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Patrick Hofmann 1 , M. Hu 1 , S. G. Benjamin 2 , S . S. Weygandt 2 , C. R. Alexander 1

GSI applications within the Rapid Refresh and High Resolution Rapid Refresh 17 th IOAS-AOLS Conference 93 rd AMS Annual Meeting 9 January 2013. Patrick Hofmann 1 , M. Hu 1 , S. G. Benjamin 2 , S . S. Weygandt 2 , C. R. Alexander 1.

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Patrick Hofmann 1 , M. Hu 1 , S. G. Benjamin 2 , S . S. Weygandt 2 , C. R. Alexander 1

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  1. GSI applications within the Rapid Refresh and High Resolution Rapid Refresh17th IOAS-AOLS Conference93rd AMS Annual Meeting9 January 2013 Patrick Hofmann1, M. Hu1, S. G. Benjamin2, S. S. Weygandt2, C. R. Alexander1 1Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado 2NOAA/ESRL/Global Systems Division – Assimilation and Modeling Branch

  2. Rapid Refresh and HRRRNOAA hourly updated models NCEP RUC Rapid Refresh (01 May 2012) — Advanced community codes (ARW and GSI) — Retain key features from RUC analysis / model system ( hourly cycle -- radar DFI assimilation -- cloud analysis ) — RAP short-range guidance for aviation, severe weather, energy applications Rapid Refresh v2 GSD • Many improvements, • target NCEP implement • early 2014? HRRR GSD • Runs as nest within RAP v2

  3. Rapid RefreshHourly Update Cycle Partial cycle atmospheric fields – introduce GFS information 2x/day Fully cycle all land-sfc fields 1-hr fcst 1-hr fcst 1-hr fcst Back- ground Fields Analysis Fields 3DVAR 3DVAR Obs Obs Time (UTC) 11 12 13

  4. Rapid Refresh – specific analysis features Cloud and hydrometeor analysis Special treatments for surface observations Digital filter-based reflectivity assimilation

  5. Rapid Refresh version 2 data assimilation upgrades • Cloud analysis-related changes • Improved full column cloud building using emissivity from satellite data • Conservation of virtual potential temperature during cloud building • Improved use of existing observations • Assimilation of surface moisture pseudo-obs in PBL • Soil adjustment based on surface temperature and moisture increments • Elevation correction, innovation limitation for PW observations • Closer fit to rawinsondes • Other improvements • GFS ensemble background error covariance specification • Merge with recent GSI trunk • Addition of tower, nacelle, and sodar observations • Addition of GLD 360 lightning data (proxy for radar reflectivity) • Radiance bias correction

  6. Rapid Refresh version 2 data assimilation upgrades • Cloud analysis-related changes • Improved full column cloud building using emissivity from satellite data • Conservation of virtual potential temperature during cloud building • Improved use of existing observations • Assimilation of surface moisture pseudo-obs in PBL • Soil adjustment based on surface temperature and moisture increments • Elevation correction, innovation limitation for PW observations • Closer fit to rawinsondes • Other improvements • GFS ensemble background error covariance specification • Merge with recent GSI trunk • Addition of tower, nacelle, and sodar observations • Addition of GLD 360 lightning data (proxy for radar reflectivity) • Radiance bias correction

  7. Cloud Building Experiments • Retro Period: 29 May – 12 June 2011 • CONTROL: RAPV2, with cloud building below 1200m • FULL BUILDING: Full column building using a cloud top pressure-based cloud fraction • ECA BUILDING: Full column building using effective cloud amount (ECA), which uses cloud emissivity as a proxy for true cloud fraction • ECA BUILDINGv2: ECA BUILDING, but no clearing from partially cloudy regions NESDIS CLAVR-x data provided courtesy of Andrew Heidinger (UW/CIMSS/NOAA-AWG) CLAVR-x is NOAA's operational cloud processing system for the AVHRR on the NOAA - POES and EUMETSAT-METOP series of polar orbiting satellites

  8. Cloud Building Experiments 29 May - 11 June 2011 Relative Humidity Bias 3HR CTRL FULL ECA ECAv2

  9. Cloud Building Experiments 29 May - 11 June 2011 Relative Humidity Bias 6HR CTRL FULL ECA ECAv2

  10. Cloud Building Experiments 29 May - 11 June 2011 3000ft Ceiling TSS 1HR CTRL FULL ECA ECAv2

  11. Cloud Building Experiments 29 May - 11 June 2011 3000ft Ceiling TSS 3HR CTRL FULL ECA ECAv2

  12. Cloud Top Comparison 12Z 7 June 2011 Low (<1200m) Cloud Building Full Column Cloud Building

  13. Ceiling Comparison 12Z 7 June 2011 Low Cloud Building 3000ft Ceiling Stats CSI= 0.55 BIAS= 1.3 Full Column Cloud Building 3000ft Ceiling Stats CSI= 0.56 BIAS= 1.3

  14. HourlyHRRRInitialization from RAP 13z 14z 15z 13 km RAP Obs Obs Obs GSI 3D-VAR GSI 3D-VAR GSI 3D-VAR HM Obs HM Obs HM Obs Cloud Anx Cloud Anx Cloud Anx 1 hr fcst 1 hr fcst Refl Obs Refl Obs Refl Obs Digital Filter Digital Filter Digital Filter 18 hr fcst 18 hr fcst 18 hr fcst 3-km Interp 3-km Interp 3-km Interp 3 km HRRR 15 hr fcst 15 hr fcst 15 hr fcst

  15. GSI Applications

  16. GSI Applications • RTMA-HRRR • Real Time Meso-scale Analysis • 1hr HRRR forecast used as background field • Anisotropic error covariance fields • Currently run hourly; plan to produce 15min output • RUA-HRRR • Rapidly Updated Analysis • Full cloud analysis based on HRRR background field • Includes cloud, radar, and surface analyses • Specifies hydrometeors from radar observations • Improves initial reflectivity field

  17. GSI Applications • RTMA-HRRR • Real Time Meso-scale Analysis • 1hr HRRR forecast used as background field • Anisotropic error covariance fields • Currently run hourly; plan to produce 15min output • RUA-HRRR • Rapidly Updated Analysis • Full 3D GSI analysis based on HRRR background field • Includes cloud, radar, and surface analyses • Specifies hydrometeors from radar observations • Improves initial reflectivity field

  18. 3km RTMA-HRRR 1-hr HRRR Fcst (Background)Valid 19 UTC 30 Nov 2012 RTMA-HRRRValid 19 UTC 30 Nov 2012 Analysis Increments RTMA Anx HRRR 10 m Winds

  19. 3km RTMA-HRRR 30 Nov – 4 Dec 2012 2m Temperature RMS 1 Day Avgs RTMA HRRR 0HR HRRR 1HR

  20. 3km RTMA-HRRR 30 Nov – 4 Dec 2012 2m Dewpoint RMS 1 Day Avgs RTMA HRRR 0HR HRRR 1HR

  21. 3km RTMA-HRRR 30 Nov – 4 Dec 2012 10m WindsRMS 1 Day Avgs RTMA HRRR 0HR HRRR 1HR

  22. GSI Applications • RTMA-HRRR • Real Time Meso-scale Analysis • 1hr HRRR forecast used as background field • Anisotropic error covariance fields • Currently run hourly; plan to produce 15min output • RUA-HRRR • Rapidly Updated Analysis • Full 3D GSI analysis based on HRRR background field • Includes cloud, radar, and surface analyses • Specifies hydrometeors from radar observations • Improves initial reflectivity field

  23. 3km RUA-HRRR Obs 22 UTC03 Nov 2012 Specifies Hydrometeors From Radar Observations Rapidly Updating Analysis (RUA) Cloud Anx GSI 1-hr HRRR Forecast (Background)Valid 22 UTC 03 November 2012 0-hr HRRR Analysis (RUA)Valid 22 UTC 03 November 2012

  24. Conclusion • Completed RAP v2 Changes • GFS ensemble background error covariance specification • Improved cloud building • Assimilation of surface moisture pseudo-obsin PBL • Soil adjustment based on near-surface • temperature / moisture increments • Elevation correction, innovation limitation for PW observations • Conservation of virtual potential temperature during cloud building • Radiance bias correction • Merge with latest version of GSI from NCEP community trunk • Additional observations (radial wind, wind tower/nacelle, lightning) • GSI 3km applications • RTMA-HRRR provides improved first-guess fields for NDFD • RUA-HRRR results in more realistic initial model state, greatly improving reflectivity and 3-D hydrometeor fields

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