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.

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Presentation transcript:

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 1 Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado 2 NOAA/ESRL/Global Systems Division – Assimilation and Modeling Branch

Rapid Refresh and HRRR NOAA hourly updated models — 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 RUC  Rapid Refresh (01 May 2012) Rapid Refresh v2 Many improvements, target NCEP implement early 2014? HRRR NCEP GSD Runs as nest within RAP v2

Rapid Refresh Hourly Update Cycle 1-hr fcst 1-hr fcst 1-hr fcst Time (UTC) Analysis Fields 3DVAR Obs 3DVAR Obs Back- ground Fields Partial cycle atmospheric fields – introduce GFS information 2x/day Fully cycle all land-sfc fields Hourly observations (stations for raobs/profiles) RAP 2012 N.Amer Rawinsonde (T,V,RH)120 Profiler – NOAA Network (V)21 Profiler – 915 MHz (V, Tv)25 Radar – VAD (V)125 Radar reflectivity - CONUS2km Lightning (proxy reflectivity)NLDN Aircraft (V,T)2-15K Aircraft - WVSS (RH)0-800 Surface/METAR (T,Td,V,ps,cloud, vis, wx) Buoys/ships (V, ps) Mesonet (T, Td, V, ps)flagged GOES AMVs (V) AMSU/HIRS/MHS radiancesUsed GOES cloud-top pressure/temp13km WindSat scatterometer2-10K

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

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 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 Rapid Refresh version 2 data assimilation upgrades

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

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

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

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

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

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

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

3-km Interp Hourly HRRR Initialization from RAP GSI 3D-VAR Cloud Anx Digital Filter 1 hr fcst 18 hr fcst 15 hr fcst 3-km Interp GSI 3D-VAR Cloud Anx Digital Filter 1 hr fcst 18 hr fcst 15 hr fcst 3-km Interp GSI 3D-VAR Cloud Anx Digital Filter 18 hr fcst 15 hr fcst 13 km RAP 3 km HRRR 13z 14z 15z

Backgroun d Radar Specification of Hydrometeors Scale at which Latent Heating is applied DimensionalityUpdated 2013 RAP model initialization BCs from GFS No13-km3-DHourly 2013 HRRR model initialization 13-km RAPNo 3km in 60min spin-up (also using 13km radar-LH-DFI) 3-DHourly Rapidly Updating Analysis (RUA- HRRR) 3-km HRRR 1 hr fcst YesNone3-DHourly Real-Time Meso Analysis (RTMA-HRRR) 3-km HRRR 1 hr fcst NoNone2-D Hourly (15 min planned) 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 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 GSI Applications

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

30 Nov – 4 Dec m Temperature RMS 1 Day Avgs RTMA HRRR 0HR HRRR 1HR

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

3km RTMA-HRRR 30 Nov – 4 Dec m Winds RMS 1 Day Avgs RTMA HRRR 0HR HRRR 1HR

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 GSI Applications

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

Conclusion Completed RAP v2 Changes -GFS ensemble background error covariance specification -Improved cloud building -Assimilation of surface moisture pseudo-obs in 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