1 / 34

Data Assimilation in AMPS

Data Assimilation in AMPS. Dale Barker S. Rizvi, and M. Duda MMM Division, NCAR Email: dmbarker@ucar.edu http://www.mmm.ucar.edu/3dvar. Talk Overview. The MM5/WRF 3DVAR system. MM5 3DVAR in Alaska. AMPS observation study. 3DVAR performance in AMPS. Ensemble Kalman Filter in AMPS.

dunn
Download Presentation

Data Assimilation in AMPS

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. Data Assimilation in AMPS Dale Barker S. Rizvi, and M. Duda MMM Division, NCAR Email:dmbarker@ucar.edu http://www.mmm.ucar.edu/3dvar

  2. Talk Overview • The MM5/WRF 3DVAR system. • MM5 3DVAR in Alaska. • AMPS observation study. • 3DVAR performance in AMPS. • Ensemble Kalman Filter in AMPS.

  3. The MM5/WRF 3D-Var System

  4. MM5/WRF 3DVAR Algorithm • Define analysis increments: xa = xb + I x’ • Solve model space, incremental cost function: where y’ = Hx’, yo’ = yo - y. • Preconditioned control variablev analysis space (B = UUT): • Choice of background error covariance model (NCAR, NCEP).

  5. 3DVAR in the MM5/WRF Modeling Systems MM5/WRF Background Preprocessing xb Observation Preprocessor yo 3DVAR xa Update Boundary Conditions MM5/WRF Forecast Background Error Calculation B Cold-Start Mode

  6. 3DVAR in the MM5/WRF Modeling Systems xb Observation Preprocessor yo 3DVAR xa Update Boundary Conditions MM5/WRF Forecast Background Error Calculation B Cycling Mode

  7. 2. MM5 3D-Var in Alaska Courtesy of AFWA

  8. 3DVAR/MM5 AFWA Alaska “T1” Theater

  9. MM5 3D-Var Comparisons: Alaska Theater (T1)

  10. MM5 3D-Var Comparisons: Alaska Theater (T1) • MM5 production (MVOI) compared to 3D-Var initialized MM5 over the Alaska Theater • Two cycles were run: 6Z and 18Z • Data from both cycles is averaged together • Model runs occurred between 9/7/02 and 9/15/02 • All verification is compared to observations

  11. MM5 3D-Var Comparisons: Alaska Theater (T1)

  12. AFWA Europe “T3A” 45km Verification: June 4-July 10 2002. • 3DVAR ( ) vs. MVOI ( ). • Verification against radiosondes: 00hr, 12hr, 24hr. Height Relative Humidity

  13. 3. AMPS Observation Study

  14. Observations Available (September 2003) • In-Situ: • Surface (SYNOP, METAR, SHIP, BUOY). • Upper air (TEMP, PIBAL, AIREP, ACARS). • Remotely sensed retrievals: • Wind profiler. • Atmospheric Motion Vectors (SATOBS). • ATOVS thicknesses (SATEMs). • GPS total precipitable water. • GPS refractivity. • SSM/I oceanic surface wind speed and TPW. • SSM/T1 temperature. • SSM/T2 relative humidity. • Scatterometer (Quikscat) oceanic surface winds. • Radar radial velocity. • Radiances: • SSM/I brightness temperatures.

  15. December 2002 AMPS Observation Statistics • Present statistics for 30km AMPS domain 2. • 3DVAR performed at 00 and 12 UTC. • First Guess = NCEP “final” analysis. • Total 62 analyses.

  16. December 2002 AMPS 30km Temperature Statistics Current setup: 12 hourly “cold starts” from NCEP global analysis

  17. December 2002 AMPS 30km Wind Statistics

  18. December 2002 Synop T, p variation by station. • Variety of diagnostic utilities developed. • Some stations indicate bias w.r.t. model. • Need to update station elevations?

  19. Preliminary Testing of MODIS (TERRA) data Data time: 12 UTC 12/01/2002 Time Window: +/- 90 minutes. QC: Reject if O-B>5 sigma_o Observation Error: 4.5m/s 3060 obs after QC in 45km area. O-B mean/std.dev = 0.53, 5.26m/s

  20. Preliminary Testing of MODIS (TERRA) data MODIS O-B vs Pressure MODIS O-B vs Latitude

  21. Preliminary Testing of MODIS (TERRA) data 55 obs after QC in 15km area. O-B u mean/rms = 4.32/6.88m/s O-A u mean/rms = 0.77/3.72m/s J / num_obs = 0.468

  22. 4. 3DVAR performance in AMPS Work performed by Syed Rizvi, Mike Duda

  23. 3DVAR Background Error – Vertical Eigenvectors Streamfunction Velocity Potential • Conclusion: • Minor differences in streamfunction. • Very different dominant mode for velocity potential. Old (global) – above, New (AMPS) – below.

  24. 3DVAR Background Error – Horizontal Lengthscales Streamfunction Velocity Potential • Conclusion: • “Local” lengthscales significantly shorter. • Should result in closer fit to observations. Old (global) – above, New (AMPS) – below.

  25. 3DVAR Single Observation Test

  26. 3DVAR Single Observation Test po- pb = 1mb, observation error = 1mb.

  27. 3DVAR Single Observation Test po- pb = 1mb, observation error = 1mb.

  28. 3DVAR Analysis – 00 UTC 17 June 2003 T, p increment Sea Level P, Surface Wind

  29. 3DVAR Analysis – 00 UTC 17 June 2003 u, v increment Sea Level P, Surface Wind

  30. AMPS Domain 1 Real-Time Verification: T+00

  31. AMPS Domain 1 Real-Time Verification: T+12

  32. AMPS Domain 1 Real-Time Verification: T+24

  33. AMPS Domain 1 Real-Time Verification: T+36

  34. Conclusions and Future Work • “Basic” 3DVAR operational in AFWA Alaskan domain. • Antarctic December 2002-January 2003 data collection underway (GPS, MODIS, includes tuning of ob errors). • AMPS real-time data ingest issues isolated, working on. • Initial performance of 3DVAR in AMPS is satisfactory. • 3DVAR/Ensemble Kalman Filter comparison begun.

More Related