1 / 28

David H. Bromwich, Lesheng Bai and Keith M. Hines

PROTOTYPE OF THE ARCTIC SYSTEM REANALYSIS. David H. Bromwich, Lesheng Bai and Keith M. Hines Polar Meteorology Group, Byrd Polar Research Center The Ohio State University Plus Many Collaborators. Supported by NSF, NOAA, and DOE. Outline. • The Arctic System Reanalysis (ASR)

eithne
Download Presentation

David H. Bromwich, Lesheng Bai and Keith M. Hines

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. PROTOTYPE OF THE ARCTIC SYSTEM REANALYSIS David H. Bromwich, Lesheng Bai and Keith M. Hines Polar Meteorology Group, Byrd Polar Research Center The Ohio State University Plus Many Collaborators Supported by NSF, NOAA, and DOE

  2. Outline • The Arctic System Reanalysis (ASR) • Polar WRF • Atmospheric Data Assimilation • Noah Land Surface Data Assimilation • Validation • ASR Results

  3. Arctic System Reanalysis Motivation 1. Rapid climate change is happening in the Arctic. A comprehensive picture of the climate interactions is needed. 2. Global reanalyses encounter many problems at high latitudes. The ASR is using the best available depiction of Arctic processes at much higher spatial resolution. 3. A system-oriented approach provides community focus with the atmosphere, land surface and sea ice communities. 4. The ASR provides a convenient synthesis of Arctic field programs (SHEBA, LAII/ATLAS, ARM, ...).

  4. ASR Outline A physically-consistent integration of Arctic and other Northern Hemisphere data High resolution in space (10 km) and time (3 hours) Begin with years 2000-2010 (Earth Observing System) Participants: Ohio State University - Byrd Polar Research Center (BPRC) - and Ohio Supercomputer Center (OSC) National Center Atmospheric Research (NCAR) University of Colorado-Boulder University of Illinois at Urbana-Champaign

  5. Polar WRF (Version 3.2.1) The key modifications for Polar WRF are: Optimal turbulence (boundary layer) parameterization Implementation of fractional sea ice as well as variable thickness of sea ice and snow cover on sea ice in the Noah LSM Improved treatment of heat transfer for ice sheets and revised surface energy balance calculation in the Noah LSM Model evaluations through Polar WRF simulations over Greenland, the Arctic Ocean (SHEBA site), and Alaska have been performed. Polar WRF is used by forecasters as part of the National Science Foundation sponsored Antarctic Mesoscale Prediction System. Polar WRF is used by ASR.

  6. Atmospheric Data Assimilation Global Fields ERA-Interim ASR WRF-3DVar / WRF Modeling System WRF-3DVar Analysis performed over one domain in a 3-h interval xlbc Background Preprocessing (WPS , real) xb xf Cycled Background PREPBUFR Radiance BUFR Other spec. data 3h frequency WRF-3DVar yo , R xa Update Lateral & Lower BCs (UPDATE_BC) Nested WRF 3h Forecast HRLDAS output Verification & Visualization Background Error (gen_be) B0 Seasonal dependent

  7. Atmospheric Data Assimilation Observational Datafor ASR • PREPBUFR from Jack Woollen • (including synop, metar, ship, buoy, qscat, • sound, airep, profiler, pilot, satob, ssmi_retrieval_sea_surface_wind_speed, ssmi_retrieval_pw, gpspw) • Radiances also from JW • different sensors (amsua, amsub, mhs, • hirs3, hirs4) in separate BUFR files • GPS • GPSRO, GPSIPW NOAA (HIRS, AMSU) Aqua (AMSU, AIRS)

  8. High-Resolution Land Data Assimilation System (HRLDAS) for ASR 􀂄 Blending atmospheric and land-surface observations with the Noah land surface model. E.g., MODIS surface albedo, snow cover, and vegetation characteristics. To provide land state variables for driving the coupled Polar WRF/ Noah modeling system – Soil moisture (liquid and solid phase) – Soil temperature – Snow water equivalent and depth – Canopy water content – Vegetation characteristics To provide long-term evolution of the above variables plus surface hydrological cycle (runoff, evaporation) and energy cycle (surface heat flux, ground heat flux, upward long-wave radiation)

  9. Domain for ASR

  10. Validation • Polar WRF • Satellite Radiance • Microphysics and Cloud Fraction • Diurnal Pressure over Mountain Regions

  11. ASR Data Assimilation Result Polar WRF corrected 2m-temperature warm bias over Greenland Polar WRF AVN AVN AVN ERA AVN ERA-Interm Data assimilation 2m-temperature (oC) using Polar WRF and WRF, and ERA-Interim 2m-temperature (oC) in October 2007.

  12. Validation: T2m cold bias over sea ice (200712) Polar WRF WRF AVN

  13. Test Cases: Radiance T2m NCEP PREBUFER NCEP PREBUFER + RAD

  14. Validation: Radiance NCEP PREBUFER NCEP PREBUFER + RAD

  15. ValidationMicrophysics and Cloud Fraction Morrison 2-moment scheme WSM 6-class scheme Cloud fraction. one month cycling run (200808) WSM 6-class scheme Morrison 2-moment scheme Cloud fraction. one month cycling run (200808)

  16. Validation: Surface Pressure Diurnal Cycle (200808) ASR (3h) AVN (6h) ERA-Interim (6h)

  17. Two YearCycling Runs • Period: 2007-2008 • Reduced Resolution: 30km/71L, 10mb top • Observations in 3-hour time window • PREPBUFR (global obs errors) • satellite radiance data (AMSU-A, AMSU-B, MHIS) • ERA-Interim reanalysis for LBCs • WRF-3DVar/Polar WRF version 3.1.1 • Full 3-hourly cycling run on OSC Glenn cluster

  18. Polar WRF (Version 3.2) Numerical and Physics options Non hydrostatic dynamics; 5th order horizontal advection (upwind-based); 3rd order vertical advection; Positive-definite advection for moisture; 6th-order horizontal hyper diffusion; Full horizontal diffusion; Grid nudging to ERA-Interim 200 hPa and above; Upper damping; Morrison double-moment scheme; New Grell sub-grid scale cumulus scheme; RRTMG atmospheric radiation scheme; RRTMG shortwave scheme; MYNN planetary boundary layer scheme; MYNN surface layer; Noah land surface model; Fractional sea ice.

  19. Observational Datafor ASR • PREPBUFR • Including synop, metar, ship, buoy, qscat, sound, airep, profiler, pilot, satob, ssmi_retrieval_sea_surface_wind_speed, ssmi_retrieval_pw • Radiances • amsua, amsub, mhs • Fractional Sea Ice

  20. Domain for ASR

  21. ASR Data Assimilation Result Precipitation ASR ERA-Interim ERA ERA Yearly Total 2008, Unit: cm

  22. Near Surface Variables Statistics The data used for the surface statistics: NCEP FNL (or AVN) ERA-Interim Surface stations (More than 12000 obtained from the National Climatic Data Center (NCDC) and Greenland Climate Network (GC-NET))

  23. ASR Data Assimilation Result: Near Surface Variables Statistics Average statistics from comparing ASR for 2007 with observations. Average statistics from comparing AVN for 2007 with observations.

  24. ASR Data Assimilation Result 2m Temperature Statistic Differences between ASR (AVN) assimilation and observations in August 2008 ASRcorrelation -AVNcorrelation ASRrmse -AVNrmse

  25. ASR Data Assimilation Result 2m Temperature Statistic Differences between ASR (ERA) assimilation and observations in August 2008 ASRcorrelation -ERAcorrelation ASRrmse -ERArmse

  26. ASR Data Assimilation Result 10m Wind Speed Statistic Differences between ASR (AVN) assimilation and observations in August 2008 ASRcorrelation -AVNcorrelation ASRrmse -AVNrmse

  27. ASR Data Assimilation Result 10m Wind Speed Statistic Differences between ASR (ERA) assimilation and observations in August 2008 ASRcorrelation -ERAcorrelation ASRrmse -ERArmse

  28. Summary A reduced-resolution prototype assimilation for the Arctic for 2007-2008 has been completed. Major progress has been made. The test results look very encouraging, and have being distributed to selected users (16 months). However, still quite some testing to do to optimize ASR, especially the physics, and land surface data assimilation. To engage the community in early ASR evaluation and use, a 10-km, 3-h assimilation is planned to be run for 4 months, and a 30-km (3h) assimilation will be completed for 10 years(2000-2009)by end of 2010.

More Related