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Define the problems of CWB WRF ---performance of 3DVAR

Define the problems of CWB WRF ---performance of 3DVAR. Impact of observations. Black line : first guess Purple line : analysis. Only Qscat data. Black line : first guess Purple line : analysis. Qscat data & Sound data. Qscat data & Sound data & Pilot data. Black line : first guess

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Define the problems of CWB WRF ---performance of 3DVAR

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  1. Define the problems of CWB WRF ---performance of 3DVAR

  2. Impact of observations

  3. Black line : first guess Purple line : analysis Only Qscat data

  4. Black line : first guess Purple line : analysis Qscat data & Sound data

  5. Qscat data & Sound data & Pilot data Black line : first guess Purple line : analysis

  6. Qscat data & Sound data & Pilot data & Airep data Impact of ACARS Black line : first guess Purple line : analysis

  7. Qscat data & Sound data & Pilot data & Airep data & Gpsro data Black line : first guess Purple line : analysis Strange results over indonesia after assimilate the GPSSRO data

  8. 在Ａ基礎下加入地面資料的分析場比較 A Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data & Metar data Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data & Buoy data Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data Impact of SYNOP Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data & Synop data Qscat data & Sound data & Pilot data & Airep data & Gpsro data & Satem data & Satob data & Ships data All obs data

  9. NCEP GFS WRF GPS/RO ․ 09070500 500 hPa 850 hPa 200 hPa

  10. Data impact of GPSRO on analysis field Focus on 2008062100~2008063012

  11. NODA & EC COLDACV3 & EC COLDACV5 & EC 0.979 0.953 10 day Anomaly Correlation 0.936 COLDACV3 (NO GPSRO) & EC COLDACV5 (NO GPSRO) & EC Blue : analysis Red : EC analysis 2008062100 ~ 2008063012 10 day mean analysis compare with EC analysis 500 hPa Geopotential Height 0.968 0.963 拿掉GPSRO對分析場有明顯的助益 • Take out GPSRO improved sub high range and also modified analysis field on Indo-China Peninsula

  12. NODA & EC COLDACV3 & EC COLDACV5 & EC 0.991 0.975 0.961 COLDACV3 (NO GPSRO) & EC COLDACV5(NO GPSRO) & EC Blue : analysis Red : EC analysis 2008062100 ~ 2008063012 10 day mean analysis compare with EC analysis 300 hPa Geopotential Height 0.982 0.980 拿掉GPSRO對分析場有明顯的助益

  13. 10 day mean 24hr 48hr 00hr Red : EC analysis NODA & EC NODA & EC NODA & EC 0.979 0.967 0.930 COLDACV5 & EC COLDACV5 & EC COLDACV5 & EC 0.932 0.936 0.966 reduce CYCLEACV5 & EC CYCLEACV5 & EC CYCLEACV5 & EC 0.920 0.950 0.908

  14. Although the poor quality of analysis could be recovered quickly in forecast, however, the direct impact is to contribute the uncertainty in verification and drop off forecaster’s confidence. We have to conduct a comprehensive OSE to optimal use the observations in 3DVAR. Especially for GPSRO, Synop, and Airep. Can we not only to remove or eliminate the “question data”, but also find ways to best use the observations? In addition to improve the data use policy, e.g. the QC or data thinning, is there problems in 3DVAR? How to do a reasonable surface analysis? How to assimilate the surface observations?

  15. Analysis performance

  16. 03 and 09 hr fcst 00 and 06 hr fcst 06 and 12 hr fcst 15 and 21 hr fcst

  17. 12 and 18 hr fcst 03 and 09 hr fcst

  18. IC at 00Z IC at 06Z Calculate the difference

  19. 80 cases Analysis increment F12-F6 F18-F12 The difference for 00Z-18Z and 12Z-06Z should be larger, while 06Z-00Z and 18Z-112Z should be smaller (The observations at 06 and 18Z is not much as 00 and 12Z). For analysis increment, IC at 06 Z (06Z-00Z ) has the largest analysis increment, why?

  20. 00-12 hr fcst Calculate over 56 runs at 00 and 12 Z NoDFI 12-24 hr fcst With DFI NODA 24-36 hr fcst

  21. 00-12 hr fcst Calculate over 56 runs at 00 and 12 Z CV3 12-24 hr fcst CV5 24-36 hr fcst

  22. Calculate over 28 runs at 00 Z only 00-12 hr fcst 12-24 hr fcst 24-36 hr fcst

  23. Experiments design 1. All experiments run one month (2008060100~2008063012) except for “no gpsro” 2. All experiments compare with ECMWF analysis

  24. Analysis Field

  25. Summary of compare with ECMWF Big jump

  26. Summary of compare with ECMWF

  27. Summary of analysis field • Height : NO DATA > COLDSTART+CV3 >COLDSTART+CV5 > CYCLE+CV5 • Temperature: NO DATA is best，CYCLE+CV5 is worst • Wind: NO DATA is best，CYCLE+CV5 is worst • There are obvious gaps between NO DATA and experiments with 3DVAR, especially on height field. The difference in temperature is not so big. • Any problems to derive the height field in 3DVAR? • COLDSTART is better than CYCLE !

  28. Forecast Field

  29. Geopotential Height -- AC OP2 is the worst • The degrade in 3DVAR experiment is reduced in forecast. • CV5 (cycle) is worst, Is it due to the bad 1st guess, both in analysis and forecast, which is due to the bad analysis?

  30. Geopotential Height -- RMS • The analysis error is larger than the 24-hr forecast error in 3DVAR cases, which is consistent with Hong’s results.

  31. Temperature -- RMS • In analysis, OP2 is worst, in forecast, OP2 is still worst in 850 hPa, no big difference above 500 hPa.

  32. Wind -- RMS • Base on RMS results，the reduced difference between four experiments followed the increase of forecast • 850hPa : NO DATA is best

  33. Summary • The 3DVAR degrade the analysis performance • Height is more significant than T, is there any problem to derive the height field in 3DVAR? • The degrade in 3DVAR experiment is reduced in forecast. • Cold start is better than cycle • Is it due to the bad 1st guess, which is from the bad analysis? • The bottom line is • We can’t give up the mesoscale data assimilation • We need to keep some kind of cyclic run, to take the advantage from firstguess. • The poor performance of analysis is not only hurt model initial condition, but also first guess.

  34. It is shown that there is apparent problems on the 3DVAR performance. Is there any fundamental problems in 3DVAR? -The performance of multi-variable analysis? -The minimization procedure? -The translation between analysis variables and model variable? -Role of outer loop? Is it a general feature in regional variational analysis? -Assess the performance of GSI -Assess the performance of EAKF

  35. Impact of DFI on 500 hPa Geopotential Height 10 day mean Blue : analysis Red : EC analysis CYCLEACV5 & EC CYCLEACV5DFI & EC 0.920 0.936 Use DFI DFI improved (smoothed) analysis field of CYCLE+CV5 obviously，but still can’t similar to NO DATA

  36. DFI DFI

  37. DFI

  38. without DFI Relo+new bogus with DFI Relo+new bogus 00 12 24 36 48 60 72 hours Without 21 89 169 235 333 359 307 km With 11 106 180 201 277 248 258 km Cases 12 12 10 9 7 2 2

  39. Summary • DFI can improve the poor 3DVAR analysis efficiently, in particular in typhoon initialization. • How to use DFI efficiently and correctly? • The plan to develop the DFI in nest domain. • Any plan to develop the other initialization scheme?

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