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Regional Data Impact Studies at NCAR And The JCSDA WMO Observation Impact Meeting, Geneva, Switzerland, March 27th 2008 Dale Barker, T. Auligne, M. Demirtas,

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Presentation on theme: "Regional Data Impact Studies at NCAR And The JCSDA WMO Observation Impact Meeting, Geneva, Switzerland, March 27th 2008 Dale Barker, T. Auligne, M. Demirtas,"— Presentation transcript:

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) NCAR/AFWA DA Program initiated in August 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. AFWA: Pre-operational testing, implementation NCAR (DTC, DATC): Extended period testing NCAR/MMM (WRF-Var, ARW) JCSDA (CRTM, GSI) WRF Community R&D Testbeds Operations

3 1) WRF-Var Overview 2) Antarctica: COSMIC/AMSU/AIRS/MODIS Impact. 3) AFWA: AMSU Impacts 4) Korea: Radar Impacts. 5) Summary Outline of Talk

4 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 AFWA Theaters: GPS Radio Occultation (B. Kuo):

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

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

7 DATC-COSMIC Testbeds Antarctic Mesoscale Prediction System (AMPS) Taiwan Civil Aeronautics Administration (CAA) Air Force Weather Agency (AFWA) Number of radiosonde and COSMIC soundings within one time window

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

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

10 RMSE Difference of u, v, T and q: WGPS-NOGPS (Negative Values Positive Impacts of COSMIC data) ANLF12F24F48F72 u (m/s) v (m/s) T (K) q (g/kg) Positive impacts

11 Sensitivity Study of Stratospheric COSMIC Data Assimilation

12 NOGPS WGPS WGPS_250mb WGPS_damp3 WGPS_10mb 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. WGPS_damp3 vs WGPS: The enhanced damping at the model top only marginally changes the RMSE of T(U) forecasts. WGPS_10mb vs WGPS: Moving the model top to 10mb decreases the RMSE of U and T forecasts in the stratosphere. RMSE of 36hr Forecasts wrt Sondes

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 DMSP(SSMI/S) Aqua (AMSU, AIRS) NOAA (HIRS, AMSU)

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

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 AIRS innovations: Channel Selection T Surface O3O3 TQ AIRS T Jacobians AIRS T Jacobians Model Top Ozone Solar contamination Model Top RTTOV CRTM

18 AIRS innovations: QC & Thinning 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 Channel-level QC –Gross check (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%) Thinning (120km) 345 active data Thinning (120km) 345 active data Warmest FoV 696 active data Warmest FoV 696 active data Thinning

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

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

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

22 Pegasus North Winds Wind Speed (ms -1 ) OBS: WRF: Hr from 00 UTC 15 May Wind Speed (ms -1 ) Hr from 00 UTC 15 May NoDA DA - Reduced MODISConventional DA - With MODIS Record ends

23 3. AFWA: AMSU Impacts

24 24hr Forecast Verification Vs. Obs for AFWA Testbed Conclusions: 1. Regional DA adds significant value (even without radiances). 2. Update-cycling (GFS first guess at 00/12 UTC) superior to full-cycling. No Data Assimilation Update Cycling Full-cycling Meral Demirtas, DATC

25 East Asia Domain (T46) Land Use Category 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

26 Obs used in assimilation (from AFWA operational datafeed)

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

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

29 Atlantic Domain (T8) Land Use Category 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 robustne ss of the system with all Microwave sensors which can be assimilated in WRF-Var now.

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

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

35 KMA/WRF Testbed 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. 10km res. RDAPS3.3km res. HiNWP Nest

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) NOAA 43 Flight Track Surface Pressure analysis

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

39 1) WRF-Var: 3D-Var robust, 4D-Var/EnKF initial tests. 2) Antarctica: Encouraging results from COSMIC, AMSU, AIRS. 3) AFWA: Neutral/positive impacts of AMSU in E. Asia/Tropics. 4) Korea: +ve impact in 3D-Var, mainly from radial velocities. 5) Current foci: Bias correction, cloud detection, new applications. Summary

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