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Regional Data Impact Studies at NCAR And The JCSDA

Regional Data Impact Studies at NCAR And The JCSDA. WMO Observation Impact Meeting, Geneva, Switzerland, March 27th 2008 Dale Barker, T. Auligne, M. Demirtas, H. C. Lin, Z. Liu, S. Rizvi, H. Shao, Q. Xiao, and X. Zhang National Center for Atmospheric Research, Boulder, Colorado, USA.

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Regional Data Impact Studies at NCAR And The JCSDA

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  1. Regional Data Impact Studies at NCAR And The JCSDA WMO Observation Impact Meeting, Geneva, Switzerland, March 27th 2008 Dale Barker, T. Auligne, M. Demirtas, H. C. Lin, Z. Liu, S. Rizvi, H. Shao, Q. Xiao, and X. Zhang National Center for Atmospheric Research, Boulder, Colorado, USA

  2. WRF DA Research To Operations (NCAR/AFWA) JCSDA (CRTM, GSI) NCAR/MMM (WRF-Var, ARW) WRF Community R&D NCAR (DTC, DATC): Extended period testing Testbeds AFWA: Pre-operational testing, implementation Operations • NCAR/AFWA DA Program initiated in August 2006. • NCAR responsible for WRF-Var development and initial testing. • JCSDA provides Community Radiative Transfer Model (CRTM), etc. • WRF Community contributions include radar, radiance (RTTOVS), GPS, etc. • Data Assimilation Testbed Center (DATC) performs rigorous testing prior to ops.

  3. Outline of Talk WRF-Var Overview Antarctica: COSMIC/AMSU/AIRS/MODIS Impact. AFWA: AMSU Impacts Korea: Radar Impacts. Summary

  4. AFWA Theaters: WRF-Var Data Assimilation Overview • Goal: Community WRF DA system for regional/global, research/operations, and deterministic/probabilistic applications. • Techniques: 3D-Var, 4D-Var (regional), Hybrid Variational/Ensemble DA. • Models: WRF, MM5, KMA global. • Support: MMM Division, NCAR. • Observations: Conv.+Sat.+Radar GPS Radio Occultation (B. Kuo):

  5. 2. Antarctica: COSMIC, AMSU, AIRS, and MODIS Impacts

  6. DATC Antarctica Testbed Hui Shao, DATC Sonde Coverage COSMIC Coverage (24hrs) Testbed Configuration (from MMM/AMPS): • Model: WRF-ARW, WRF-Var (version 2.2). • Namelists: 60km (165x217), 31 vertical levels, 240s timestep. • Period: October 2006. • Purpose: Impact of DA cycling, model top, COSMIC.

  7. Number ofradiosondeand COSMICsoundings within one time window DATC-COSMIC Testbeds Air Force Weather Agency (AFWA) Taiwan Civil Aeronautics Administration (CAA) Antarctic Mesoscale Prediction System (AMPS)

  8. T (K) Forecast Impact 36hr Forecasts of Temperature vs Sondes Bias Assimilation of COSMIC refractivity: • Reduces the RMSE in the troposphere RMSE • Increases the RMSE in the stratosphere Direct impacts * Verified in the domain south of 60S NOGPS WGPSWGPS_ BE

  9. U (m/s) 36hr Forecasts of Wind Speed (U) vs Sondes Bias Assimilation of COSMIC refractivity: • Can reduce wind biases • Reduces the RMSE of wind forecast RMSE Indirect impacts * Verified in the domain south of 60S NOGPS WGPSWGPS_ BE

  10. RMSE Difference of u, v, T and q: WGPS-NOGPS (Negative Values <=> Positive Impacts of COSMIC data) ANL F12 F24 F48 F72 u (m/s) Positive impacts -1.2 0.3 v (m/s) -2.0 0.5 T (K) -0.4 1.6 q (g/kg) -0.4 0.2

  11. Sensitivity Study of Stratospheric COSMIC Data Assimilation

  12. RMSE of 36hr Forecasts wrt Sondes • WGPS_10mb vs WGPS: Moving the model top to 10mb decreases the RMSE of U and T forecasts in the stratosphere. • WGPS_250mb vs WGPS & WGPS_250mb vs NOGPS: Assimilation of COSMIC data only in troposphere sustains positive impacts in troposphere and decreases the RMSE of T forecasts in stratosphere as shown in WGPS. NOGPS WGPSWGPS_250mb WGPS_damp3 WGPS_10mb • WGPS_damp3 vs WGPS: The enhanced damping at the model top only marginally changes the RMSE of T(U) forecasts.

  13. Bias and RMSE of 36hr Forecasts of T wrt Sondes Assimilation of COSMIC data: • Reduces the bias of T forecasts in the lower-middle troposphere and stratosphere • Decreases the RMSE of T forecasts below 70mb NOGPS_10mb WGPS_10mb

  14. WRF-Var Radiance Assimilation (Liu et al. 2009) • BUFR 1b radiance ingest. • RTM interface: RTTOV8_5 or CRTM • NESDIS microwave surface emissivity model • Range of monitoring diagnostics. • Quality Control for HIRS, AMSU, AIRS, SSMI/S. • Bias Correction (Adaptive or Variational) • Variational observation error tuning • Parallel: MPI • Flexible design to easily add new satellite sensors NOAA (HIRS, AMSU) Aqua (AMSU, AIRS) DMSP(SSMI/S)

  15. OMB Before Bias Correction OMB and OMA for NOAA-15 CH7 OMA OMB After Bias Correction * QC has been applied to the data after BC

  16. AMSUA Impact: 36hr Forecast Score vs. RS • Horiz. resolution = 60km • 57 Levels, Model top = 10hPa • Full cycling • NOAA 15/16/18 AMSU-A channels 4 to 9 • Radiance over ocean only • Static Bias Correction (Harris and Kelly, 2001): 4 predictors • Thinning 120km • QC = thresholds on innovations

  17. ModelTop ModelTop Solarcontamination Ozone AIRS T Jacobians AIRS innovations: Channel Selection RTTOV CRTM T Surface O3 Q T

  18. Thinning (120km) 345 active data Warmest FoV 696 active data AIRS innovations: QC & Thinning • Channel-level QC • Grosscheck(innovations <15 K) • First-guess check(innovations < 3o). Error factor tuned from objective method (Desrozier and Ivanov, 2001) • Imager AIRS/VIS-NIR Day only (cloud coverage within AIRS pixel <5%) • Pixel-level QC • Reject limb observations • Reject pixels over land and sea-ice • NESDIS Cloud detection • LW window channel > 271K • Thresholds on model SST minus SST from 4 AIRS LW channels • Thinning

  19. AIRS Impact: 36hr Fcst. Score vs. Sondes Whole Domain High Latitudes (> 60S)

  20. Western Ross Sea / Ross Is. grids 6.6-km, 2.2-km grids 2.2-km Gill McMurdo Pegasus North Black Is. Transantarctic Mtns. • Minna Bluff Mt. Discovery Mt. Morning McMurdo Region & AWS sites

  21. Impact Of High-Resolution Cycling 2300 UTC 15 May— Hr 23 ms-1 ms-1 34 • 25 25 L Sfc Winds(ms-1) SLP(hPa) Von Karman vortex DA - Conventional DA: With MODIS DA: With reduced MODIS CTRL - WRF with GFS ICs

  22. Pegasus North Winds NoDA DA - With MODIS 35 35 35.3 35.3 36.6 24.6 20 20 Wind Speed (ms-1) Wind Speed (ms-1) Record ends Record ends OBS:WRF: Conventional DA - Reduced MODIS 35 35 35.3 35.3 31.5 29.3 20 20 Wind Speed (ms-1) Wind Speed (ms-1) Record ends Record ends Hr from 00 UTC 15 May Hr from 00 UTC 15 May

  23. 3. AFWA: AMSU Impacts

  24. 24hr Forecast Verification Vs. Obs for AFWA Testbed Meral Demirtas, DATC No Data Assimilation “Update” Cycling Full-cycling Conclusions: Regional DA adds significant value (even without radiances). Update-cycling (GFS first guess at 00/12 UTC) superior to full-cycling.

  25. East Asia Domain (T46) • 162*212*42L, 15km • model top: 50mb • Full cycling exp. for a month • 1 ~ 30 July 2007 • GTS+AMSU • NOAA-15/16, AMSU-A/B from AFWA • AMSU-A: channels 5~9 (T sensitive) • AMSU-B: channels 3~5 (Q sensitive) • Radiance used only over water • thinned to 120km • +-2h time window • Bias Correction (H&K, 2001) • Compare to GTS exp. • Only use GTS data from AFWA • 48h forecast, 4 times each day • 00Z, 006, 12Z, 18Z Land Use Category

  26. Obs used in assimilation (from AFWA operational datafeed)

  27. Impact Of AMSU Radiances in T46 (Liu et al. 2009) Verification against assimilated obs Vs. Profiler U Slightly negative impact within 24h Vs. Profiler V Slightly positive impact Beyond 24h Vs. Sound T Neutral Vs. Sound Q Neutral/Slightly negative

  28. Impact Of AMSU Radiances in T46 Verification against unassimilated obs Vs. SATEM Thickness Positive impact Vs. AIRS retrieval T Slightly positive impact Impact decreases With forecast range LBC takes control For long range FC Vs. AIRS retrieval Q Slightly positive impact beyond 24h Vs. GPS Refractivity Postive impact

  29. Atlantic Domain (T8) • 361*325*57L, 15km • Quite compute-demanding for WRF forecast • model top: 10mb • Full cycling exp. for 6 days • 15 ~ 20 August 2007 • GTS: assimilate NCAR conventional obs • Select similar data type used by AFWA • No SSM/I retrieval • GTS+AMSU+MHS (use NCEP BUFR rad.) • NOAA-15/16/18, AMSU-A, ch. 5~10 • NOAA-15/16/17, AMSU-B, ch. 3~5 • NOAA-18, MHS (similar to AMSU-B) • Radiance used only over water • thinned to 120km • +-2h time window • Bias Correction (H&K, 2001) • 48h forecast twice each day • 00Z, 12Z • Might not optimal to use all sensors/satellites at the first try, but I want to test the robustness of the system with all Microwave sensors which can be assimilated in WRF-Var now. Land Use Category

  30. T8: 48h forecast error vs. sound

  31. 4. Korea: Radar Impacts

  32. Radar Assimilation In WRF-Var • Quality Control: • Complex, vital…… • Radial Velocity Assimilation: • Vertical velocity increments diagnosed. • 3D radial velocity observations assimilated. • Reflectivity Assimilation: • Total water control variable (qt=qv+qc+qr). • Background error statistics for qt currently based on water-vapor (qv). • 3D-Var: Moist physics scheme included in observation operator. • 4D-Var: Awaiting inclusion of microphysics scheme in linear model.

  33. Resolution : 0.02o x 0.02o x 0.5km • Domain : 5o x 5o x 10km Coordinate conversion/ interpolation (SPRINT, CEDRIC) Flowchart for radar data preprocessing Vr, dBZ (Lat, Lon, Z ) RKSG Vr, dBZ Velocity Dealiasing Composite map 3-hr forecast as a reference wind • Reflectivity: Maximum value • Radial velocity: in order RGDK, RJNI Vr, dBZ (r,,  ) • dBZ >= 10 • > 5 levels Additional QC • every 3 grid points ~ 6 km Thinning Write out for 3dVar

  34. Korean Radar Data Assimilation in WRF-Var Typhoon Rusa Test Case 3hr Precip: Typhoon Rusa 3hr Precip. Verification: Obs (03Z, 31/08) No Radar KMA Pre-operational Verification: (no radar: blue, with radar: red) Threat Score Bias Radar RV Radar RV+RF

  35. KMA/WRF Testbed 10km res. “RDAPS” 3.3km res. “HiNWP” Nest Testbed Configuration (NCAR/KMA project 2007): • Model:WRF-ARW, WRF-Var (version 2.2). • Domains: 10km (574x514) RDAPS, 3.3km (428x388) HiNWP. • Period: Summer 2007 Changma Season (July 1 - August 10th). • DA: 3D-Var. RDAPS - 6-hrly cycling. HiNWP- 3hrly cycling.

  36. Korean 41 day Changma/Baiu Season Testbed:24hr Forecast Verification: Bias 10km Domain (+ve cycling impact) 3.3km Domain (+ve radar RV impact) Barker et al., In preparation

  37. Airborne Doppler Radar Assimilation for Hurricane Jeanne (Xiao et al 2007) Surface Pressure analysis NOAA 43 Flight Track

  38. Hurricane Jeanne Forecast Skill Track Error Maximum Wind • CRTL = No data assimilation. • GTS = Conventional observations only. • RV43 = Radar winds only. • RV43/GTS = Radar winds + GTS. • 24hr forecast errors shown.

  39. Summary WRF-Var: 3D-Var robust, 4D-Var/EnKF initial tests. Antarctica: Encouraging results from COSMIC, AMSU, AIRS. AFWA: Neutral/positive impacts of AMSU in E. Asia/Tropics. Korea: +ve impact in 3D-Var, mainly from radial velocities. Current foci: Bias correction, cloud detection, new applications.

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