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Future Enhancements to NCEP Climate Forecast System

Overview of planned developments for the NCEP Climate Forecast System, including new components, implementation timeline, and ongoing testing procedures. Emphasis on enhancing forecast accuracy and operational efficiency.

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Future Enhancements to NCEP Climate Forecast System

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  1. An Overview of New and Future Developments with the NCEP Climate Forecast System SURANJANA SAHA and HUA-LU PAN Environmental Modeling Center NCEP/NWS/NOAA 21st Annual Climate Diagnostics and Prediction Workshop Boulder, CO 23 October 2006

  2. The NCEP Climate Forecast was made operational in August 2004 • Currently, two fully-coupled nine-month forecasts are made every day • The present CFS operational system is frozen • Development work is underway at EMC to improve the CFS • We anticipate a new CFS implementation will take in January 2010

  3. For a new CFS implementation Two main components: CFS Reanalysis (1979-2007) T254L64 CFS Retrospective Forecasts (1981-2007) T126L64

  4. For a new CFS implementation (contd) • Analysis Systems : GSI, GODAS, GLDAS • Atmospheric Model : GFS • Ocean Model : MOM4 (1/40 at the Equator, 1/20 at globally beyond 100N and 100S • SEA ICE MODEL

  5. Proposed Time-Line for the next CFS implementation in January 2010 October 2006 In anticipation of the operational implementation of the sigma-p version of the GSI in February 2007, start testing this version for a few particular years in the 1979-2006 period, where changes in satellites will lead to new corrections in radiance measurements. Run the GSI for a few random years and save the fluxes. (Saha, Treadon, van Delst, Derber, Kliest, Woollen, Pan, Kistler, White)

  6. Proposed Time-Line for the next CFS implementation in January 2010 June 2007 In anticipation of the ESMF version of the coupler and MOM4, start testing the new GODAS (with MOM4 and sea ice) and GLDAS (with Noah Land model and observed precipitation) with fluxes obtained from atmospheric GSI analyses of the few random years. (Behringer, Mitchell, Sheinin, Wang, Wu, Nadiga, Stokes, Pan, Moorthi, Grumbine, Saha, Meng, Wei)

  7. Proposed Time-Line for the next CFS implementation in January 2010 August 2007 In anticipation of the operational implementation of the next GFS, start pilot studies with the first “prototype” of the fully coupled CFS reanalysis, which will include the GSI (with new GFS), GODAS and GLDAS. (Saha, Pan, Iredell, Moorthi, Derber, Behringer, Mitchell, Meng, Yang, Treadon, Sheinin, Woollen, Kistler)

  8. Proposed Time-Line for the next CFS implementation in January 2010 January 2008 Begin Production and Evaluation of the CFS Reanalysis for the full period from 1979 to 2007 (29 years) Reanalysis Team: Saha, Thiaw, Wang, Nadiga, Wu, Lu. Evaluation Team: EMC and CPC Personnel

  9. Proposed Time-Line for the next CFS implementation in January 2010 July 2008 Begin running CFS Retrospective Forecasts for 2 initial months: October and April, and evaluate the monthly forecasts as well as the seasonal Lead-1 DJF and JJA forecasts. Hindcast Team: Saha, Thiaw, Wang, Nadiga, Wu, Lu Evaluation Team: EMC and CPC Personnel

  10. Proposed Time-Line for the next CFS implementation in January 2010 January 2009 Operational Implementation of the CFS Reforecast Project (for the rest of the 10 calendar months) in the proposed slot of REFCST in the NCEP production suite. (NCO personnel, Saha, Moorthi, Pan and Thiaw)

  11. Proposed Time-Line for the next CFS implementation in January 2010 November 2009 Begin computing calibration statistics for CFS daily, monthly and seasonal forecasts. Prepare CFS Reanalysis and Retrospective Forecast data for public distribution. (Saha, Thiaw, Pan and CPC personnel) January 2010: Operational implementation of the next CFS monthly and seasonal forecast

  12. CURRENT OPERATIONAL VERSION OF THE GFS (USED FOR WEATHER PREDICTION) UPGRADES TO THE CFS VERSION • NOAH Land Model : 4 soil levels. Improved treatment of snow and frozen soil and glacial physics • Sea Ice Model : Prediction of ice concentration and ice fraction • Sub grid scale mountain blocking • Reduced vertical diffusion

  13. CURRENT OPERATIONAL VERSION OF THE GFS (USED FOR WEATHER PREDICTION) UPGRADES TO THE CFS VERSION RRTM long wave radiation (clouds are maximum random, which leads to reduced cloud cover) ESMF Version NRL Based Ozone Climatology for Production and destruction

  14. Ongoing GFS Developmental work • Test the generalized vertical coordinate system with a combination of sigma, theta and pressure levels • Test new convection scheme (RAS) • Test upgrades to operational convection scheme (SAS) • Test Moorthi-Ferrier microphysics package for large scale condensation • Test improved boundary layer physics • Test convectively forced gravity wave drag

  15. Other Ongoing CFS Developmental work • Test the new Grid Point Statistical Interpolation Scheme (GSI) • Test historical settings of CO2, aerosol and solar cycle in the CFS • Test the new MPI level coupling of GFS to MOM4

  16. GSI – NCEP’s Next Generation Analysis System John Derber1,Lidia Cucurull2, Daryl Kleist3, Xu Li3, Curtis Marshall3, Dave Parrish1, Manuel Pondeca3, Jim Purser3, Russ Treadon1, Paul vanDelst2, Wan-Shu Wu1 1 NOAA/NWS/NCEP/EMC, 2 UCAR, 3 SAIC Courtesy : Russ Treadon, EMC

  17. Assimilated data types • All data types currently assimilated by SSI may also be assimilated by GSI • Sondes, ship reports, surface stations, aircraft data, profilers, etc • Cloud drift and water vapor winds • TOVS, ATOVS, AQUA, and GOES sounder brightness temperatures • SBUV ozone profiles and total ozone • SSM/I and QuikScat surface winds • SSM/I and TMI rain rates Courtesy : Russ Treadon, EMC

  18. GSI development: Analysis variables • SST analysis • Physical retrieval from AVHRR Tb data • Option to add / assimilate in-situ SST data rms Slight, but consistent reduction in rms and bias fits to independent buoy SST data bias Courtesy : Russ Treadon, EMC

  19. GSI development: New radiance data • Aqua AIRS/AMSU-A • Operational as of 12 UTC, 31 May 2005 • Future improvements • Examine all FOVs to determine warmest spots • Use MODIS data for cloud detection • SSM/I • Use of Tb data reduces model moisture bias • Forward model for emissivity includes effects of surface winds • Assimilation of SSM/I Tb data can affect surface winds • Could (should) turn off assimilate of SSM/I wind product Courtesy : Russ Treadon, EMC

  20. GSI development: New radiance data • NOAA – N (18) Summer 2005 • HIRS, AMSU-A, MHS • Code ready and waiting for data to evaluate • SSM/IS • QC and bias correction difficulties because FOVs not collocated • AMSR-E (NASA) • Beginning tests with radiative transfer model • AVHRR and GOES imagery • Testing underway Courtesy : Russ Treadon, EMC

  21. GSI development: GPS radio occultation • Preparation for COSMIC well advanced • Code for assimilating local refractivities done and being tested • Adding code to handle local bending angle underway • QC issues • Tracking errors • Caused by complicated refractivity structure in moist lower troposphere • Super-refraction • Occurs on sharp top of moist PBL Courtesy : Russ Treadon, EMC

  22. GSI development: Doppler Radar • Code being developed to handle radar radial velocities • Currently working on data processing, quality control, and superobs issues • Longer term project is to make use of radar reflectivities • Currently working on quality control issues • Bird migration, ground clutter, anomalous propagation, etc Courtesy : Russ Treadon, EMC

  23. GSI development: CRTM development • Proto-type CRTM with modular design • Simplifies user interaction with code • Permits easier evaluation of various algorithms • Soon will include • Algorithms to handle scattering and absorption from clouds for microwave channels Courtesy : Russ Treadon, EMC

  24. Anisotropic vs Isotropic Error Covariances Observation influence extends into mountains indiscriminately Error Correlations Plotted Over Utah Topography Observation influence restricted to areas of similar elevation Courtesy : Russ Treadon, EMC

  25. INTRODUCTION TO POSTERS

  26. Subseasonal skill Forecast with AR(1) precipitation No seasonal skill Initialization issues? Augustin Vintzileos and Wassila Thiaw Examples of Subseasonal Forecasting with the CFS: The Sahel Forecast skill for Cumulative Precipitation over the Sahel

  27. Examples of Subseasonal Forecasting with the CFS: The Sahel Observed precipitation over the western Sahel for 2006 (Xie) Real Time 30-day Forecasts: good vs. bad prediction 2006 Precipitation (blue) Mean annual Precipitation (red) Observed Forecast Observed The 2006 monsoon period over the Sahel started drier than average. Circa July 16th, there was a sudden jump towards persistent wetter conditions and finally the 2006 monsoon season was a good one. We discuss the ability of the CFS to forecast these conditions at subseasonal lead times on the Poster Session P1.12 on Tuesday. Forecast

  28. CFS Retrospective Forecast Daily Climatology in the EMC/NCEP NOMAD public server Åke Johansson, Catherine Thiaw and Suranjana Saha, Environmental Modeling Center, NCEP/NWS/NOAA Methodology A Fourier series is fitted to the raw 24-yr average data y(tj) which are available at 180 irregularily spaced days in a 365 day year [ T = 365 days ]. The method of least squares is used, i.e., minimization of gives the Fourier Coefficients. The climatology is defined by considering 4 harmonics, i.e., in accordance with NCEP practice, Schemm et al. 1998. There is a risk of: Overfitting – Noise included in the climatology Underfitting – Not all of the true climatology is included in the calculated climatology

  29. CFS Retrospective Forecast Daily Climatology in the EMC/NCEP NOMAD public server Åke Johansson, Catherine Thiaw and Suranjana Saha, Environmental Modeling Center, NCEP/NWS/NOAA MEAN SD 2m Temperature Washington 1 month lead Winter-time Warming of ~ 2-3 ºC Winter-time Variability reduced by 15% 8 month lead

  30. NCEP REGIONAL CLIMATE FORECAST BY NCEP RSM MODEL • Regional climate models have been used in dynamic downscaling for the seasonal climate forecasts and regional climate change assessment in the past decade and demonstrated capability in regenerate seasonal mean climate and inter-annual variability. • NCEP Regional Spectral Model (RSM) nested in Global Forecast System (GFS) has been implemented for CONUS regional climate prediction. A 20 year 3 member ensemble hindcast using sst forecasted from CFS was conducted to explore the capability of NCEP RSM model on seasonal forecast. Jun Wang and Henry Juang, P1.19 9:20-10:50AM, Tuesday, 10/24/2006

  31. Jun Wang and Henry Juang, P1.19 9:20-10:50AM, Tuesday, 10/24/2006

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