DOCSLIB.ORG

Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range ISSN 0252-1075 Contribution from IITM Research Report No. RR-145 ESSO/IITM/SERP/SR/01(2019)/196 Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay Indian Institute of Tropical Meteorology (IITM) Earth System Science Organization (ESSO) Ministry of Earth Sciences (MoES) PUNE, INDIA http://www.tropmet.res.in/ ISSN 0252-1075 Contribution from IITM Research Report No. RR-145 ESSO/IITM/SERP/SR/01(2019)/196 Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay *Corresponding Author Address: Dr. A.K. Sahai Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune – 411008, India. E-mail: [email protected] Phone: +91-(0)20-25904520 Indian Institute of Tropical Meteorology (IITM) Earth System Science Organization (ESSO) Ministry of Earth Sciences (MoES) PUNE, INDIA http://www.tropmet.res.in/ DOCUMENT CONTROL SHEET -------------------------------------------------------------------------------------------------------------------------------------- Earth System Science Organization (ESSO) Ministry of Earth Sciences (MoES) Indian Institute of Tropical Meteorology (IITM) ESSO Document Number ESSO/IITM/SERP/SR/01(2019)/196 Title of the Report Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range Authors Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay Type of Document Scientific Report (Research Report) Number of pages and figures 37, 17 Number of references 37 Keywords Dynamical downscaling, Bias-correction, Extended range prediction Security classification Open Distribution Unrestricted Date of Publication January 2019 Abstract This study aims to document the strengths and shortcomings of forecast strategy being developed based on dynamical downscaling for better extended range prediction of extreme weather events. One heavy rainfall event and two cyclone cases are selected to dynamically downscale the global forecasts of Extended Range Prediction (ERP) system using Weather Research and forecasting (WRF) model at 9km for Indian domain. The ERP outputs are bias-corrected and fed to WRF as lateral boundary conditions to minimize migrated errors from parent model via boundary conditions. Results show bias-corrected downscaled ERP is more efficient in predicting extreme weather with 10-12 days lead time. Summary Indian subcontinent is prone to weather extremes like cyclones and heavy rainfall events causing severe damages due to floods and landslides. General circulation models (GCMs) running at coarse resolution are unable to forewarn the intensity of these catastrophic rains at sufficient lead time. In this study, a forecast strategy of dynamical downscaling is adopted to improve the extended range predictions of high impact weather events. Ensemble members of an Extended Range Prediction (raw-ERP) system based on higher (T382) and lower (T126) resolution of Climate Forecast System (CFSv2) and Global Forecast system (GFS) are downscaled individually using Weather Research and Forecasting (WRF) model. WRF run at 9 km resolution for Indian region such that the selected model domain is large enough to include ocean-atmospheric interactions in lateral boundary conditions. Downscaled ERP (ERPWRF) will take advantage of good prediction skills of raw-ERP in capturing the large scale signals and will help to reduce spatio-temporal errors in regional detailing. For the present study we have selected three different events - two cyclone cases and one heavy rainfall event. Global raw-ERP ensemble forecasts are bias-corrected to minimize GCM intrigued biases via boundary conditions. Bias-corrected and downscaled forecasts show improvement in predicting the spatio-temporal evolution of rainfall associated with the selected severe weather cases. This study affirms that dynamical downscaling can be an effective tool to generate useful high resolution predictions at 10-12 days lead time. Contents Abstract 1 Summary 2 1 Introduction 3 2 Data and Methodology 5 2.1 Models 5 2.2 Bias-correction & dynamical downscaling 5 2.3 Objective tracking algorithm 6 3 Results and Discussions 6 3.1 Cyclonic Storm Roanu of 2016 6 3.1.1 Results from Experiments 7 3.1.2 Results from final setup 8 3.2 Uttarakhand Heavy Rainfall 2013 8 3.3 Severe Cyclonic Storm Mora of 2017 9 4 Conclusions and Remarks 10 Acknowledgements 11 References 12 Figures 15 Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay Indian Institute of Tropical Meteorology, Pune-411008 Abstract This study aims to document the strengths and shortcomings of forecast strategy being developed based on dynamical downscaling for better extended range prediction of extreme weather events. One heavy rainfall event and two cyclone cases are selected to dynamically downscale the global forecasts of Extended Range Prediction (ERP) system using Weather Research and forecasting (WRF) model at 9km for Indian domain. The ERP outputs are bias-corrected and fed to WRF as lateral boundary conditions to minimize migrated errors from parent model via boundary conditions. Results show bias-corrected downscaled ERP is more efficient in predicting extreme weather with 10-12 days lead time. *Corresponding Author: Dr. A.K. Sahai Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune – 411008, India. E-mail: [email protected] Phone: +91-(0)20-25904520 1 Summary Indian subcontinent is prone to weather extremes like cyclones and heavy rainfall events causing severe damages due to floods and landslides. General circulation models (GCMs) running at coarse resolution are unable to forewarn the intensity of these catastrophic rains at sufficient lead time. In this study, a forecast strategy of dynamical downscaling is adopted to improve the extended range predictions of high impact weather events. Ensemble members of an Extended Range Prediction (raw-ERP) system based on higher (T382) and lower (T126) resolution of Climate Forecast System(CFSv2) and Global Forecast system (GFS) are downscaled individually using Weather Research and Forecasting (WRF) model. WRF run at 9 km resolution for Indian region such that the selected model domain is large enough to include ocean-atmospheric interactions in lateral boundary conditions. Downscaled ERP (ERPWRF) will take advantage of good prediction skills of raw-ERP in capturing the large scale signals and will help to reduce spatio-temporal errors in regional detailing. For the present study we have selected three different events - two cyclone cases and one heavy rainfall event. Global raw-ERP ensemble forecasts are bias-corrected to minimize GCM intrigued biases via boundary conditions. Bias-corrected and downscaled forecasts show improvement in predicting the spatio-temporal evolution of rainfall associated with the selected severe weather cases. This study affirms that dynamical downscaling can be an effective tool to generate useful high resolution predictions at 10-12 days lead time. Key-Words: Dynamical downscaling, Bias-correction, Extended range prediction 2 1. Introduction Extreme weather events are devastative due to their impacts on society such as excessive loss of lives, severe economic collapse and endangered ecosystems. Cyclonic storms and low pressure systems causing exceptionally heavy precipitation are few examples of extreme events. Studies have shown increasing trends of heavy rainfall( ≥ 100 mm/day) and very heavy rainfall (≥150 mm/day) over Indian region1. Increased frequency of such severe weather events are suspected to be linked to changes in hydro-cycle due to enhanced green-house effect2. Forewarning of these extreme cases at least 10-12 days in advance can help in reducing socio-economic loss. The forecasting of meteorological parameters with 2-3 weeks lead time is referred as extended range prediction. This sub seasonal forecast range has predictability due to the presence of quasi-periodicities in intra-seasonal scales; and is important for different sectors like agriculture, health, water management etc. A real time forecast system for extended range prediction (ERP) of active and break spells of Indian summer monsoon is developed at Indian Institute of Tropical Meteorology (IITM). This system is now operational at India Meteorological Department (hereafter termed as IITM/IMD ERP). ERP system is a combination of deterministic as well as probabilistic forecast which includes coupled model Climate Forecast System (CFSv2)3 and Atmospheric model Global Forecast System (GFS) at higher (T382) and lower (T126) resolutions with ensembles of perturbed initial conditions (ICs)4. ERP has impressive skill in predicting Monsoon onset & progression, cyclone genesis, Madden-Julian Oscillation, heat and cold waves with a lead time of two weeks5. It has been found in previous studies that ERP system is successful in providing outlooks on impending heavy rainfall events, albeit there are some spatio-temporal errors5,6. Therefore, it is hypothesized in this study that if the model could reasonably predict the large scale features associated with heavy
Load more

Recommended publications
  • Emulation of a Full Suite of Atmospheric Physics
    Emulation of a Full Suite of Atmospheric Physics Parameterizations in NCEP GFS using a Neural Network Alexei Belochitski1;2 and Vladimir Krasnopolsky2 1IMSG, 2NOAA/NWS/NCEP/EMC email: [email protected] 1 Introduction is captured by periodic functions of day and month, and variability of solar energy output is reflected in the solar constant. In addition to the full atmospheric state, NN receives full land-surface model (LSM) state to Machine learning (ML) can be used in parameterization development at least in two different ways: 1) as an capture impact of surface boundary conditions. All NN outputs are increments of dynamical core's prognostic emulation technique for accelerating calculation of parameterizations developed previously, and 2) for devel- variables that the original atmospheric physics package modifies. opment of new \empirical" parameterizations based on reanalysis/observed data or data simulated by high resolution models. An example of the former is the paper by Krasnopolsky et al. (2012) who have developed highly efficient neural network (NN) emulations of both long{ and short-wave radiation parameterizations for 4 Hybrid Coupling of Full Atmospheric Physics NN to GFS a high resolution state-of-the-art short{ to medium-range weather forecasting model. More recently, Gentine Full atmospheric physics NN only et al. (2018) have used a neural network to emulate 2D cloud resolving models (CRMs) embedded into columns updates the state of the atmo- of a \super-parameterized" general circulation model (GCM) in an aqua-planet configuration with prescribed sphere and does not update the invariant zonally symmetric SST, full diurnal cycle, and no annual cycle.
    [Show full text]
  • INTEGRATED FORECAST and MANAGEMENT in NORTHERN CALIFORNIA – INFORM a Demonstration Project
    CPASW March 23, 2006 INTEGRATED FORECAST AND MANAGEMENT IN NORTHERN CALIFORNIA – INFORM A Demonstration Project Present: Eylon Shamir HYDROLOGIC RESEARCH CENTER GEORGIA WATER RESOURCES INSTITUTE Eylon Shamir: [email protected] www.hrc-web.org INFORM: Integrated Forecast and Management in Northern California Hydrologic Research Center & Georgia Water Resources Institute Sponsors: CALFED Bay Delta Authority California Energy Commission National Oceanic and Atmospheric Administration Collaborators: California Department of Water Resources California-Nevada River Forecast Center Sacramento Area Flood Control Agency U.S. Army Corps of Engineers U.S. Bureau of Reclamation Eylon Shamir: [email protected] www.hrc-web.org Vision Statement Increase efficiency of water use in Northern California using climate, hydrologic and decision science Eylon Shamir: [email protected] www.hrc-web.org Goal and Objectives Demonstrate the utility of climate and hydrologic forecasts for water resources management in Northern California Implement integrated forecast-management systems for the Northern California reservoirs using real-time data Perform tests with actual data and with management input Eylon Shamir: [email protected] www.hrc-web.org Major Resevoirs in Nothern California Application Area 41.5 Sacramen to River Capacity of Major Reservoirs 41 (million acre-feet): Pit River Trinit y Trinity - 2.4 Trinity Shasta 40.5 Trinity River Shasta Shasta - 4.5 Oroville - 3.5 Feather River 40 Folsom - 1 39.5 Degrees North Latitude Oroville Oroville N.
    [Show full text]
  • 5Th International Conference on Reanalysis (ICR5)
    5th International Conference on Reanalysis (ICR5) 13–17 November 2017, Rome IMPLEMENTED BY Contents ECMWF | 5th International Conference on Reanalysis (ICR5) 2017 2 Introduction It is our pleasure to welcome the Climate research has benefited over the • Status and plans for future reanalyses • Evaluation of reanalyses reanalysis community at the 5th years from the continuing development Global and regional production, inclusive Comparisons with observations, International Conference on Reanalysis of reanalysis. As reanalysis datasets of all WCRP thematic areas: atmosphere, other types of analysis and models, (ICR5). We are delighted that we are become more diverse (atmosphere, land, ocean and cryosphere – Session inter-comparisons, diagnostics – all able to come together in Rome. ocean and land components), more organisers: Mike Bosilovich (NASA Session organisers: Franco Desiato This five-day international conference complete (moving towards Earth-system GMAO), Shinya Kobayashi (JMA), (ISPRA), Masatomo Fujiwara (Hokkaido is the worldwide leading event for the coupled reanalysis), more detailed, and Simona Masina (CMCC) University), Sonia Seneviratne (ETH), continuing development of reanalysis of longer timespan, community efforts Adrian Simmons (ECMWF) • Observations for reanalyses for climate research, which provides a to evaluate and intercompare them Preparation, organisation in large • Applications of reanalyses comprehensive numerical description become more important. archives, data rescue, reanalysis Generating time-series of Essential
    [Show full text]
  • Evaluation of Cloud Properties in the NOAA/NCEP Global Forecast System Using Multiple Satellite Products
    Clim Dyn DOI 10.1007/s00382-012-1430-0 Evaluation of cloud properties in the NOAA/NCEP global forecast system using multiple satellite products Hyelim Yoo • Zhanqing Li Received: 15 July 2011 / Accepted: 19 June 2012 Ó Springer-Verlag 2012 Abstract Knowledge of cloud properties and their vertical are similar to those from the model, latitudinal variations structure is important for meteorological studies due to their show discrepancies in terms of structure and pattern. The impact on both the Earth’s radiation budget and adiabatic modeled cloud optical depth over storm track region and heating within the atmosphere. The objective of this study is to subtropical region is less than that from the passive sensor and evaluate bulk cloud properties and vertical distribution sim- is overestimated for deep convective clouds. The distributions ulated by the US National Oceanic and Atmospheric of ice water path (IWP) agree better with satellite observations Administration National Centers for Environmental than do liquid water path (LWP) distributions. Discrepancies Prediction Global Forecast System (GFS) using three global in LWP/IWP distributions between observations and the satellite products. Cloud variables evaluated include the model are attributed to differences in cloud water mixing ratio occurrence and fraction of clouds in up to three layers, cloud and mean relative humidity fields, which are major control optical depth, liquid water path, and ice water path. Cloud variables determining the formation of clouds. vertical structure data are retrieved from both active (Cloud- Sat/CALIPSO) and passive sensors and are subsequently Keywords Cloud fraction Á NCEP global forecast system Á compared with GFS model results.
    [Show full text]
  • HIRLAM-B Review September 2016
    External review of the HIRLAM-B programme 2011 – 2015 Peter Lynch Dominique Marbouty Tiziana Paccagnella September 2015 External review of the HIRLAM-B programme, September 2015 Page 2 Table of contents List of recommendations page 4 Summary page 5 Part one Introduction page 7 Background to the review Composition of the Review Team and its organisation Part two The HIRLAM programme page 10 Structure and governance of the programme Staffing resources Achievements of the HIRLAM-B programme Recommendations of the previous review, follow-up Part three Model performance, forecasters’ feedback page 17 Part four Towards an expanded programme page 22 Scope of the single Consortium, core and optional activities Organisation and governance Staffing resources and code-development Data policy and code ownership Branding of the new collaboration and system Part five Conclusion page33 Annexes page 35 Annex 1 – Scope and Term of Reference for HIRLAM-B External Review Annex 2 – Joint HIRLAM-ALADIN Declaration Annex 3 – List of acronyms External review of the HIRLAM-B programme, September 2015 Page 3 Summary The Review Team appointed by the HIRLAM Council examined the governance, resources and achievement of the HIRLAM-B programme. It reviewed the implementation of the recommendations made by the previous review team in 2010 at the end of HIRLAM-A. The HIRLAM cooperation has a long history and is now well organised. As a result the Review Team made few comments concerning HIRLAM in isolation from the wider collaboration with ALADIN. The review team also met forecasters in most of HIRLAM centers. Benefits of using regional high resolution model for short range forecasts, as compared to ECMWF global forecasts, is no longer questioned.
    [Show full text]
  • NAEFS Status and Future Plan
    NAEFS Status and Future Plan Yuejian Zhu Ensemble team leader Environmental Modeling Center NCEP/NWS/NOAA Presentation for International S2S conference February 14 2014 Warnings Warnings Coordination Coordination Assessments Forecasts Guidance Forecasts Guidance Watches Watches Outlook Outlook Threats & & Alert Alert Products Forecast Lead Time Minutes Minutes Life & Property NOAA Hours Hours Aviation Days Days Spanning 1 Maritime 1 Week Week Seamless 2 Space Operations 2 Week Week Fire Weather Fire Weather Months Months Emergency Mgmt Climate/Weather/Water Commerce Suite Benefits Seasons Seasons Energy Planning Hydropower of Reservoir Control Forecast O G O O H C U D R C R D Y L T P S Agriculture I Years Years Recreation Weather Prediction Climate Prediction Ecosystem Products Health Products Uncertainty ForecastForecast Uncertainty Forecast Uncertainty Environment Warnings Warnings Coordination Coordination Assessments Forecasts Guidance Forecasts Guidance Watches Watches Outlook Outlook Threats & & Alert Alert Forecast Lead Time Products Service CenterPerspective Minutes Minutes Life & Property NOAA Hours Hours Aviation Days Days 1 1 Maritime Spanning Week Week Seamless SPC 2 Space Operations 2 Week Week Fire Weather HPC Fire Weather Months Months Emergency Mgmt AWC Climate/Weather Climate OPC Commerce Suite Linkage Benefits Seasons Seasons SWPC Energy Planning CPC TPC Hydropower of and Reservoir Control Forecast Reservoir Control Fire WeatherOutlooks toDay8 Week 2HazardsAssessment Winter WeatherDeskDays1-3 Seasonal Predictions NDFD,
    [Show full text]
  • CFS Reanalysis Design
    Design of the 30-year NCEP CFSRR T382L64 Global Reanalysis and T126L64 Seasonal Reforecast Project (1979-2009) Suru Saha and Hua-Lu Pan, EMC/NCEP With Input from Stephen Lord, Mark Iredell, Shrinivas Moorthi, David Behringer, Ken Mitchell, Bob Kistler, Jack Woollen, Huug van den Dool, Catherine Thiaw and others Operational Daily CFS • 2 9-month coupled forecasts at 0000GMT • (1) GR2 (d-3) atmospheric initial condition • (2) Avg [GR2(d-3),GR2(d-4)] • T62L28S2 – MOM3 • GODAS (d-7) oceanic initial condition 2004 CFS Reforecasts The CFS includes a comprehensive set of retrospective runs that are used to calibrate and evaluate the skill of its forecasts. Each run is a full 9-month integration. The retrospective period covers all 12 calendar months in the 24 years from 1981 to 2004. Runs are initiated from 15 initial conditions that span each month, amounting to a total of 4320 runs. Since each run is a 9-month integration, the CFS was run for an equivalent of 3240 yr! An upgrade to the coupled atmosphere-ocean-seaice-land NCEP Climate Forecast System (CFS) is being planned for Jan 2010. Involves improvements to all data assimilation components of the CFS: • the atmosphere with the new NCEP Gridded Statistical Interpolation Scheme (GSI) and major improvements to the physics and dynamics of operational NCEP Global Forecast System (GFS) • the ocean and ice with the NCEP Global Ocean Data Assimilation System, (GODAS) and a new GFDL MOM4 Ocean Model • the land with the NCEP Global Land Data Assimilation System, (GLDAS) and a new NCEP Noah Land model For a new Climate Forecast System (CFS) implementation Two essential components: A new Reanalysis of the atmosphere, ocean, sea-ice and land over the 31-year period (1979-2009) is required to provide consistent initial conditions for: A complete Reforecast of the new CFS over the 28-year period (1982-2009), in order to provide stable calibration and skill estimates of the new system, for operational seasonal prediction at NCEP There are 4 differences with the earlier two NCEP Global Reanalysis efforts: 1.
    [Show full text]
  • Performance of a Very High-Resolution Global Forecast System Model (GFS T1534) at 12.5 Km Over the Indian Region During the 2016–2017 Monsoon Seasons
    J. Earth Syst. Sci. (2019) 128:155 c Indian Academy of Sciences https://doi.org/10.1007/s12040-019-1186-6 Performance of a very high-resolution global forecast system model (GFS T1534) at 12.5 km over the Indian region during the 2016–2017 monsoon seasons P Mukhopadhyay1,* ,VSPrasad2, R Phani Murali Krishna1, Medha Deshpande1, Malay Ganai1, Snehlata Tirkey1, Sahadat Sarkar1, Tanmoy Goswami1, C J Johny2, Kumar Roy1, M Mahakur1, V R Durai3 and M Rajeevan4 1 Indian Institute of Tropical Meteorology, Dr Homi Bhabha Road, Pashan, Pune 411 008, India. 2National Centre for Medium Range Weather Forecasting, A-50, Sector-62, Noida, UP, India. 3India Meteorological Department, Mausam Bhavan, Lodi Road, New Delhi 110 003, India. 4Ministry of Earth Science, Government of India, Prithvi Bhavan, New Delhi 110 003, India. *Corresponding author. e-mail: [email protected] [email protected] MS received 1 September 2018; revised 17 March 2019; accepted 20 March 2019; published online 4 June 2019 A global forecast system model at a horizontal resolution of T1534 (∼12.5 km) has been evaluated for the monsoon seasons of 2016 and 2017 over the Indian region. It is for the first time that such a high-resolution global model is being run operationally for monsoon weather forecast. A detailed validation of the model therefore is essential. The validation of mean monsoon rainfall for the season and individual months indicates a tendency for wet bias over the land region in all the forecast lead time. The probability distribution of forecast rainfall shows an overestimation (underestimation) of rainfall for the lighter (heavy) categories.
    [Show full text]
  • 5Th International Conference on Reanalysis (ICR5)
    5th International Conference on Reanalysis (ICR5) 13–17 November 2017, Rome IMPLEMENTED BY Contents ECMWF | 5th International Conference on Reanalysis (ICR5) 2017 2 Introduction It is our pleasure to welcome the Climate research has benefited over the • Status and plans for future reanalyses • Evaluation of reanalyses reanalysis community at the 5th years from the continuing development Global and regional production, inclusive Comparisons with observations, other International Conference on Reanalysis of reanalysis. As reanalysis datasets of all WCRP thematic areas: atmosphere, types of analysis and models, inter- (ICR5). We are delighted that we are become more diverse (atmosphere, land, ocean and cryosphere – Session comparisons, diagnostics – Session all able to come together in Rome. ocean and land components), more organizers: Mike Bosilovich (NASA organizers: Franco Desiato (ISPRA), This five-day international conference complete (moving towards Earth-system GMAO), Shinya Kobayashi (JMA), Masatomo Fujiwara (Hokkaido is the worldwide leading event for the coupled reanalysis), more detailed, and Simona Masina (CMCC) University), Sonia Seneviratne (ETH), continuing development of reanalysis of longer timespan, community efforts Adrian Simmons (ECMWF) • Observations for reanalyses for climate research, which provides a to evaluate and intercompare them Preparation, organization in large • Applications of reanalyses comprehensive numerical description become more important. archives, data rescue, reanalysis Generating time-series of
    [Show full text]
  • Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation
    Cite this entry as: Pu Z., Kalnay E. (2018) Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation. In: Duan Q., Pappenberger F., Thielen J., Wood A., Cloke H., Schaake J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg Numerical Weather Prediction Basics: Models, Numerical Methods, and Data Assimilation Zhaoxia Pu and Eugenia Kalnay Contents 1 Introduction: Basic Concept and Historical Overview .. .................................... 2 2 NWP Models and Numerical Methods ...................................................... 4 2.1 Basic Equations ......................................................................... 4 2.2 Numerical Frameworks of NWP Model ............................................... 7 2.3 Global and Regional Models ........................................................... 12 2.4 Physical Parameterizations ............................................................. 13 2.5 Land-Surface and Ocean Models and Coupled Numerical Models in NWP . 18 3 Data Assimilation ............................................................................. 22 3.1 Least Squares Theory ................................................................... 22 3.2 Assimilation Methods .................................................................. 24 4 Recent Developments and Challenges ....................................................... 28 References ........................................................................................ 29 Abstract Numerical
    [Show full text]
  • Implementation of Global Ensemble Forecast System (GEFS) at 12Km Resolution
    ISSN 0252-1075 Contribution from IITM Technical Report No.TR-06 ESSO/IITM/MM/TR/02(2020)/200 Implementation of Global Ensemble Forecast System (GEFS) at 12km Resolution Medha Deshpande, C.J. Johny, Radhika Kanase, Snehlata Tirkey, Sahadat Sarkar, Tanmoy Goswami, Kumar Roy, Malay Ganai, R. Phani Murali Krishna, V.S. Prasad, P. Mukhopadhyay , V.R. Durai, Ravi S. Nanjundiah and M. Rajeevan Indian Institute of Tropical Meteorology (IITM) Ministry of Earth Sciences (MoES) PUNE, INDIA http://www.tropmet.res.in/ ISSN 0252-1075 Contribution from IITM Technical Report No.TR-06 ESSO/IITM/MM/TR/02(2020)/200 Implementation of Global Ensemble Forecast System (GEFS) at 12km Resolution Medha Deshpande1, C.J. Johny2, RadhikaKanase1, Snehlata Tirkey1, Sahadat Sarkar1, Tanmoy Goswami1, Kumar Roy1, Malay Ganai1, R.Phani Murali Krishna1, V.S. Prasad2, P. Mukhopadhyay1 , V.R. Durai3, Ravi S. Nanjundiah1, 4and M. Rajeevan5 1 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Dr.Homi Bhabha Road, Pashan, Pune- 411008 2 National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, A-50, Sector-62, NOIDA, UP 3 India Meteorological Department, Ministry of Earth Sciences,MausamBhavan, Lodi Road, New Delhi 4 Center for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru - 560012 5 Ministry of Earth Science, Government of India, PrithviBhavan, New Delhi *Corresponding Author: Dr. P. Mukhopadhyay Indian Institute of Tropical Meteorology, Dr.Homi Bhabha Road, Pashan, Pune-411008, India. E-mail: [email protected],
    [Show full text]
  • The Reanalysis for the Global Ensemble Forecast System, Version 12
    1 The Reanalysis for the Global Ensemble Forecast System, version 12. 2 Thomas M. Hamill1, Jeffrey S. Whitaker1, Anna Shlyaeva2, Gary Bates3, Sherrie Fredrick,3 Philip 3 Pegion3, Eric Sinsky6, Yuejian Zhu4, Vijay Tallapragada4, Hong Guan5, Xiaqiong Zhou4, and 4 Jack Woollen4 5 1 NOAA Physical Sciences Laboratory, Boulder, CO 6 2 Joint Center for Satellite Data Assimilation, University Corporation for Atmospheric Research, 7 Boulder CO 8 3Cooperative Institute for Research in the Environmental Sciences, University of Colorado, 9 Boulder CO 10 4National Weather Service, Environmental ModelinG Center, ColleGe Park, MD 11 5 SRG at NOAA/NWS/NCEP/EMC, ColleGe Park, MD 12 6 IMSG at NOAA/NWS/NCEP/EMC, ColleGe Park, MD 13 Submitted to Monthly Weather Review (presumably) 14 9 February 2021 15 Corresponding author: 16 Dr. Thomas M. Hamill, 17 NOAA ESRL/PSL, R/PSL1 18 325 Broadway, Boulder CO 80305 19 [email protected] 20 phone: +1 (303) 497-3060 21 telefax: +1 (303) 497-6449 22 telework: +1 (303) 516-0340 23 1 24 ABSTRACT 25 NOAA has created a global reanalysis data set, intended primarily for initialization of 26 reforecasts for its Global Ensemble Forecast System, version 12 (GEFSv12), which 27 provides ensemble forecasts out to +35 days lead time. The reanalysis covers the period 28 2000-2019. It assimilates most of the observations that were assimilated into the 29 operational data assimilation system used for initializing global predictions. These include a 30 variety of conventional data, infrared and microwave radiances, Global Positioning System 31 radio occultations, and more. The reanalysis quality is generally superior to that from 32 NOAA’s previous-generation Climate Forecast System Reanalysis (CFSR), demonstrated in 33 the fit of short-term forecasts to the observations and in the skill of 5-day deterministic 34 forecasts initialized from CFSR vs.
    [Show full text]