favourable conditions for pyroCb formation was developed (Tory et al. 2018) which has been combined with a plume-rise model to determine the rate heat needs to be produced for pyroCb to develop. Specifically, this is the rate at which heat enters the fire plume often termed the "power of the fire", or "firepower". Thus, we identify a theoretical minimum firepower required for pyroCb development, termed the Pyrocumulonimbus Firepower Threshold (PFT).
Forecast spatial plots of PFT are being trialled that provide an indication of how the favourability of the atmosphere for pyroCb development varies in space and time over typical weather forecast periods. These plots have provided very useful guidance for fire weather forecasters and fire agencies during the September–October 2019 fires in northeast New South Wales and southeast Queensland. The PFT will be introduced and examples presented that draw on the recent real-time experience of fire-weather forecasters using PFT forecasts.
Pyrocumulonimbus Firepower Threshold (PFT) spatial plot during the Sir Ivan pyroCb. A narrow spatial band of reduced PFT (higher threat) passed over the fire ground with the approach of a wind change at the same time of the observed pyroCb activity. References
Tory, K. J., W. Thurston and J. D. Kepert 2018: Thermodynamics of pyrocumulus: A conceptual study. Mon. Wea. Rev., 146, 2579-2598. DOI: 10.1175/MWR-D-17-0377.1
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
PRESENT AND FUTURE OF OCEAN FORECAST IN NEW ZEALAND — NEW PERSPECTIVE UNDER THE MOANA PROJECT
Joao de Souza
Metocean Solutions
New Zealand’s maritime domain is one of the largest on the planet, with an exclusive economic zone of approximately 4,300,000 km2 — about 15 times its land area. The seafood sector alone brings $4.18B to NZ annually. Oil and gas offshore exploration provides about 30% of the country’s consumption, and tourism is a growing industry accounting for about 5.9% of the GDP. The NZ MetService is the institution responsible for providing reliable and timely forecasts of the ocean conditions to respond to such demand. To the present, such forecast consists of free-running downscaling of global simulations, based on a complex system including different ocean models and information endpoint delivery mechanisms. An architecture based on docker images and controlled by an “in house” built python- based scheduler ensures a stable and robust system.
The Moana Project — the largest research initiative in ocean sciences in New Zealand to this date — provides a unique opportunity to bring this system to the state-of-the-art in operational ocean forecast. Following international best practices, new developments including wave-circulation coupling, unstructured mesh simulations, statistical post-processing, machine learning, and data assimilation are under-way.
A general description of the operational system with its unique architecture, and the steps taken in the design and implementation of this national operational model are presented. A discussion on the history and present of operational ocean forecast is used to align the MetService research priorities with the international community while looking to the future.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
DEVELOPMENTS IN ACCESS-C AND CE: A SERVICE FOCUSSED PERSPECTIVE
Gary Dietachmayer, for the C3/CE3 Project Team
Bureau of Meteorology, Melbourne, Australia
The Bureau of Meteorology deployed its first convection-allowing-model (CAM) NWP systems into operations in August 2017. These were the "C2" systems (short for ACCESS-C, version APS2), running at 1.5km resolution across six domains centred on major population centres. Under APS3 development, ACCESS-C is evolving from a pure-downscaling system, to a full 4dVAR assimilation system, and we're also introducing our first high-resolution (2.2km) ensemble system, ACCESS-CE.
This talk will briefly describe the C3 and CE3 system configuration, performance, and development status, with a particular emphasis on how that performance maps onto service support. For example, as the Bureau moves to more automation of routine-weather forecast generation, the quality of post- processed NWP forecasts is increasingly valuable. The post-processed forecasts can be enhanced by tuning of the configuration of the NWP systems themselves. Where statistical post-processing methods are driven by model versus observation differences, ensuring that the model represents the observed world correctly at critical points (eg., model coastal grid-points are always land where AWS are sited) reduces the number of spurious model-obs differences the post-processing has to handle, and their impact on the quality of the final forecast. Such NWP configuration changes, while having a large impact on post-processed products, generally don't alter the standard NWP verification metrics.
For severe weather, at CAM length/time-scales, forecast error growth rates are measured in hours, and purely deterministic NWP guidance has very limited value – hence the introduction of ACCESS-CE in APS3. Service impact of Bureau CAM NWP can arise through direct improvement of the quality of the raw guidance itself. For example, improvements in model physics and numerics in C3 over C2 have reduced the tendency of the latter to "run hot" in tropical heavy convection, allowing greater forecaster confidence in the convective guidance. But service impact can also be enhanced by increasing the efficiency with which the NWP guidance can be incorporated into the forecast process. To this end, the model physics upgrades introduced as part of C3/CE3 include a new range of forecast-oriented guidance fields, such as model synthetic radar reflectivity, experimental direct guidance for lightning and hail, and, critically for severe weather, maximum-updraft-helicity.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
DEVELOPMENTS FOR GLOBAL OCEAN SEA-ICE AND SUB- MESOSCALE REGIONAL OCEAN FORECASTING
Gary Brassington1, Pavel Sakov1, Prasanth Divakaran1, Justin Freeman1, Mirko Velic1, Helen Beggs1, Peter Oke2, Paul Sandery2, Matt Chamberlain2, Russel Fiedler2, Andy Hogg3, Andrew Kiss3 and Petra Heil4
1Science to Services, Bureau of Meteorology, Sydney/Hobart/Melbourne, Australia 2CSIRO, Hobart, Australia 3Australian National University, Canberra, Australia 4Australian Antarctic Division, Hobart, Australia
The Bluelink global forecasting capability supports a category one operational system at the Bureau of Meteorology that delivers services for a range of national applications including: defence, search and rescue, ports and coastal management, and offshore industries. Sustaining an internationally competitive performance into the future will require extending the system to include: (1) ocean models with finer resolution and flexible grids; (2) coupled modelling frameworks; (3) four-dimensional, multi-scale and coupled data assimilation methods; (4) observing system design and rapid adoption of new platforms such as wide-swath altimetry; and (5) probabilistic forecasting.
A next generation global ocean forecast system (OceanMAPS v4) is in the final stages of development with a target for operationalization in 2020. This system will include: a fully global ocean sea-ice model and an ensemble Kalman filter (EnKF) data assimilation system. The global ocean sea-ice model is based on the Modular Ocean Model v5, CICE v5.1 and OASIS3-MCT. The ocean model features a 1/10° Mercator/tri-polar grid and 75 vertical levels and includes a 1.1 m top cell and a maximum vertical cell of 198 m. The sea-ice model includes elastic-viscous-plastic dynamics and a ridging scheme with 5 thickness categories. The model has been evaluated via repeat year forcing and JRA-55 forced integrations. The EnKF is configured as a 96-member ensemble with a 3-day update cycle and an inflation capped at 3%. Observations are asynchronously assimilated with satellite SST assimilated every 6 hrs, satellite altimetry every 24 hrs and vertical profiles assimilated every three days. This system uses 9 kSU/cycle and has a storage per cycle of up to 7 TB with a full restart of 2.8 TB. The results of the hindcasts and progress toward operationalisation will be presented as well as an outlook.
A gap in the national operational ocean forecast service resides in finely resolved regional and coastal modelling. One recent project has examined the effect of forecasting the atmospheric, oceanic and wave conditions in the maritime continent region at 1/50° resolution. The atmospheric model is based on the Unified Model v10.6 with a full Euler (non-hydrostatic) formulation, 80 model levels and an explicit treatment of convection. The ocean model is based on the Regional Ocean Modeling System with 30 vertical levels and SRTM30+ bathymetry. The wave model uses WaveWatch III with an unstructured grid and boundary conditions from AUSWAVE. The three models were assessed for a parallel forecast period of one month, March 2018. The atmospheric model demonstrated an improved representation of tropical convection, but a reduction in precipitation. The wave model demonstrated improvements associated with resolved narrow passages and islands. The ocean model was compared with 16 tide gauges showing correlations better than 0.94 for all gauges except those along the boundary in Torres Strait and one in the Gulf of Carpentaria. A follow-up project has optimised the ocean and atmosphere model and added EnOI ocean data assimilation that is delivering 10 to 15 % performance gain over the global model and a step toward fully coupled regional prediction.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
RESEARCH AND OPERATIONAL ADVANCES IN SHORT RANGE HYDROLOGICAL FORECASTING IN AUSTRALIA
Aynul Kabir1, Prasantha Hapuarachchi1, Mohammed Hasan2, Sophie Zhang1, Jayaratne Liyanage1, Patrick Sunter1, Nikeeth Ramanathan1, Fatemeh Mekanik1, Mohammed Bari3, Narendra Tuteja2, Daehyok Shin1, David Robertson4, Durga Shrestha4 and James Bennett4
Water Forecasting Services, Bureau of Meteorology, 1Melbourne, 2Canberra, 3Perth 4CSIRO Land and Water, Melbourne
The Bureau of Meteorology has been providing and improving short-range hydrological forecasting services for Australia for last six years. The service covers most of the high-value water resources catchments across Australia (around 100 catchments at more than 200 forecast locations). It plans to launch its upgraded national 7-day ensemble streamflow forecast service by the end of 2019. The upgraded operational system uses multi-model Numerical Weather Prediction (NWP) rainfall forecasts to generate an ensemble streamflow forecast aiming at a greater level of accuracy and reliability at daily and hourly time scales. The operational system generates streamflow forecasts using rainfall forecasts from the following NWP models: (i) the Australian Community Climate and Earth-System Simulator – Global Ensembles (ACCESS-GE), (ii) Poor Man's Ensemble (PME,), and (iii) European Centre for Medium-Range Weather Forecasts (ECMWF).
Forecast evaluation shows that rainfall and streamflow post-processing is an essential component of the forecast system to improve forecast quality. An extension of the Bayesian Joint Probability method is integrated into the operational system to post-process NWP rainfall forecasts to correct bias and improve reliability. Post-processed rainfall is used as input into the Short-term Water Information Forecasting Tools (SWIFT) hydrologic modelling package, which is then used to generate an ensemble streamflow forecast. The streamflow forecast is subsequently post-processed using a comprehensive multi-stage error correction model, Error Reduction and Representation in Stages (ERRIS). The model parameters are retrospectively cross-validated, and the forecasts are verified using a consistent methodology. A robust methodology is adopted to determine the optimal number of ensemble members. This is important for the operational service to adequately characterise the uncertainty involved in the streamflow forecasts while maximising the efficiency of the forecast system. Forecast skill at each site is analysed using an advanced bootstrapping technique, before the site is integrated into the operational service.
Operational forecasts are generated daily using an automated workflow and are delivered through the Hydrological Forecasting System (HyFS), an enterprise approach to operational flood and short-range water forecasting. The forecasts are available to the public via http://www.bom.gov.au/water/7daystreamflow while a premium service is provided to registered users via http://www.bom.gov.au/water/reg/7daystreamflow.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
DEVELOPMENTS IN AUSWAVE
Stefan Zieger and Diana Greenslade
Science to Services, Bureau of Meteorology, Melbourne, Australia
The Bureau of Meteorology's operational wave forecast system (AUSWAVE) has a comparatively low spatial resolution of ~25 km globally and ~10 km around Australia. Applications that require accurate wave information cover a range of scales, from offshore wave forecasts for ship routing to near-shore wave forecasts for predicting hazards to recreational users and coastal infrastructure. Besides grid resolution, the number of forecast parameters limits the applicability of the wave forecast system. While forecasting bulk wave parameters, such as significant wave height or wave peak period is common practice, it remains a challenge to forecast directional sea surface variance spectra for engineering and scientific applications, such as dynamical downscaling and phase-resolving modelling. The challenge with directional wave spectra is inflated file size and limited bandwidth to transfer forecasts to customers. To meet the demand of a wide range of customers, a pilot wave model forecast system has been developed.
The pilot system is an implementation of WAVEWATCH III® with support for rectilinear, curvilinear and unstructured grids. The wave model grid features a mean global resolution of approximately 12 km, increasing to 250m at selected coastlines. The system is forced with surface winds from the Bureau of Meteorology's operational Numerical Weather Prediction System. The presentation will provide an overview of the system, including verification against available observations and a comparison with existing operational systems. In addition, examples for downscaling applications in the Australian and Austral-Pacific region are given.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
THE NATIONAL BLEND OF MODELS: A STATISTICALLY POST- PROCESSED MULTI-MODEL ENSEMBLE
Jeffrey Craven
NOAA/National Weather Service/Meteorological Development Laboratory/Statistical Modeling Division, Silver Spring, MD, USA
The National Blend of Models (NBM — Craven et al 2019) is the culmination of an effort to develop a nationally consistent set of foundational gridded guidance products based on well-calibrated National Weather Service (NWS) and non-NWS model information. These guidance products are made available to the National Centers for Environmental Prediction and NWS Weather Forecast Offices for use in their forecast process. As the NWS continues to shift emphasis from production of forecast products to impact-based decision support services for core partners, the deterministic and probabilistic output from the NBM will become increasingly important as a starting point to the forecast process.
The NWS has been issuing gridded forecast products using the National Digital Forecast Database for nearly two decades. During this time, there has been a gradual evolution from initialising gridded forecasts using individual models such as the North American Mesoscale Model (NAM), the Global Forecast System (GFS), and GFS gridded model output statistics (MOS-GMOS) to using blends of various guidance. This evolution was inspired by the success of using a consensus of models to produce more accurate forecasts. The National Hurricane Center has a long history of using a consensus of numerical weather prediction (NWP) solutions to forecast the track of tropical cyclones and this has led to other similar efforts within the NWS.
NBM (v3.2) contains 31 different model systems that are blended to produce a single deterministic forecast product. For a growing number of elements, probabilistic forecasts are also created. Inputs from five different global modelling centres are present, including USA National Centers for Environmental Prediction (NCEP), Canadian Meteorological Center (CMC), Navy Fleet Numerical Operations Center (FNMOC), European Centre for Medium Range Weather Forecasts (ECMWF), and Bureau of Meteorology (BoM) Australia. The Real Time Mesoscale Analysis (RTMA) and Unrestricted Real Time Mesoscale Analysis (URMA) are used as the ground truth observation. Exponentially weighted decaying average is used to bias correct and downscale the inputs to roughly 2.5km resolution.
This talk discusses the progress of the NBM and what techniques are used to blend the individual models and ensembles for a number of forecast elements and regions.
References
Craven, J., D. Rudack, and Shafer, P. 2019 (submitted): The National Blend of Models: a statistically post-processed multi-model ensemble. National Weather Association Journal of Operational Meteorology.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
GUIDANCE POST PROCESSING PRESENT AND FUTURE
Anja Schubert, Gary Weymouth, Thomas Gale, Tim Hume, James Canvin and Andrew Charles
Bureau of Meteorology, Vic, Australia
Post-processing of weather models, both local and international, into a single best-consensus forecast maximises the value and impact of the available data. Calibrated, combined and down-scaled guidance from Numerical Weather Prediction (NWP) is found to out-perform raw NWP for medium-range weather forecasting on average. The Guidance Post Processing (GPP) team is continuously improving forecast fields produced by its Gridded Operational Consensus Forecast (GOCF) post-processing platform, in response to both user needs and in support of increased automation of weather forecast production. The fields include wind direction, wind speed, temperature and dew point.
The new post-processing system for 3-hourly ensemble rainfall probability is presented. The new guidance combines a “poor man's” ensemble of local and international deterministic guidance with the ECMWF global weather forecast ensemble. Inputs are combined in probability space in a process analogous to quantile mapping. Calibration of the guidance is localised and tuned to provide rainfall probabilities at the scale of rain gauges by using daily (Australian Water Availability Project) analyses in combination with daily and 3-hourly rain-gauge observations as the 'truth' data. New daily and 3- hourly guidance is intended to be used as the basis for automated forecasts in 'routine' weather conditions. Increased automation requires compatible daily and 3-hourly guidance of sufficient quality. Daily rainfall probability skill has been improved by the equivalent of at least 36 hours lead time and shown quality at least as good as official forecasts for many areas in the few months after its introduction. The new 3-hourly guidance also shows large skill gains. Factors affecting performance and uptake of these improved guidance products will be discussed.
Improvements forming upcoming GOCF releases for wind, temperature and dewpoint gridded fields will also briefly be discussed.
References
GPP, NOC, 2019: Upgrades to the Operational PME (3hourly PoP) System. http://web.bom.gov.au/nmoc/stan/opsbull/opsbull-123.pdf
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
IMPROVER: A NEW PROBABILISTIC GUIDANCE POST- PROCESSING SYSTEM
Gary Weymouth and Thomas Gale
Science to Services, Bureau of Meteorology, Melbourne, Australia
The Bureau needs to automate and streamline more of its weather forecast production to enable a redirection of forecaster effort to higher value tasks and produce more valuable and resilient services under a Public Services Transformation (PST) program. A key support of this is guidance post- processing (GPP), which produces best practice gridded basic weather parameter guidance (e.g. wind, temperature, rain).
The Bureau has commenced collaboration with the UK Met Office on a new GPP system called IMPROVER (Integrated Model PROcessing and VERification). IMPROVER has been developed at the UK Met Office over approximately the last three years. It extends existing GPP support for automation and additionally provides calibrated probabilistic and scenario prediction in a modern extensible framework. This will help to fully utilise our investment in supercomputing and modelling, to enhance internal and external alerting of weather hazards and decision support. IMPROVER will provide seamless guidance from radar-based nowcasting and rapid update cycle convection allowing model ensembles such as ACCESS-CE, through medium-range ensemble prediction such as ACCESS-GE and potentially beyond.
IMPROVER will provide guidance to support the Bureau to move from 'one size fits all' single-valued forecasts that are impossible to optimise for all users, to guidance fit for and tailorable to multiple purposes to improve public safety and economic health. IMPROVER can also provide multi-hazard and simple impact guidance – for example probabilistic fire danger indices.
We will give an overview of what IMPROVER can provide, how it works, progress and plans.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
AUTOMATING THE WARNING PROCESS AT DEUTSCHER WETTERDIENST (DWD) – LESSONS LEARNED
Hans-Joachim Koppert, and Franz-Josef Molé
Weather Forecasting, Deutscher Wetterdienst, Offenbach, Germany
After a missed tornado warning in 2006, DWD launched the project AutoWARN to optimise the monitoring of severe weather and to automate the warning process. The original concept was to monitor automatically-created warning proposals by the forecaster and, if necessary, to revise them. The first release of AutoWARN was made operational in 2014. Substantial improvements to the warning process were achieved in the project, but full automation could not be realised.
The warning process consists of: NowCastMIX (James et al., 2018), which provides data for the nowcasting time range (making use of in-situ, remote sensing, and model-data); ModelMIX, which combines different deterministic and ensemble-based models; and editing components that create homogenised warning proposals (ASG) and allow manual modifications (ASE) to the warning status. Today forecasters heavily rely on NowCastMIX when nowcasting thunderstorm warnings. Although ModelMIX gust and continuous rain-forecasts reach the performance of the manual interpretation by forecasters, the manual interpretation still relies on raw NWP model output and makes limited use of the homogenised warning proposals provided by ASG.
The AutoWARN project has made serious attempts to incorporate the automatic warnings directly into the editing process. Instead of amending the warning status completely manually, it allows automatic proposals into the new warnings status by a simple mouse click. However, experience has shown that this objective seemed not realistic. The warning status is a fragmented collection of polygons that cover Germany in a meteorologically consistent way. Merging new proposals into the forecaster’s own conception appears to be a complex problem. This leads to cumbersome and repeated manual editing. Consequently, forecasters find it more convenient to keep full control over the editing process and use the proposals for visual inspection only.
A lesson learned from AutoWARN is the perspective of partial automation. It can only be successful if the editing workflow is improved. The design of an appropriate method remains a tremendous challenge. For selected targets, it may be more realistic to develop a highly automated system with very limited intervention by forecasters. For some weather events, e.g. thunderstorms and very short lead times, the performance already appears promising.
References
James, P., Reichert B., and Heizenreder, D., 2018: NowCastMIX: Automatic Integrated Warnings for Severe Convection on Nowcasting Time Scales at the German Weather Service, Weather and Forecasting, 33, 1413-1433.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
A STREAMLINED FORECAST PROCESS UTILIZING FORECASTBUILDER AND THE ASSOCIATED CULTURE CHANGE
Andrew Just
National Weather Service Central Region Headquarters, Kansas City, Missouri, USA
Numerous tools have been developed since the inception of the Graphical Forecast Editor (GFE) in the U.S. NWS in the early 2000s. These tools were developed to more efficiently create the forecast without sacrificing quality. However, they are not without issues including: 1) questions about their scientific and technical validity, 2) knowing the correct order of execution, and 3) ensuring all offices both acquired and used the tools consistently. To resolve these problems, the “ForecastBuilder” program was developed. The goal of this program has been to create a streamlined forecast process across the NWS consisting of an embedded common set of scientific tools, which utilises the National Blend of Models (NBM) as a common forecast starting point. An extension of the program, “HazardBuilder,” aids forecasters in determining “targets of opportunity” where further investigation into the forecast may be required, as well as providing input to a Graphical Hazardous Weather Outlook for core partners (including emergency managers).
While there are significant benefits in adopting ForecastBuilder, there are also cultural challenges to overcome, including: 1) forecasters who have essentially used the same process for decades, 2) concern about increased automation, 3) forecasters’ desire to maintain consistency from forecast to forecast, 4) trust in the process including the common forecast starting point, and 5) training. The Central Region of the NWS (composed of 38 offices) found ForecastBuilder improved their operations and adopted the program over two years ago. Additionally, ForecastBuilder continues to spread across the entire NWS as offices adapt to the new culture.
This presentation will discuss how ForecastBuilder was developed, the culture change associated with the program, and how it has given forecasters more time for Impact-based Decision Support Services (IDSS).
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
TO WHAT EXTENT CAN WE AUTOMATE ROUTINE FORECAST PRODUCTION?
Michael Foley1, Deryn Griffiths1, Tom Pagano2 and Nick Loveday3
1Science and Innovation Group, Bureau of Meteorology, Melbourne, Australia 2National Forecast Services Group, Bureau of Meteorology, Melbourne, Australia 3Science and Innovation Group, Bureau of Meteorology, Darwin, Australia
What is the best use the Bureau of Meteorology can make of its highly-skilled cohort of operational meteorologists, to optimise the services it provides to the Australian community? There is a demand for meteorologists to spend more time with customers to assist their decision making in the face of weather impacts. The key question is whether they can be diverted to such activities by using more automated forecast production processes, without an unacceptable loss in overall service quality.
Over the past four years we have conducted verification comparing the forecasts issued to the public with automated alternatives based on statistically post-processed NWP consensus guidance. The forecast parameters examined have progressively expanded to include rainfall, temperature, wind, humidity and fire danger. The findings are that the quality of automated forecasts is generally good. Where deficiencies have been exposed in the automated forecasts, this has helped to guide development to improve the automation.
There has been an ongoing dialog with the Bureau's operational forecasting area to unpack the implications of our verification findings. This has led to updated standard operating procedures, which promote widespread use of the automated forecast from three to seven days lead time. The Bureau's National Operations Centre is monitoring the extent to which forecasts of particular parameters are the same as the automated forecasts and has seen significant adoption of the automated forecasts for the longer lead-time forecasts. While the forecast process has changed, the quality of forecasts has been maintained.
There are a number of aspects of the forecast process where interventions by the forecaster continue. For some aspects (e.g. distinguishing rain/showers/drizzle, fog, dust) the automated forecast does not yet provide the necessary information for the service. In some weather situations, particularly for gridded wind forecasts, the forecaster will prefer to depict a particular forecast scenario rather than a mean outcome provided by the automated forecast. Such interventions raise questions about how our services are defined and how automatable they need to be. Similarly, interventions needed in other aspects such as the representation of thunderstorms in our forecasts, may only be resolved through new service definitions and improvements to the way forecast information is fed into those services.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
VALUE-ADDING BY OPERATIONAL METEOROLOGISTS
Jenny Sturrock
Bureau of Meteorology, Melbourne, Australia
From August 2019 forecasters changed the way they interact with model data to produce local weather forecasts. Backed by verification and a drive to shift meteorologists' focus on how to best value-add, the new approach uses first-cut forecasting and specific guidelines on when forecaster intervention is applied. We look at a case study to demonstrate this new approach and also discuss some of the challenges and opportunities taking on this new way of operating has provided.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
IMPROVING CLIENT DECISION MAKING
James Crow and Matthew Boterhoven
State Forecasting Centre, Bureau of Meteorology, Perth
The Western Australian State Forecasting Centre provides meteorological and oceanographic services for a variety of organisations with a particular focus on commercial customers in the energy and resource industry. It is relatively easy to provide a customer with a forecast, but for them to make optimal decisions, scientifically proven psychological/cognitive biases must be mitigated. We will provide some examples where we have observed suboptimal decisions due to cognitive biases, including loss aversion (Fig. 1).
As the the Bureau of Meteorology becomes a more outwardly focused organisation, decision support meteorologists and researchers will need to be cognisant of psychological biases in product and service delivery in order to become more effective. Increasingly, it is our experience that closer engagement with customers leads to improved decisions with more optimal outcomes, but it does come with challenges.
Fig. 1. Kahneman & Tversky (1979) discovered that people are more sensitive to losses than gains, and consequently they will bias their decisions to avoid loss. Note the psychological value of losing $100 is roughly equivalent to gaining $300.
References
Kahneman, D., and Tversky, A. 1979: Prospect theory: An analysis of decision under risk, Econometrica, 47, 263-291.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
RISK COMMUNICATION
Matalena Tofa1,2, Melanie Taylor1,2, Katharine Haynes2,3, and Gemma Hope1,2
1Department of Psychology, Macquarie University, Sydney, Australia 2Bushfire and Natural Hazards CRC, Melbourne, Australia 3University of Wollongong, Wollongong, Australia
In Australia, flooding accounts for the second highest number of fatalities due to natural hazards (after heatwaves), and is a significant cause of death internationally (Coates et al. 2014; Haynes et al. 2017; Kellar and Schmidlin 2012). Analysis of Australian flood fatality data has shown that males, and children/young adults (<29) are overrepresented in these statistics (79%, and 43%, respectively), and that the two activities linked to the highest proportions of flood deaths are driving through floodwater and recreating in floodwater (Ahmed et al. 2018; Haynes et al. 2017). The current Bushfire and Natural Hazards Co-operative Research Centre funded Flood Risk Communication project examines public and emergency service staff and volunteer behaviour around floodwater and perceptions of floodwater. In this presentation, we will share insights into risk communication that can be drawn from this research. Firstly, we will discuss the complexity of risk communication and, in particular, the diverse ways in which people receive, interpret, and act upon risk messages. Secondly, we will explore the issue of defining hazards — in this case, floodwater — and the importance of developing a shared understanding of the hazard for risk communication. Thirdly, despite official advice, evidence shows that people are entering floodwater, both in vehicles and on foot. We will discuss the implications of this for developing appropriate risk messaging. Finally, we will explore how the cultural relationship with water in Australia, and the idea that ‘water is fun,’ influences risk communication in relation to floods. Taken together, these insights demonstrate the complexity of risk communication in relation to floods, and extreme weather events more broadly. References
Ahmed, M. A., K. Haynes, and M. Taylor, 2018: Driving into floodwater: A systematic review of risks, behaviour and mitigation. Int. Journal of Disaster Risk Reduction, 31, 953-963.
Coates, L., K. Haynes, J. O'Brien, J. McAneney, and F. D. De Oliveira, 2014: Exploring 167 years of vulnerability: An examination of extreme heat events in Australia 1844-2010. Environmental Science and Policy, 42, 33-44.
Haynes, K., and Coauthors, 2017: Exploring the circumstances surrounding flood fatalities in Australia—1900–2015 and the implications for policy and practice. Environmental Science & Policy, 76, 165-176.
Kellar, D. M. M., and T. W. Schmidlin, 2012: Vehicle-related flood deaths in the United States, 1995– 2005. Journal of Flood Risk Management, 5, 153-163.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
A NEW CONVECTION SCHEME FOR THE UM: IDEALISED SENSITIVITY EXPERIMENTS
Sally Lavender1, Michael Whitall2, Alison Stirling2 and Rachel Stratton2
1University of Southern Queensland, Toowoomba, Australia 2Met Office, Exeter, UK
The North Australian Climate Program (NACP) aims to make significant improvements to seasonal, sub-seasonal and multi-year climate forecasting systems, with a focus on developing products that are useful to agricultural industries in Northern Australia. As part of this project, we are exploring how different representations of convection within the UK Met Office Unified Model (UM) impact on some of the key atmospheric drivers that affect rainfall variability over Northern Australia.
This presentation focusses on initial work testing and developing a new convection scheme, CoMorph, for implementation in the next version of the UM. After a brief overview of the performance of CoMorph when simulating the large-scale climate, an idealised setup for performing some more detailed tests will be introduced. The sensitivity of convection to moisture is evaluated based on experiments in which the large-scale environment relaxes back to a range of different moisture profiles. Results from some high resolution, convection-resolving experiments will be compared with lower resolution experiments using both the current convection scheme and CoMorph.
For more detailed testing of CoMorph, in order to aid further development of the scheme, several physical parameters are adjusted to examine the influence on convective variables. Testing has been performed in parallel using both the global model and using the idealised setup described. Results of tests including modifying the entrainment rate, initial parcel perturbations and the inclusion of graupel will be explored in this presentation.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
IMPACTS AND PREDICTABILITY OF AUSTRALIAN WINTERTIME MINIMUM TEMPERATURES DRIVEN BY THE MJO
Guomin Wang and Harry Hendon
Bureau of Meteorology, Melbourne, Australia
The Madden-Julian Oscillation (MJO) is the major mode of tropical intraseasonal variation, and is a potential source of multiweek predictability. MJO's impacts on Australian weather and climate have been widely recognized. Here we investigate the MJO’s impact on temperature extremes during Austral wintertime using observational data analysis. We find a significant MJO influence on weekly mean minimum temperature across much of northern and eastern Australia, with lower than normal minimum temperatures tending to occur during MJO phases 6 and 7. The likelihood of extreme weekly mean minimum temperatures also increases by at least a factor of 2 during these phases. In contrast, little impact on maximum temperatures is observed. The proximate cause of the lower than normal minimum temperatures in these phases is the anomalous equatorward advection of cool and dry continental air and enhanced night time radiative cooling due to the drier conditions.
Australia sits at the boundary of the tropics and extratropics; consequently the circulation anomalies over Australia during MJO phases 5-7 are shown to be a combination of the direct baroclinic response to the anomalous tropical convection driven by the MJO and the Rossby wave train that propagates from the tropics to the extratropics that is primarily confined to the Australian sector. The confinement of the extratropical Rossby wave train to the Australian sector results from the longitudinal localization of the Rossby wave source driven by MJO convection and wave propagation stemming from the refractive characteristics of the mean state zonal wind, which does not permit tropical-extratropical wave paths to the east of Australia. An extratropical wave source due to feedback from transient eddies was also shown to be an effective source of the extratropical response to the south of Australia and for the wave train that impinges on South America.
The capability of the Bureau of Meteorology sub-seasonal to seasonal forecast system (ACCESS-S1) to predict weekly mean extreme minimum temperatures across Australia was also assessed. Although the skill is good out to at least 3 weeks lead-time across much of tropical eastern Australia, there appears not to be any additional skill for times when the MJO is strong during the forecast. Potential systematic errors that would prevent realization of any additional forecast skill from the MJO are discussed.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
IMPACT OF SOUTHERN HEMISPHERE STRATOSPHERIC POLAR VORTEX WEAKENING ON AUSTRALIAN CLIMATE EXTREMES
Eun-Pa Lim1, Harry Hendon1, Ghyslaine Boschat2,3, Debra Hudson1, David Thompson4, Andrew Dowdy1 and Julie Arblaster2,3
1Bureau of Meteorology, Melbourne, Australia 2ARC Centre of Excellence for Climate Extremes, Monash University, Clayton, Australia 3School of Earth, Atmosphere and Environment, Monash University, Clayton, Australia 4Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA
The role of Southern Hemisphere (SH) stratospheric polar vortex variability for promoting Australian temperatures and rainfall extremes during austral spring-summer is explored. Anomalous weakening of the polar vortex during spring is shown to be associated with significantly increased chances of higher than normal maximum temperature (Tmax) and lower than normal rainfall during October-January across a large area of eastern Australia (Lim et al. 2019). The probability of occurrence of extreme events is also enhanced, with the chances of being in the top 20% for Tmax and bottom 20% for rainfall of October-January mean increasing by more than 4 times during vortex weakening years. Consequently, the probability of occurrence of the top 20% bushfire-prone weather conditions also substantially increases. This promotion of extreme climate conditions is shown to result from the associated swing to the low phase of the Southern Annular Mode that is induced by the downward coupling from the stratospheric vortex anomalies to the surface. This vertical coupling that occurs on monthly to seasonal time scales implies enhanced predictability of temperature and rainfall extremes during vortex weakening years. This enhanced predictability is demonstrated with the Bureau of Meteorology ACCESS-S seasonal forecast system, which has a well resolved stratosphere and can capture the stratosphere to troposphere downward coupling and its impact on Australian climate extremes for the season of October-January.
References
Lim, E.-P., Hendon, H. H., Boschat, G., Hudson, D., Thompson, D. W. J., Dowdy, A., and Arblaster, J. M. 2019: Australian hot and dry extremes induced by weakenings of the stratospheric polar vortex, Nature Geoscience. https://doi.org/10.1038/s41561-019-0456-x
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
CHALLENGES IN ASSESSING SKILL OF SEASONAL FORECASTS IN AUSTRALIA
Andrew King1, Debra Hudson2, Eun-Pa Lim2, Andrew Marshall3, Harry Hendon2, Todd Lane1, Benjamin Henley1, Tim Cowan2,4, and Oscar Alves2
1School of Earth Sciences and ARC Centre of Excellence for Climate Extremes, University of Melbourne, Melbourne, Australia 2Bureau of Meteorology, Melbourne, Australia 3Department, Institution, City, Country Bureau of Meteorology, Melbourne, Australia 4University of Southern Queensland, Toowoomba, Australia
In this presentation we first discuss analysis of sub-seasonal to seasonal prediction skill in Australian rainfall extreme indices in ACCESS-S1 and then we focus on the issue of hindcast timing and length sensitivity in determining skill verification results.
Skill verification of seasonal prediction models is based on hindcast simulations usually performed for a recent 20 to 40 year period. For example, in the case of ACCESS-S1, a 23-year hindcast ensemble was run from 1990-2012 and for ACCESS-S2 a longer 37-year hindcast set is being prepared for 1981- 2017. Given that Australian climate is strongly variable on inter-annual and decadal timescales, and that much of the predictive skill in seasonal outlooks in Australia is tied to the El Niño-Southern Oscillation (ENSO), this begs the question: how important is the timing and length of the hindcast period in influencing skill verification results?
In our analysis we have used observational datasets and ACCESS-S1 to show that skill verification is strongly related to the choice of hindcast period. We firstly examine the average difference in observed seasonal Australia-average precipitation between La Niña and El Niño periods and we find that there is strong decadal-scale variability in this metric associated with the Interdecadal Pacific Oscillation (IPO). In particular, ENSO-related variability in spring precipitation is particularly large for 1990-2012 in connection with predominantly negative IPO conditions. Within the ACCESS-S1 hindcast period we find that there is a relationship between the ENSO-related spring precipitation variability and the skill of the model such that negative IPO periods with enhanced ENSO-related rainfall variations have greater skill than positive IPO periods.
Our results suggest that the choice of hindcast is important in verifying seasonal rainfall outlooks in Australia and that certain hindcast windows could lead to over- or under-confidence in the model relative to the true skill. We finish the presentation by discussing the implications of our results for future work in skill verification of seasonal outlooks.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
PREDICTION OF THE EXTREME CONDITIONS ASSOCIATED WITH THE FLOODS IN NORTHERN QUEENSLAND IN ACCESS-S1
Tim Cowan1,2, Matthew Wheeler2, Oscar Alves2, Sugata Narsey2, Catherine de Burgh- Day2, Morwenna Griffiths2, Chelsea Jarvis1, David Cobon1, and Matthew Hawcroft1,3
1Centre for Applied Climate Sciences, University of Southern Qld, Toowoomba, Australia 2Bureau of Meteorology, Melbourne, Australia 3Met Office, Exeter, United Kingdom
During late January and early February 2019, a slow-moving monsoon depression over northern Queensland caused extreme weather conditions and flooding that led to the deaths of an estimated 625,000 head of cattle and 48,000 sheep across the inland Gulf catchments, as well as inundating more than 3000 homes in Townsville. The monsoon depression lasted close to 10 days, driving daily rainfall totals above 200 mm, maximum temperatures 8–12°C below average, and sustained wind speeds of between 30-40 km/h. Results show that during this extended extreme event, an active Madden-Julian Oscillation pulse stalled over the western Pacific, which likely contributed to the record-breaking weekly rainfall totals. The dominant interannual climate driver, the El Niño-Southern Oscillation, was not in its usual phase that typically leads to increased rainfall over Queensland. Over the northern Tasman Sea, a blocking anticyclone helped maintain a positive phase of the Southern Annular Mode and promote onshore easterly flow, helping to sustain the relatively cold conditions that added to the livestock losses. Yet, the monthly rainfall outlook for February issued by the Bureau of Meteorology on 31 January provided little indication of the upcoming event. Here we show that forecasts of weekly- averaged conditions by the Bureau's dynamical subseasonal-to-seasonal (S2S) prediction system – ACCESS-S1 – were more successful. For the week of 31 January to 6 February, the ACCESS-S1 forecast a more than doubling of the probability of extreme weekly rainfall a week prior to the floods, along with increased probabilities of extremely low maximum temperatures and high winds. Ensemble- mean weekly rainfall amounts, however, were considerably underestimated by ACCESS-S1, even in forecasts initialised at the start of the peak flooding week, consistent with other state-of-the-art dynamical S2S prediction systems. Predicting this type of exceptional event beyond two weeks with great accuracy appears beyond our current capability despite the dynamical systems' lead week 1 forecasts showing good skill in forecasting the broad-scale atmospheric conditions north of Australia.
References
Cowan, T., Wheeler, M.C., Alves, O., Narsey, S., de Burgh-Day, C., Griffiths, M., Jarvis, C., Cobon, D.H. and Hawcroft, M.K. 2019: Forecasting the extreme rainfall, low temperatures, and strong winds associated with the northern Queensland floods of February 2019, Weather and Climate Extremes, https://doi.org/10.1016/j.wace.2019.100232 (in press).
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
SEASONAL FORECASTING FOR MARINE MANAGEMENT AROUND AUSTRALIA IN A CHANGING CLIMATE
Claire Spillman1, Grant Smith1, Alistair Hobday2, Jason Hartog2, Catherine de Burgh- Day1 and Paige Eveson2
1Bureau of Meteorology, Melbourne, VIC 3001, Australia. 2CSIRO Oceans and Atmosphere, Hobart, TAS 7000, Australia.
Warming oceans have huge implications for marine resources and industries, with impacts projected to increase under climate change in the future. Recent marine heatwaves have led to mass coral bleaching and mortality, reduced aquaculture yields and altered wild fish migration patterns. The Australian Bureau of Meteorology’s seasonal forecast model ACCESS-S1 currently produces operational, real- time, global forecasts of sea surface temperatures out to six months into the future, with tailored outlooks produced for coral reef, aquaculture and wild fisheries management around Australia. Thermal stress forecast products have been developed, incorporating both the magnitude and duration of heat stress events, with widespread management applications. Advance warning of extreme marine heat events can enable managers and industries to plan ahead and effectively manage resources to reduce impacts of such events. Climate change is leading to a future where past experience is of reduced value. Seasonal forecast tools can enable proactive management, which can increase the resilience of both marine resources and industries in a changing climate
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
CLIMATE OUTLOOKS—THE BUREAU'S WEEKLY, MONTHLY AND SEASONAL FORECASTING SERVICES
Jonathan Pollock, Robyn Duell, William Wang, Avijeet Ramchurn, Elise Chandler, Kevin Keay, Felicity Gamble, Catherine Ganter and Andrew Watkins
Climate Services, Bureau of Meteorology, Melbourne, Australia
The Bureau has been issuing seasonal forecasts since 1989, recently passing 30 years since its first simple map. The outlooks provide probabilistic guidance on the likely rainfall and temperature patterns in the months ahead. Called "Climate Outlooks", the science and service has evolved over the past 30 years. Whereas in the first 20 years of the Climate Outlooks service life it was delivered via static maps and text, it is now delivered through multiple channels, including the Bureau's website, mainstream media, videos, social media, email subscriptions, webinars, press conferences, audio and video news releases and face-to-face briefings. The website now receives over 14 million views per year, while the video broadcast on ABC-TV's Landline program reaches an estimated audience of 500,000 each month.
Today’s Climate Outlooks are based on feedback from our users obtained through two market research studies—the first in 2011–12 and the second in 2015–16. These reports found that there is an array of users, making multiple, different types of decisions using the Climate Outlooks. This highlighted that the delivery of the service in ways people could meaningfully use was almost as important as the fundamental science underpinning the service itself. Users also wanted more outlooks delivered more often. In 2015, one-month forecasts were added alongside the three-month forecasts. Now, in 2019, the service has added one-week and fortnight-length forecasts, closing the gap between the seven-day forecasts and the longer-range seasonal outlooks. This represents a seamless forecasting service from days, to weeks, to months, to seasons.
The shorter outlook periods also demanded more frequent updates. Previously, the seasonal forecasts had been updated twice monthly. Now, the monthly and seasonal forecasts are updated each week, and the weekly and fortnight forecasts updated twice per week. The shorter outlook periods also called for a new type of forecast—temperature anomalies for the weeks and fortnights ahead. We were also able to deliver an extra three-month outlook, looking one month further ahead than previously available. Climate outlooks are verified internally using multiple assessment scores, while the model hindcasts for all weekly through to seasonal products are shown publicly using a new, weighted percent, consistent method (Wang et al., 2019).
This talk will discuss: How changes in forecast production have impacted service delivery; user's reception to the new weekly and fortnightly forecasts; and early verification results for the publicly issued multi-week forecasts.
References
Wang, W., Watkins, A., and D. Jones, 2019: Meteorol. Z. (Contrib. Atm. Sci.), Vol. 28(3), 193–202.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
APPLYING REAL-TIME ACCESS-S FORECASTS FOR WATER AVAILABILITY FORECASTING
Andrew Schepen1, David Robertson2 and Cuan Petheram3
1Land and Water, CSIRO, Brisbane, Australia 2Land and Water, CSIRO, Melbourne, Australia 3Land and Water, CSIRO, Hobart, Australia
Andrew.Schepen @csiro.au
Seasonal rainfall forecast information from dynamical climate models can be harnessed for hydrological modelling and water availability forecasting via statistical post-processing that produces ensembles with reduced bias, less error and improved reliability in ensemble spread. Post-processing forecasts from climate models with different hindcast and real-time configurations can be complicated by the difference in number of ensemble members and forecast lead time. Moreover, using ensemble members generated over multiple lead times may render a mix of exchangeable and non-exchange ensemble members, which ought to be considered when applying statistical post-processing methods.
In this study, we apply a new Bayesian forecast calibration method called Ensemble Link Functions (ELFs). It is an ensemble model output statistics type method that is designed to be a generically applicable method to post-process a) forecasts with both exchangeable and non-exchangeable ensemble members, b) hindcasts and forecasts being different in ensemble configuration, and c) a varying degree of correspondence between ensemble spread and real forecast uncertainty. We apply the new ensemble forecast calibration method to post-process forecasts from the Australian Bureau of Meteorology’s ACCESS-S model. The complex distribution of rainfall is handled by embedding data transformation and left-censoring of rainfall values into the methodology. The ensemble forecast calibration method is shown to be effective for producing reliable, bias-corrected forecasts of climate variables.
Applications of the ELFs forecasting calibration method to date include the post-processing of Nino3.4 forecasts and the post-processing of rainfall forecasts. We demonstrate the application of post-processed ACCESS-S rainfall forecasts to generate water availability forecasts for Norfolk Island by connecting ACCESS-S forecasts to a rainwater tank model. Such a service could be valuable to Norfolk Island residents who have been threatened by water scarcity in recent years.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
WEATHER YOUR WAY
Christine Killip1,2 and Esteban Abellan1
1Weather Intelligence Pty Ltd, Brisbane, Australia 2Katestone Environmental Pty Ltd, Brisbane, Australia
In a world where time is money, commercial users of weather information have little time to shift through pages of information, consult multiple web sites and weather apps or compile complex information to allow them to make an informed decision. Businesses also know that not understanding the impacts of severe weather on their operations can cost them even more money and potentially put lives at risk. In Australia the cost of weather is increasing by an estimate of 3.4% per year and is estimated at $18 billion per year, $11 billion of this is attributed to Queensland. This is not unexpected considering the extreme nature of tropical weather and relatively large population along the coast and in the South East corner and industrial development.
This presentation provides some specific examples of how consumers want their weather data served to them. In an era of personalisation, combining a site-specific forecast (or multiple forecasts) with a consumer's own knowledge and data provides powerful weather intelligence to allow users to reduce the risk of weather on their operations. Toolbox reports sent every day in a consumer-driven format lead to fast reliable decisions. Automated sms alerts, for example, can trigger the need for a water truck or additional personell ready to make changes to operations if needed. This allows for a proactive management of weather risks and turns a simple weather forecast into weather intelligence.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
TOWARDS A SEASONAL LANDSCAPE FORECASTING SERVICE FOR AUSTRALIA
Julien Lerat1, Elisabeth Vogel2, Chantal Donnelly3, Sean Loh2, Andrew Frost4 and Wendy Sharples2
1 Water Investigations, Bureau of Meteorology, Canberra, Australia 2 Water Investigations, Bureau of Meteorology, Melbourne, Australia 3 Water Investigations, Bureau of Meteorology, Brisbane, Australia 4 Water Investigations, Bureau of Meteorology, Sydney, Australia
The Bureau of Meteorology provides climate outlooks of precipitation and temperature for the Australian continent several months in advance. Bureau stakeholders from various sectors, including water resources management, food production and flood and bushfire risk assessments, have expressed a growing need for similar forecasts of hydrological variables. Here, we present the development and testing of a seasonal forecasting system for soil moisture, evapotranspiration and runoff for Australia using the Australian Water Resource Assessment Landscape model (AWRA-L), and the Australian Community Climate and Earth-System Simulator – Seasonal (ACCESS-S).
The AWRA-L model simulates hydrological fluxes and stores, including runoff, evapotranspiration and soil moisture for three soil layers (0-0.1m, 0.1-1m, 1-6m) and two landuse classes on a 5 km grid. Hydrological forecasts are generated by forcing AWRA-L with interpolated and bias-corrected ACCESS-S climate forecasts for the period 1990-2012. To assess the skill of the hindcast, the daily output was aggregated to the monthly scale. In addition, a data assimilation approach was implemented where the model top soil layer was updated at the beginning of each forecast period using a combination of remotely sensed data from SMAP and ASCAT sensors.
Forecast performance was assessed against a reference run where AWRA-L was forced with observed climate data from the AWAP archive, with and without data assimilation. A range of verification metrics were computed to capture the accuracy and statistical reliability of the forecasts (including mean bias, anomaly correlation, CRPS) for mean conditions and percentile-based thresholds. Subsequently, we discuss sources of skill by presenting comparisons with a climatology ensemble forecast.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
TAILORING SEASONAL FORECASTS TO MATCH THE REQUIREMENTS OF AUSTRALIAN FARMING SYSTEMS
Patrick Mitchell and Jaclyn Brown
CSIRO, Agriculture and Food, Hobart, Australia
Seasonal climate outlooks have long offered the promise of de-risking agricultural decisions within the farm enterprise and across the value chain more broadly. Barriers to adopting this information stem mainly from issues of confidence. Confidence in being able to interpret the forecast and confidence around the relevance and skill within an agricultural context. This paper outlines research activities that have sought to: 1) improve the usefulness and value of forecasting information through understanding user requirements in agribusiness, 2) provide new approaches and metrics to test climate model performance and 3) provide a streamlined and consistent framework to evaluate seasonal climate model output for predicting agricultural productivity.
User engagement within the grains industry emphasised the diversity of perceptions of the usefulness of seasonal climate information. ‘But how good is the forecast?’ was a common response from our stakeholders who were unclear on the skill of the model for their purposes. We provide an overview of a seasonal outlook for broad-acre farming that aligns seasonal climate information with on-farm decision patterns. The key components of this outlook include: new metrics that gauge confidence in the model output, a categorical forecast approach that captures resolution of the forecast and the inclusion of current growing season conditions, to help orientate the user and to identify potential outcomes for their enterprise (Mitchell and Brown, 2019).
The rainfall forecasting approach identifies periods where the model provides relatively clear guidance for ‘dry’, ‘average’ or ‘wet’ outcomes or alternately where there is little agreement on a particular outcome. Even in instances where the model lacked a clear signal, identifying conditions that were highly unlikely to eventuate were perceived to provide important information for decision-making. We assessed forecast confidence for users in agriculture that identifies time periods where the forecast is sufficiently misleading and could potentially lead to costly decisions for farmers. Further value from a forecast is realised by presenting the forecast outcomes relative to the current in-season rainfall and information on summer rainfall preceding the growing season. This can highlight opportunities or risks in acting on forecasts at any given time within the farm calendar.
To establish an overarching benchmarking framework and analysis platform we developed the AgScore tool. This tool evaluates climate model performance using a consistent framework based on several criteria that cover both climate-based metrics and detailed crop and pasture growth indices. AgScore represents a streamlined and repeatable approach for providing quantitative feedback to the climate modelling and agricultural research communities. It will help guide future research investment and the development of the next generation of seasonal forecasting for agriculture. References
Mitchell P., Brown J. 2019: What makes a ‘good’ seasonal forecast? Delivering actionable climate outlooks for grains farming. In: Cells to Satellites. J Pratley Ed. Proceedings of the 19th Australian Society of Agronomy Conference,25-29 August 2019, Wagga Wagga, NSW, Australia 2019. (http://www.agronomyaustraliaproceedings.org/)
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
VALIDATION AND IMPACT OF SEASONAL PASTURE AVAILABILITY AND LAMB GROWTH PREDICTIONS IN ASKBILL
Johan Boshoff
University of New England
NO ABSTRACT
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
INNOVATIVE PATHWAYS FOR DELIVERING ACTIONABLE WEATHER AND CLIMATE INFORMATION
Jaclyn Brown
CSIRO Agriculture and Food, Hobart, Australia.
The skill of weather forecasts has increased dramatically in the last few decades. At longer time scales, seasonal climate forecasting techniques have switched from statistical to dynamical methods and provide valuable insights into the year ahead. Beyond this, multi-year and decadal forecasts are an emerging capability with a vast array of potential stakeholders. Climate projections are undoubtedly essential for planning and adaptation. Given these great sources of information, why do we anecdotally hear from stakeholders that the forecast is ‘useless’ and you are ‘better off flipping a coin’? While there is always room for improvement in forecasting, the challenge for harnessing greater impact from forecasts may lie in how we deliver them. Here we discuss some of our learnings and our proposed solutions for delivery.
Extensive stakeholder engagement continues to show us that there is a broad spectrum of users. These range from users that don’t look at forecasts, to those who say ‘you’re the experts, just tell me the answer’ to ‘I want a full discussion of why and how this is happening. It is difficult to make a tool that services all these attitudes. We have learnt that stakeholders are frustrated by the many sources of information that they need to be across. This is often a reflection of institutional structure i.e. a web page for current conditions, another for the weather forecast, another for soil moisture, another for the climate outlook and so on. What the stakeholder actually wants is a single source that contains everything about ‘their situation.’ For example, a farmer wants to know, on their farm, how much rain there has been this year and how that compares to normal; what has happened in the last week, what is happening next week and for the rest of the year? Further the information they are looking for needs to be tailored to their decision-making process, at their location. A seasonal climate forecast has the most value when a decision can be made relative to information on the current state of play and an action taken, otherwise it is just ‘nice to know.’
Sustaining the delivery of well-crafted decision support tools is often difficult when it is reliant on short- term research funding. Huge amounts of energy can be put into building a tool that is well-loved but if there isn’t the funding to maintain it, all that effort can be lost and institutional reputation is damaged. For these reasons the Weather and Climate Decisions team in CSIRO are building Software as a Service (SaaS) approaches. We build scientific workflows, delivered through APIs containing bespoke agricultural and climate metrics which embeds our extensive scientific capability into highly scalable and repeatable data services. These APIs can then be purchased by intermediaries such as AgTech companies who can build the tools for individual agricultural sectors, customers and locations. The business model is sustainable as we have an on-going funding source to deliver data and develop new data feeds. It is also a faster path to impact by allowing others with specialist skills and domain knowledge to identify requirements of users and market and deliver the information they need. Here we provide some examples of future products which deliver weather and climate information, from a range of sources, tailored to individual user’s needs.
More information can be found at https://products.csiro.au/agclimatedatashop/
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
OPERATIONAL CLIMATE SERVICES IN EUROPE: THE CASE OF THE COPERNICUS CLIMATE CHANGE SERVICE
Carlo Buontempo
Copernicus Climate Change Service, ECMWF, Reading, UK
Copernicus is the European Union's Earth Observation Programme, looking at our planet and its environment. The programme, which is coordinated and managed by the European Commission, is implemented in partnership with the Member States, the European Space Agency (ESA), the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), the European Centre for Medium-Range Weather Forecasts (ECMWF), EU Agencies and Mercator Océan. Alongside the space component and the raw satellite data it generates, Copernicus offers information services for six thematic areas: atmosphere, marine, land, security, emergency and climate change. The European Centre for Medium Range Weather Forecasts (ECMWF) has been entrusted to run, on behalf of the commission, both the Copernicus Atmosphere Monitoring Service (CAMS) and the Copernicus Climate Change Service (C3S). Here we present the basic structure of C3S, its most tangible outputs, as well as the strategy that has been followed to ensure the programme had a lasting impact on the societal ability to develop downstream climate services for the ultimate benefit of European citizens.
CLIMATE FORECAST SERVICES
David Jones
Bureau of Meteorology, Melbourne, Australia
NO ABSTRACT
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
AN OVERVIEW OF THE BUREAU'S CURRENT ACTIVITIES IN HYDROLOGICAL PROJECTIONS AND THEIR LINKS TO FUTURE CLIMATE SERVICES
Louise Wilson1, Chantal Donnelly2, Pandora Hope3, Wendy Sharples1, Justin Peter3, Elisabeth Vogel1, Sean Loh1, Jake Roussis1, Margot Turner1, Stuart Baron-Hay4 and Ulrike Bende-Michl5
1 Water Investigations, Bureau of Meteorology, Melbourne, Australia 2 Water Investigations, Bureau of Meteorology, Brisbane, Australia 3 Science to Services, Bureau of Meteorology, Melbourne, Australia 4 Water Investigations, Bureau of Meteorology, Perth, Australia 5 Water Investigations, Bureau of Meteorology, Canberra, Australia
Australia's water policy and infrastructure investment decisions require high-resolution climate information taking into account both past and future variability. Currently water information exists for limited geographical regions such as single catchments, urban regions or states; stems from varying regional downscaling efforts and uses different methods to interpret this data for hydrological impacts. These regional downscaling and hydrological impact data collections are often tailored for specific purposes only or are not application-ready, which poses additional barriers to their use across the water and other sectors. Agreed and consistent approaches across water impacts at the national scale are yet to be developed. However, an accessible and consistent set of climate projections for water will help ensure that climate change risks are properly factored into decision-making.
The Bureau of Meteorology is undertaking to produce an ensemble of consistent, national projections of the impacts of climate change on water and water related variables. The project aims to bring together several state-of-the-art downscaling and bias correction techniques together with the CMIP5 ensemble to sample uncertainty along the impact modelling chain. Uncertainties due to downscaling of global circulation model (GCM) outputs are considerable and as part of this project, the currently available downscaled climate projections for Australia, both statistical and dynamical, will be evaluated and bias- corrected for use as an ensemble of downscaled climate data to force hydrological models. Hydrological indicators will be processed from the hydrological model outputs and presented together with key change and confidence messaging to provide application ready climate change impact data for the water sector.
The final service aims to support customers with both nationally modelled climate change impacts on water as well as hydrological model ready ensembles of downscaled climate inputs for customers to run their own models. In this presentation, we present an overview of the Bureau's current activities in hydrological projections and their links to future climate services.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
BUSHFIRES AND WEATHER EXTREMES: SEAMLESS PRODUCTS FROM SEASONAL PREDICTION TO CLIMATE PROJECTIONS
Andrew Dowdy
Climate Research Section, Bureau of Meteorology, Australia
New data and guidance products are available for bushfire and other extreme weather conditions, including using a seamless approach to make long-range climate predictions more consistent with historical observations (based on quantile matching). These seamless products are intended to be easier for users to apply, given that their existing applications are often based on historical observations, noting relevance for sectors such as emergency management, insurance/finance, energy, water, agriculture and environment.
Results are presented for variables such as rainfall, temperature, humidity and wind measures. These variables are needed for calculating fire weather indices such as the McArthur Forest Fire Danger Index (FFDI), as is commonly used in various regions of Australia for broad-scale climate analysis purposes. Extreme conditions are examined using fixed-magnitude thresholds over Australia as well as locally- defined thresholds. The ability of the models to represent extremes is assessed, including for fire weather conditions in the historical time period as well as future periods. Factors influencing the predictability of extreme weather conditions at climate time scales are also discussed.
A more seamless service for providing extreme weather and climate hazards information across different time scales is intended to help enhance planning and policy development, as well as climate adaptation and disaster risk reduction efforts.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
CLIMATE SERVICES FOR CLIMATE RISK
Karl Braganza
Bureau of Meteorology, Melbourne, Australia
While much of the public conversation around climate change relates to emissions of carbon dioxide, there is also a parallel conversation about climate risk and adaptation. The Bureau’s operational role will see it playing a large part in Australia’s efforts to manage future climate risks and undertake informed adaptation. This role will likely see the Bureau engaged through all of its Business Solutions Groups, as those sectors respond to the changes being imposed by both climate change itself, and global policy and market responses to climate change. It should be expected that National Forecast Services and Science to Services will also play a large role in informing climate risk and adaptation, most obviously through providing data and scientific information for improved planning and preparation for disaster risk reduction and public safety. It is therefore important to understand the current environment around climate risk in Australia, and the major changes underway in finance, insurance and the management of critical infrastructure.
Karl Braganza will provide insights on the above, drawing on his experience in playing a key role in briefing government agencies on the science of climate change, and his extensive engagement with the emergency management, energy and water resources sectors. Karl will also present on opportunities and risks for the Bureau in this emerging field of endeavour.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
THE RATE AT WHICH WE WILL EXPERIENCE UNPRECEDENTED HIGH TEMPERATURE EXTREMES: BENEFITS AND LIMITATIONS OF REDUCING GREENHOUSE GAS EMISSIONS
Scott Power123 and Francois Delage1
1Bureau of Meteorology 2Global Change Institute, University of Queensland. 3ARC Climate Extremes Centre of Excellence.
Changes in the intensity or frequency of extreme climate events can profoundly increase the disruption caused by climate change. The more extreme these events, the greater the potential to push ecosystems and communities beyond their ability to cope. The rate at which existing high temperature records have been broken has increased in response to rising global greenhouse gas emissions, and the rate at which historical records are surpassed is projected to increase further over the coming century. Here we examine future events that will be so extreme that they will not have been experienced previously, when new records are set. We examine record setting and record smashing – when a new record is set that exceeds the record it replaces by a large amount - in 22 climate models from the world’s leading climate research centres.
Under a scenario called RCP8.5 (in which increases in emissions continue unabated) the globe warms around 3.2-5.4 degrees by the end of the century compared with the latter half of the 19th century and high monthly mean temperature records are projected to be set in approximately 58% of all years. This figure varies regionally. The highest rate at which records will be set and the greatest benefits from reducing emissions on this rate tend to occur in the poorest countries: approximately 68% of years will see records set in the world’s least developed countries and in small island developing states by the end of the century, whereas this figure is only 54% in wealthier nations. These figures all drop to 14% under a scenario with major cuts in emissions (RCP2.6; 0.9-2.3 degrees global warming by the end of the century).
Near the end of the century, the likelihood of new records exceeding previous records by more than 1.0°C is 8 times more likely if emissions are not markedly reduced, and are over 20 times more likely than would be the case if anthropogenic emissions had not increased at all. The benefit of markedly reducing emissions is clear: it decreases the rate at which we will experience unprecedented, extreme, high monthly maximum temperatures. However, these benefits take more than twenty years to be significant, and the likelihood of setting unprecedented high monthly temperature records is projected to remain at high levels for the next two decades. This means that people will need to manage risks associated with these events for at least the next twenty years– longer if emissions are not markedly reduced.
References
Power, Scott B., and F. P. D. Delage, 2019: Setting and smashing extreme temperature records over the coming century. Nature Climate Change, 9, 529–534. https://doi.org/10.1038/s41558-019-0498-5
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
CLIMATE CHANGE PROJECTIONS FOR NORTHERN AUSTRALIAN RAINFALL
Sugata Narsey
Science to Services, Bureau of Meteorology, Melbourne, Australia
The tropical north of Australia experiences much of its rainfall in the Austral summer, much of which occurs as part of the Australian summer monsoon. However, the wet season rainfall exhibits large year- to-year variability, as well as large intra-seasonal variations. Long-term climate projections for the region are also highly uncertain, creating a significant challenge for the provision of advice to the Bureau of Meteorology's stakeholders, including the public.
In this presentation I will describe some of the key stakeholders who access information on northern Australian rainfall climate change projections, along with some examples of the types of information that have been provisioned to them. I will also examine some of the factors contributing to the uncertainty in those projections, as well as some of the evidence that may help to decrease the uncertainty and range of the projections. Finally, I will comment on some of the key activities currently under way within the Climate Research section that we hope will help address the uncertainty in climate change projections for northern Australia.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
VICTORIAN CLIMATE PROJECTIONS 2019: TURNING PROJECTION DATASETS INTO PROJECTIONS
John Clarke1, Marcus Thatcher1, Michael Grose2, Vanessa Hernaman1, Craig Heady1, Tony Rafter1, Vanessa Round1, Claire Trenham3 and Tim Erwin4
1CSIRO Climate Science Centre, Aspendale, Australia 2CSIRO Climate Science Centre, Hobart, Australia 3CSIRO Climate Science Centre, Canberra, Australia 4CSIRO Information Management & Technology, Clayton, Australia
In October, the Victorian Government released new climate projections for Victoria. For this work, the Department of Environment, Land, Water and Planning and Wine Australia funded new dynamical downscaling at 5 km resolution. Uniquely, the Victorian Climate Projections 2019 project combines the new modelling with pre-existing results from global climate model and multiple regional climate model simulations.
Evaluation of the new modelling demonstrated that the high-resolution results provided significant new insights in some parts of Victoria. The new modelling shows a plausible hotter ‘hot case’ for some regions than had previously been projected. In addition, the new modelling shows plausible enhanced drying on the western slopes of the alpine regions of north-east Victoria. This is consistent with other downscaled results, such as NARCliM. Additionally, we explored new ways of presenting the projections to make the most of the multiple lines of evidence available. These included framing the results in terms of the Paris Agreement targets of 1.5 and 2.0 degrees warming relative to the pre- industrial era.
A range of guidance material was also developed to help users of the projections make sense of the multiple data sources available for the state. A series of training workshops were rolled out to government staff in metropolitan and regional areas. The results are available through some of the Climate Change in Australia tools and a range of application-ready, relative change and raw model datasets are available for direct download. Importantly, ongoing support is being provided through a ‘help desk’ and more guidance material will be added over time, including case studies.
Here, we outline the project and present key findings along with a snapshot of the technical and guidance resources.
References
Grose, M. R., J. Syktus, M. Thatcher, J. P. Evans, F. Ji, T. Rafter, and T. Remenyi, 2019: The role of topography on projected rainfall change in mid-latitude mountain regions. Climate Dynamics.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
DIVING INTO WEATHER OBSERVATIONS FOR NON-TRADITIONAL USES
Simon Allen1 and Joel Lisonbee2
1Agriculture and Food, CSIRO, Sandy Bay, Tasmania, Australia 2Agriculture, Bureau of Meteorology, Canberra, ACT, Australia
As part of the federally funded drought relief program the Bureau of Meteorology was tasked with the creation of ‘Weather and Climate Guides’ for all Australian Natural Resource Management regions (http://www.bom.gov.au/climate/climate-guides/). The guides focus on helping farmers and growers understand how the weather in their region impacted their activity and assisting them in seeing any signal within the naturally noisy (highly variable) weather patterns. As part of the guide development process, face-to-face meetings were undertaken in each of the regions. At these meetings the farmers were able to describe how they used weather and climate data and the steps required to convert ‘standard’ weather variables into indices and indicators that more directly related to their operations.
In understanding the change in temperature seen in historical records, for example, a general statement that: ‘The average temperature has increased 0.6oC in the last sixty years,’ meant very little. On the other hand: ‘There have been, on average, five more days over 42oC in the last thirty years compared to the previous thirty years,’ was easier to conceptualise.
Multi-parameter analyses and their trends also provided ways to make changes and their impacts on specific agricultural processes clearer. The number of days between the last frost and the first day over 32oC was one specific example requested. Number of days where the temperature humidity index (THI) exceeded key thresholds was another simple parameter that went a long way to helping pastoralists understand changes that may impact their animals.
As the project moved into multiparameter analysis, the project moved away from observations to derived or inferred products. In generating annual views of numbers of days where a specific phenomenon occurred, gaps in observations had to be filled or highlighted. In using the longest records available in each region, the value of the ACORN-SAT reconciled temperature records became apparent, but left uncertainty around the derived or inferred products used.
While trying to tease out subtle signals in long-term records, it became apparent that products such as the Queensland government SILO database provided remarkably easy access to BOM observations but, in attempting to create a continuous record, this compromised its usefulness. SILO’s use in products such as the CliMate App very easily lead to false impressions of observational trends. The team involved in the analysis for the climate guides would like to share some of their experience in diving into the weather observations and suggest some key components of any future automated weather observation data retrieval system.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
CLIMATE ANALYSIS FORECASTING ENSEMBLE (CAFE) SYSTEM – EARLY RESULTS FROM THE CSIRO DECADAL CLIMATE FORECASTING PROJECT
Richard Matear and the Decadal Climate Forecasting Team
CSIRO, Australia
Australia is a climate exposed country with many industries dependent on favourable climatic conditions to ensure profitability. Our climate exposure is exacerbated by the combined influences of large internal variability coupled with increasingly intense extreme events like drought, floods and bush fires driven by anthropogenic forcing. Many primary producers and government entities (at the local, state and federal levels) are increasingly including climate information in their 1 to 10-years business planning tools and assessments. These factors have contributed to increasingly strong commercial and government interest in accessing climate forecasts that extend existing seasonal forecasts to the multi- year and decadal timescales.
The challenge of the CSIRO Decadal Climate Forecasting Project is to improve and advance the use of multi-year to decadal forecasts to enable Australian industries and regulators to better deal with climate variability and climate extremes. The project’s mission is to improve multi-year to decadal climate forecasts by: • Advancing fundamental climate research into sources of predictability of the climate system, the processes that give rise to that predictability, and the critical observations that will help us to realise the potential climate predictability • Applying state-of-art ensemble data assimilation to determine the initial climate state for the forecasts • Integrating climate processes with the forecasting effort in the development of the climate perturbations used in the ensemble forecasts
Our Climate Analysis Forecasting Ensemble (CAFE) system is a vital tool that unifies the project’s research effort to advance the utility of climate forecasts. In my presentation, I first review our progress and compare our climate forecasts to other forecasting systems. Secondly, I outline our future research efforts in climate processes, CAFE system development, and forecast applications.
References
Project’s website - https://research.csiro.au/dfp/about/
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
PLANS FOR THE NEXT GENERATION OF NATIONAL AND REGIONAL CLIMATE PROJECTIONS FOR AUSTRALIA
David Karoly
CSIRO
NO ABSTRACT
CLIMATE SERVICES IN THE UK MET OFFICE - CHALLENGES AND SOLUTIONS
Chris Hewitt1,2
1Met Office, Exeter, UK 2Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, Australia
A growing number of decision-makers worldwide are already aware, or are becoming aware, of the threats and opportunities arising from climate variability and climate change. In recognition of this there have been major international activities, over the past decade in particular, such as World Climate Conference-3 and the Global Framework for Climate Services, the ongoing UNFCCC and IPCC processes, as well as important and successful regional and national activities.
As is being discussed at this Bureau of Meteorology Annual R&D Workshop, there are major developments in Earth system modelling, observations, communication and use of uncertainty information, closer engagement with social and economic science, etc. The scientific community has a wealth of data, information and knowledge which can be, and often is, of use to decision-makers. However, the situation constantly needs to improve and evolve.
The development, delivery, uptake and use of climate services face numerous challenges. Such challenges include (but are not limited to): the scientific capability that underpins the climate services is not necessarily able to provide information of use or value to the decision-makers; institutional capabilities and capacities may not be able to meet the demands from the decision-makers; shortcomings often exist in the awareness and understanding of available knowledge; insufficient understanding by the climate service providers of the needs of the decision-makers.
This talk will provide examples of the UK Met Office’s international climate service activities, highlighting the above challenges along the way, and offering some suggested solutions.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
INTEGRATING CLIMATE PROJECTIONS INTO STATE GOVERNMENT DECISION-MAKING
Ramona Dalla Pozza1, Clare Brownridge1, Jacqueline Thurgood1, and Geoffrey Steendam2
1Environment and Climate Change, Department of Environment, Land, Water and Planning, East Melbourne, Victoria, Australia. 2Water and Catchments, Department of Environment, Land, Water and Planning, East Melbourne, Victoria, Australia.
The Victorian Government is bridging the gap between the scientists who provide the data and the users of the data, to build the capacity of Victorians to adapt to climate change. Understanding the specific needs of climate data users, providing support and bringing together climate scientists and users are key elements for success. Victoria has already made good progress in many areas including the agriculture and water sectors, with tailored datasets and research programs that integrate climate projections into decision-making. Under Victoria’s Climate Change Act 2017 government must consider ‘the best practicably available climate change science and its implications’ to underpin adaptation planning and decision making.
To provide the Victorian water sector with tailored climate and hydrology research and guidance, the Victorian Government has invested in the Victorian Water and Climate Initiative. This program has built on earlier research through the Victorian Climate Initiative, looking at past, present and future climate and hydrology to gain insights into the challenges that climate change poses to water resources.
We have also worked with CSIRO to develop local-scale climate projections for Victoria – Victorian Climate Projections 2019 - to help inform decision making for other sectors. We are now supporting the statewide integration of climate data and information into risk assessments to understand the impacts of climate change for Victoria to enable effective adaptation for the future.
NATIONAL CLIMATE SCIENCE ADVISORY COMMITTEE: NEXT STEPS
Chris Johnson
Climate Change Policy Branch, Department of the Environment and Energy
NO ABSTRACT
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
IMPROVING CLIMATE LITERACY THROUGH COMMUNITY AND TV NEWS
Ailie Gallant1,2, David Holmes2,3, Remy Shergill3, Stephanie Hall3, Zoe Gillet1, James Goldie3 and Steven Thomas1
1School of Earth, Atmosphere and Environment, Monash University, Clayton, Australia 2Monash Climate Change Communications Research Hub, Monash University, Caulfield, Australia 3School of Communications and Media Studies, Monash University, Caulfield, Australia
Communication style is important when communicating with non-expert and non-scientific audiences. Professional science communicators highlight that relatable story telling is a key for effective communication of scientific information on climate change. In particular, hyper-localised frames, from city to suburban scale, are ideal. However, when describing climate change attribution or future projections this spatial scale is also associated with the greatest uncertainty, particularly for non- temperature related phenomena and high-impact events, for which audiences have a keen interest. This presents a dilemma for scientists because the most effective ‘story’ for communication is also associated with a higher degree of uncertainty and nuance that can easily cause confusion for lay-audiences.
For lay-audiences, an increase in climate literacy is one method that has been shown to reduce misconceptions and confusion. Climate literacy is defined as having a basic understanding of the function of the climate system and the science behind anthropogenic climate change. Unlike the traditional sciences (e.g. chemistry, biology and physics), this knowledge is often completely lacking given the lack of climate science education in the primary and high school system until very recently. The media has been identified as a key source of information for the general public of their climate and climate change knowledge. Trust is relatively high when messages about climate change are provided directly from climate scientists and TV meteorologists and some limited studies have shown the exposure to climate education via these conduits is effective.
Using the above ideas, the Monash Climate Change Communication Research Hub has developed a world-first project to present regular climate information on the municipal and city scale. Weekly columns presenting localised climate information from long-running Bureau of Meteorology weather stations are published in the Leader newspapers of Greater Melbourne, which have an estimated real readership of 1.5 million people. These columns present a variety of information about climate change and climate variability in an effort to improve climate literacy. Similar graphs are also shown approximately once per month on both commercial and non-commercial news broadcasts during the weather segment. Here, the project will be described in detail and preliminary results on the effectiveness of the newspaper columns for improving climate science awareness and literacy will be presented.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
BEACH EROSION EARLY WARNING SYSTEM (EWS): A NEW NATIONAL RESEARCH INITIATIVE
Ian Turner1, Neil Carroll8, Bruce Coates5, Michael Cuttler2, Diana Greenslade3, Jeff Hansen2, David Hanslow5, Mitchell Harley1, Michael Kinsela5, Christopher Leaman1, Fangjun Li6, Ryan Lowe2, Nashwan Matheen1, Neal Moodie3, Craig Morrison7, Nathaniel Plant4, Kristen Splinter1, Hillary Stockton4, Adrian Turnbull7 and Stefan Zieger3
1University of New South Wales, Sydney, Australia 2University of Western Australia, Perth, Australia 3Australian Bureau of Meteorology, Melbourne, Australia 4United States Geological Survey, St Peterburg FL, USA 5NSW Department of Planning Industry & Environment, Sydney, Australia 6WA Department of Transport, Perth, Australia 7Northern Beaches Council, Sydney, Australia 8City of Mandurah, Australia
Australia is a distinctly coastal-focused nation. Half the continent’s coastline comprises sandy beaches with over 85% of Australians living within a narrow coastal strip, which will only increase. The amenity and storm protection provided by beaches nationally is estimated to be in the range of $3.8-$13 million for every kilometre of sandy shoreline. No less significantly, the cultural and environmental value of beaches is also well recognised and helps define the Australian way of life.
Recent extreme storms such as the East Coast Low that severely impacted the Queensland, NSW, Victorian and Tasmanian coastlines in June 2016 are a reminder of the extent to which storm wave damage and coastal erosion can and will threaten the safety of Australia’s coastal communities and cause tremendous damage to its coastal infrastructure. But this same event also served to highlight the gap that presently exists between coastal science and emergency warning and preparedness.
There is a need in Australia for a nationally coordinated effort to develop and establish a storm wave damage and beach erosion early warning system (EWS) to alert and inform emergency managers and vulnerable coastal communities. To address this, the Australian Research Council has invested in a new research initiative involving university, government and international partners, with the aim to develop the necessary underpinning scientific knowledge and technical framework for an open-coast hazard EWS consisting of two fully-integrated components: (1) regional-scale storm wave damage forecasting of the occurrence and geographical distribution of where storm waves are anticipated to impact and/or over-top beachfront dunes, to a resolution of nominally 100 m alongshore; and (2) local-scale coastal erosion forecasting of the quantitative and time-evolving eroded sand volume (i.e., ‘storm demand’) at known erosion hotspots, vulnerable or regionally-representative sites. This 3-year collaborative research program is developing and pilot testing EWS capabilities at the WA and NSW coastlines, bringing together national and international research and operational capabilities with the coastal emergency management experiences and needs of state and local government partners.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
WAVE OVERWASH WARNING AT A SOCIAL MEDIA BLACKSPOT
David Hanslow1, Michael Kinsela1, Hannah Power2, Caio Stringari2 and Murray Kendall2
1NSW Department of Planning Industry and Environment, Newcastle, Australia 2University of Newcastle, Newcastle, Australia
In this paper we report on the development of a wave overwash warning system for the Figure Eight Pools, a highly exposed rock platform to the south of Sydney, Australia. This site has seen dramatically increased visitation in recent years as a result of popularisation on social media. Open coast rock platforms in south east Australia are exposed to high wave energy and are typically subject to intermittent wave overwash during higher tides and waves. The exposure of these environments, combined with their use for recreation, results in a high hazard level for rock platform users. Given the increased visitation at this site and high numbers of emergency incidents, we develop an overwash hazard forecast for the pools with the aim of providing a tool to help enable safer visitation.
To investigate wave overwash at Figure Eight Pools, we carry out detailed measurements and analysis of platform morphology, ocean and nearshore wave conditions, and overwash wave processes on the rock platform. This included undertaking detail surveys of the platform and neighbouring sea bed; analysis of both deep-water and nearshore wave and water level together with coincident overwash observations from two camera systems installed on the cliffs overlooking the site. Concurrent hourly nearshore (30 m water depth) wave measurements and platform overwash observations are analysed with tide data to investigate the relationship with the extent and frequency of overwash waves on the rock platform.
The distance and frequency of wave overwash inundation is correlated with nearshore wave and ocean tide parameters to categorise the relative overwash hazard level and to develop a predictive relationship between ocean conditions and the platform overwash hazard level. This predictive relationship is implemented with forecast tide and wave data to forecast overwash hazard 4 days into the future. The performance of the hazard prediction is examined based on measured inputs and is assessed against the observation data.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
THE IMPACT OF DAMAGING WINDS ON RESIDENTIAL BUILDINGS
Harald Richter1, C. Arthur2, D. Wilke1, S. Martin2, M. Wehner2, M. Dunford2, E. Ebert1, J. Sexton2 and C. Mooney1
1 Australian Bureau of Meteorology, Victoria, Australia 2 Geoscience Australia, ACT, Australia
Strong surface wind gusts are a meteorological hazard that are predominantly produced on large spatial scales by storms such as east coast lows, tropical cyclones or large-scale thunderstorms. Interest in this hazard from a response agency point of view lies in their impact on the natural and built environment. At present, weather forecast models still predict mostly 'raw' meteorological output such as surface wind speeds at certain times. This model output needs to be combined with exposure and vulnerability information to translate the forecast hazard into predicted impact.
The Bushfire and Natural Hazards CRC project Impact-based forecasting for the coastal zone: East- Coast Lows will demonstrate a pilot capability to deliver spatial impact forecasts for residential housing from high resolution (1.5 km) Bureau of Meteorology (Bureau) models runs. The project is a collaborative effort between the Bureau and Geoscience Australia drawing together the respective expertise of each agency. It is the first example of incorporating scenario impact modelling previously conducted by Geoscience Australia into a hazard forecasting sphere. The project is focusing on the wind impact from three NSW severe weather events during 2015 and 2016. The wind hazard data are provided by BARRA-SY reanalysis data on a 1.5 km grid, with damage data acquired from NSW State Emergency Services (SES) and the NSW Emergency Information Coordination Unit (EICU).
We will show that the multi-hazard nature of an east coast low event makes attributing the observed building damage to a single hazard difficult. We will discuss the required enhancements to the SES/EICU damage survey templates that would lead to improvements in the development of relationships that link specific hazards to the observed physical damage (the hazard attribution problem). More generally, we will also show that spatial impact forecasting using heuristically derived vulnerability relations is feasible, and point towards what is required to improve the quality of such forecasts.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
FUTURE WARNINGS POLICY
Carla Mooney, Shannon Panchuk, Megan O'Donnell, Brenda Mackie and Richard Hammond
Bureau of Meteorology, Melbourne, Australia
Recent Australian research has found that prompted awareness of various warning systems sits between 41% and 56% (Metrix Consulting, 2019). Warnings are intended to trigger protective action of some kind. However, there is evidence that seeing or receiving a warning often does not translate into recommended behaviour. The reasons people don't respond to warnings are complex and include personal recognition of risk, previous experience and perception about warnings. Message construction and communication methods have a strong influence on how people respond to warnings and much can be done in these arenas to improve their efficacy.
The National Review of Warnings (Emergency Management Victoria, 2014) called for greater consistency in the development of warnings frameworks across jurisdictions and hazards. This was followed by a commitment in 2017 by the Commissioner's and Chief Officers Strategic Committee to a multi-hazard three level warning system. There has been considerable progress with the development of this policy. It is being built from a robust evidence base and includes detailed proposals for a nested warning system with scaled warnings and associated calls to action. In parallel to this national policy development and in line with international policy, there is a move towards impact-based forecasts and warnings. Impact-based warnings and forecasts involve bringing together information about the hazard with exposure and vulnerability. Providing information about the weather and its likely consequences will help people understand the potential impact of extreme weather and increase their responsiveness to warnings.
The first phase of the Future Warnings Project was an audit of the current Bureau warning products and services against the attributes of an end-to-end impact-based forecast and warning service. The next stage of the project is the development of a Warning Framework and Roadmap. The Framework will set the policy direction including, for example, impact-based warnings and consistency with the proposed national multi-hazard three level warning system. The Roadmap will chart the service transition. Improved warning systems designed to increase the uptake, understanding and motivation for protective action will contribute to zero lives lost through natural hazards and a reduction in the social and economic impact of extreme weather events.
References
Emergency Management Victoria 2014: National Review of Warnings and Information: Final Report, Victoria Government, Melbourne, Cube Group.
Metrix Consulting 2019: Multi Hazard Warnings Social Research: Research Report Stages 1 to 3.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
METEOROLOGICAL DATA AND MACHINE LEARNING: A VIEW FROM THE CUSTOMER
Will Dubyak
United Services Automobile Association
This talk, and the follow-on workshop, address the many ways in which diverse meteorological data products are leveraged in the Insurance Industry to build Machine Learning decision support models. Weather data is the original big-data problem: it is very difficult to consume directly, particularly at scale. Through creative use of advanced analytics, these data are now providing insights that translate directly into targeted efforts to deliver support and risk mitigation services to customers impacted by catastrophic events.
MACHINE LEARNING AND ENVIRONMENTAL MODELLING IN AN EXA-SCALE WORLD
Peter Steinle
Bureau of Meteorology, Melbourne, Australia
The efficiency of existing Numerical Environmental and Weather Prediction (NEWP) systems relies on algorithms and codes that are not expected to be efficient enough on new High Performance Computing (HPC) architecture to meet user demands. Thus, all aspects of major NEWP software systems are being reassessed – both in terms of their software design and the underpinning science.
Preparation of this transition has been underway for up to a decade, however, while the upgrades initially focused on numerical grids, then improving software systems, it quickly became apparent that nearly all aspects of NEWP systems could potentially benefit from considering aspects of Artificial Intelligence (AI)/Machine Learning (ML).
With new HPC architecture expected to be commonplace by the middle of the next decade, this has been identified as a priority issue and discussed at length at recent meetings such as the Working Group on Numerical Experimentation which includes the lead scientists of atmospheric model development groups from around the world. In addition, the World Meteorological Organization's World Weather Research Program has made exa-scale computing, including AI/ML, a major focus area in their strategic implementation plan.
This presentation will summarize the opportunities, challenges and potential benefits recently identified around integrating AI/ML into NEWP models.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
MACHINE LEARNING APPLICATIONS FOR WEATHER/CLIMATE
Huidong Jin
CSIRO Data61, Canberra, Australia
Machine learning is concerned with the design and development of algorithms that allow computers (machines) to improve their performance over time based on data. It is highly multidisciplinary, combining artificial intelligence, statistics, game theory, neuroscience, data mining, etc. It can extract useful patterns or knowledge from image, text and other forms of predominantly unstructured data. These data-driven patterns are not only used for prediction and decision-making, but also for scientific discovery and improved data collection. The combination of unprecedented data sources, increased computational power, and advanced statistical modelling and machine learning techniques, offers numerous opportunities for expanding our knowledge about weather or climate from data. Though machine learning may not give a mechanistic or biophysical model of a given phenomenon, successful applications are widely reported, such as precipitation now-casting, seasonal forecasts, and climate projection downscaling (Crimp et al, 2019). We present a few applications performed by CSIRO.
Observations from weather stations such as precipitation can be partially missing due to instrument failure, power outages, or operation interruptions. To infill these data gaps appropriately, we model daily precipitation time series via a three-state Markov chain model to simulate the occurrence of dry, wet and extremely wet days. Rainfall amounts for wet and extremely wet days are modelled using two different distributions so as to capture extreme events better (Jin et al. 2019).
Both frost frequency and time windows have impact on crop yields. To understand whether frost will decrease under climate change, we build a model to consider spatial and temporal context to downscale global climate models (GCMs). Driven by 10 GCMs, the projection results show the frost frequency in late winter and spring won’t decrease substantially in south-east Australia in coming decades. Such paradoxical frost frequency given the background of global warming is worth further investigation (Crimp et al 2019).
To enable growers to rapidly understand the spatial extent of crop damage on their farms following a frost event to aid with timely post-frost decision-making, a CSIRO team has collected high-resolution in-situ data on four farms, and used a non-parametric regression method to predict detailed frost maps. The frost maps have high cross-validation accuracy, which would be extended to big regions.
References
Crimp, S., Jin, H., Kokic, P., Bakar, S. and Nicholls, N., 2019. Possible future changes in South East Australian frost frequency: an inter-comparison of statistical downscaling approaches. Climate Dynamics, 52(1-2), 1247-1262. Jin, H., Shao, Q. and Crimp, S. 2019. Daily Rainfall Data Infilling with a Stochastic Model, MODSIM 2019, Canberra Australia, p.816.
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
USING MACHINE LEARNING TO IMPROVE OPERATIONAL WAVE FORECASTS
Jeff Hansen1,2, Chen Wu1,3, Phil Watson1,4 and Diana Greenslade5
1University of Western Australia Oceans Institute, Perth, Australia 2School of Earth Sciences, University of Western Australia, Perth, Australia 3International Centre for Radio Astronomy Research, University of Western Australia, Perth, Australia 4Oceans Graduate School, University of Western Australia, Perth, Australia 5Bureau of Meteorology, Melbourne, Australia
Operational wave forecasts rely on spectral wave models that due to their numerical implementation (i.e. phase-averaged) and resolution, either parametrize or do not fully resolve key physical processes that impact wave generation, propagation, and dispersion. These factors, coupled with potential errors in atmospheric forcing, can sometimes result in incorrect forecasts for wave conditions and/or the timing of their onset. Many offshore industries depend on accurate wave forecasting, and unexpected conditions may incur cost (due to halting an underway operation or a missed opportunity to complete an operation) or add safety concerns. In this presentation we outline results from an initial study to test the use of machine learning to adjust Bureau of Meteorology AUSWAVE-R wave forecasts. Eighteen months of archived wave forecasts, each extending 72 hours, were extracted at the location of three WA Department of Transport directional wave buoys. Eighty percent of the observed and forecast wave conditions were used as a training data set for a Recurrent Neural Network algorithm which was then used to adjust the remaining 20% (randomly selected and independent from training data). This initial test resulted in the root mean square error of the forecasts being reduced by 19% for significant wave height and by 40% for peak wave period and direction across all sites. The model also showed considerable skill in adjusting the arrival times of swells (Figure 1), which is of particular interest to the offshore industry. Currently the technique is also being applied to the spectral data from the buoys and forecasts. These initial results indicate that machine learning can be an effective means of improving existing operational wave forecasts with negligible additional computation.
Example 72-hour forecast showing original BOM forecast (black), machine learning adjusted forecast (blue), and buoy observations (red) for significant wave height (top) and peak period (bottom).
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
TO BE CONFIRMED
Werner Scholz
Xenon Systems
NO ABSTRACT
MACHINE LEARNING AT MONASH UNIVERSITY AND POTENTIAL APPLICATIONS IN METEOROLOGY
Ann Nicholson
Monash University, Clayton, Australia
NO ABSTRACT
ENHANCING DIGITAL CONTENT WITH MACHINE LEARNING AND AUGMENTED REALITY
Justin Freeman
Bureau of Meteorology, Melbourne, Australia
NO ABSTRACT
TO BE CONFIRMED
Matthew Greensmith
Amazon Web Services
NO ABSTRACT
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FORECASTING FOR THE FUTURE - ABSTRACTS OF THE BUREAU OF METEOROLOGY R&D ANNUAL WORKSHOP
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