CFS Reanalysis Design

Total Page:16

File Type:pdf, Size:1020Kb

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. Much higher horizontal and vertical resolution (T382L64) of the atmosphere (earlier efforts were made with T62L28 resolution) 2. The guess forecast will be generated from a coupled atmosphere – ocean – seaice - land system 3. Radiance measurements from the historical satellites will be assimilated in this Reanalysis 4. 4 Soil layers – Noah Land Data Assimilation system Progress GR1/GR2 2007 GDAS (CFSRR) Vertical structure sigma sigma-pressure Spectral resolution T62 T382 Horizontal resolution 210 km 35 km Vertical layers 28 levels 64 levels top 3 hPa .266 hPa Layers above 100 hPa 7 24 Layers below 850 hPa 6 13 Lowest layer 40 m 20 m Soil Layers 2 4 3D-var SSI GSI NESDIS data retrievals radiances Vertical coordinate comparison across North America 9New format and improved SBUV ozone data 9Calibrated 3 ch. SSU radiance data (corrected for cell pressure leaks) CFSRR at NCEP Climate Forecast System GDAS 6hr Atmospheric Model GSI GFS (2007) T382 L64 S4 24hr 6hr LDAS Land Model Ice Model Ice Anl Ocean Model MOMv4 6hr GODAS fully global 3DVAR 1/2ox1/2o (1/4o in tropics) 40 levels CFSRR BOARD MEETING PRESENTATIONS NCEP 2007 November 7-8 http://cfs.ncep.noaa.gov/cfsreanl/PRESENTATION/20071107_board_meeting.html • Overview of the CFSRR Suru Saha, EMC NCEP • The Atmospheric Model Shrinivas Moorthi, EMC NCEP • The Atmospheric Data Assimilation Russ Treadon, EMC NCEP • GPS Radio-Occultation Data Lidia Cucurull, EMC NCEP • The Ocean Model Jiande Wang, EMC NCEP • The Ocean Data Assimilation Dave Behringer, EMC NCEP • The Sea Ice Model Xingren Wu, EMC NCEP • The coupler Jun Wang, EMC NCEP • The Sea Ice Analysis Bob Grumbine, EMC NCEP • The Sea Surface Temperature Analysis Diane Stokes, EMC NCEP • The Snow Analysis George Gayno, EMC NCEP • The Land Surface Model and Land Surface Data Assimilation Ken Mitchell, EMC NCEP • The Data Jack Woollen, EMC NCEP • Tropical Cyclones Bob Kistler, EMC NCEP • Ozone Craig Long, CPC NCEP • SSU Mark Liu, NESDIS • MSU Cheng-Zhi Zou , NESDIS • Satellite Bias Correction Haixia Liu , EMC NCEP • The Results Hua-Lu Pan , EMC NCEP • EMC Monitoring Cathy Thiaw , EMC NCEP • A review of the CPC's Monitoring Efforts for the CFS Reanalysis Muthu Chelliah , EMC NCEP The changing number and characteristics of observations ONE DAY OF REANALYSIS 12Z GSI 18Z GSI 0Z GSI 6Z GSI 0Z GLDAS 12Z GODAS 18Z GODAS 0Z GODAS 6Z GODAS 9-hr coupled T382L64 forecast guess (GFS + MOM4 + Noah) 1 Jan 0Z 2 Jan 0 Z 3 Jan 0Z 4 Jan 0Z 5 Jan 0Z 2-day T126L64 coupled forecast ( GFS + MOM4 + Noah ) 4 Simultaneous Streams • Jan 1979 – Oct 1989 11 years • Apr 1989 – Oct 1998 10 years • Apr 1998 – Oct 2004 7 years • Apr 2004 – Dec 2009 6 years 6 month overlap for ocean and land spin ups Reanalysis to cover 31 years (1979-2009) + 21 overlap months Reforecasts to cover 28 years (Jan 1982 – Dec 2009) SOME NOTES PROPOSED TIME LINE FOR COMPLETION OF CFSRR •January to December 2008: Begin Production and Evaluation of the CFS Reanalysis for the full period from 1979 to 2008 (30 years) •January to December 2008: Begin running CFS Retrospective Forecasts for 2 initial months: October and April, and evaluate the monthly forecasts as well as the seasonal winter (Lead-1 DJF) and summer (Lead-1 JJA) forecasts. •January to October 2009: Continue running the CFS Reforecasts (for the rest of the 10 calendar months) •November 2009: Begin computing calibration statistics for CFS daily, monthly and seasonal forecasts. January 2010: Operational implementation of the next CFS monthly and seasonal forecast suite. NOAA Collaborators • NOAA/CPC is actively involved in the monitoring of the Reanalysis • NOAA/NCDC is actively involved in the archival of the Reanalysis. DATA ARCHIVAL • NCDC/NOAA will archive and distribute both the CFS Reanalysis and Reforecasts, through their NOMADS system. • Targets: • CFS Reanalysis - mid 2009 • Reforecast data - mid 2010. Data to be archived and available from NCDC CFS Hi Res IC (6-hourly) T382L64 Atmosphere + .5 degree Ocean CFS Lo Res IC (6-hourly) T126L64 Atmosphere + 1 degree Ocean Full Ingest Data (6-hourly) All input data for the re-analysis pgbh 0.5 x 05 (Hourly) 37 standard pressure level atmosphere products flxf T382 Gaussian (Hourly) surface and radiative fluxes on model grid ocnh 0.5 x 0.5 (Hourly) 40 standard depth level ocean products diabf 1.0 x 1.0 (Hourly) 37 standard pressure level diabatic heating rates ipvh 0.5 x 0.5 (Hourly) 16 standard isentropic level atmosphere products Status • New format and improved SBUV ozone data • Calibrated SSU radiance data (corrected for cell pressure leaks) has been tested for impact • High resolution SST analysis has been tested for impact • Satellite radiance bias correction estimated for each new satellite with a 3-month run of the full coupled GDAS at T382L64. Status (contd) • A mini T62L64 atmospheric-only Reanalysis is completed to pass through all atmospheric data from 1979 – 2007 • We had to adjust the streams as the later year data amount is much larger than earlier ones • Results of 5-day forecasts made from every 0Z cycle are encouraging. Thank you…. Email : [email protected] Website : http://cfs.ncep.noaa.gov Mingyue Chen and UnifiedUnified DailyDaily GaugeGauge DataData Pingping Xie Dense gauge networks from special CPC collections over US, Mexico, and S. America; GTS gauge network elsewhere Daily reports available from ~17,000 stations Precip blending 28-year CFS re-forecast archive by NCDC (1982-2009) 6-hourly Pgb and Flx * 37 standard pressure level atmosphere products 6-hourly Ocn * 40 standard depth level ocean products 6-hourly Ipv * 16 standard isentropic level atmosphere products • * 1.0 x 1.0 for first 6 months of forecast ; 2.5 x 2.5 for next 6 months of forecast 37 Pressure (hPa) Levels : pgb, ipv, egy and diab (atmosphere) 1000 975 950 925 900 875 850 825 800 775 750 700 650 600 550 500 450 400 350 300 250 225 200 175 150 125 100 70 50 30 20 10 7 5 3 2 1 40 Levels (depth in meters) in ocn (ocean) 4478 3972 3483 3016 2579 2174 1807 1479 1193 949 747 584 459 366 303 262 238 225 215 205 195 185 175 165 155 145 135 125 115105 95 85 75 65 55 45 35 25 15 5 16 Isentropic (K) Levels 270 280 290 300 310 320 330 350 400 450 550 650 850 1000 1250 1500 A Brief History of Conventional Data Preparation for Reanalysis at NCEP •TEMP PILOT AIREP PIREP ACARS SATOB SYNOP MARINE BOGUS •R1/R2 merged data sources from NCAR, NCDC, NCEP, JMA, and ECMWF •R1/R2 merged qc’d datasets were packaged and delivered to the JMA-25 project •R1/R2 datasets were reprocessed and enhanced by NCEP/NCAR for ERA-40 •NCEP obtained the ECMWF MARS archive of conventional data, 1979-1994 •NCAR improved versions of key datasets, ie, COADS, ON29/RAOBS, ON124 •R1/R2 and new datasets were merged for the North American Regional Reanalysis •The latest versions of all source datasets were merged for the MERRA project •Much effort was made by MERRA people to find data gaps and other problems •CFSRR will use conventional observations prepared for MERRA, 1978-2000 •CFSRR will use NCEP/NCO operational archives to overlay and complete 1997-2007 SNOW • Current GFS OPS updates snow pack once per day (00z). – SNO2MDL program maps NESDIS IMS and AFWA snow analysis data to physics grid. – Output from SNO2MDL ingested by SFC- CYCLE program. • Snow updated on model grid via direct replacement. • CFSRR will follow similar procedure. NESDIS IMS Data • IMS – Interactive Multisensor Snow and Ice Mapping System.
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]
  • 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]
  • The Temporal Cascade Structure of Reanalyses and Global Circulation Models
    The temporal cascade structure of reanalyses and Global Circulation models J. Stolle1, S. Lovejoy1, D. Schertzer2,3 (1) Physics, McGill University, 3600 University St., Montreal, Que. H3A 2T8, Canada (2) CEREVE, Université Paris Est, France (3) Météo France, 1 Quai Branly, Paris 75005, France Correspondence to: J. Stolle ([email protected]), S. Lovejoy ([email protected]) Abstract The spatial stochastic structure of deterministic models of the atmosphere has recently been shown to be well modelled by multiplicative cascade processes; in this paper we extend this to the time domain. Using data from reanalyses (ERA 40) and two meteorological models (GFS, GEM), we investigate the temporal cascade structures of the temperature, humidity, and zonal wind at various altitudes, latitudes, and forecast times. First, we estimate turbulent fluxes from the absolute second order time differences, showing that the fluxes are generally very close to those estimated in space at the model dissipation scale; thus validating the flux estimates. We then show that temporal cascades with outer scales typically in the range 5-20 days can accurately account for the statistical properties over the range from six hours up to 3-5 days. We quantify the (typically small) differences in the cascades from model to model, as functions of latitude, altitude and forecast time. By normalizing the moments by the theoretical predictions for universal multifractals, we investigate the “Levy collapse” of the statistics somewhat beyond the outer cascade limit into the low frequency weather regime. Although the due to finite size effects and the small outer temporal scale, the temporal scaling range is narrow (12-24 hours up to 2-5 days), we compare the spatial and temporal statistics by constructing space-time (Stommel) diagrammes, finding space-time transformation velocities of 450 - 1000 km/day, comparable to those predicted based on the solar energy flux driving the system.
    [Show full text]