1 / 51

Highlight of the CWB WRF development

Highlight of the CWB WRF development . OUTLINE. Re-center the EAKF using the blending scheme Hybrid variational /ensemble data assimilation . Blending Scheme. Guess Blending. Analysis Blending. Fields to be Blended ( 13) - U, V, T, QVAPOR, PH, P, MU, - U10, V10, T2, Q2, PSFC, TH2.

micol
Download Presentation

Highlight of the CWB WRF development

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. Highlight of the CWB WRF development

  2. OUTLINE • Re-center the EAKF using the blending scheme • Hybrid variational/ensemble data assimilation

  3. Blending Scheme Guess Blending Analysis Blending Fields to be Blended (13) - U, V, T, QVAPOR, PH, P, MU, - U10, V10, T2, Q2, PSFC, TH2 CLS=1200 km

  4. Ensemble Forecast 6hr analysis analysis guess EA K F Xb1 Xb1 EA K F RECENTER Xa1 Xb2 Xb2 Xa2 Xb32 Xb32 Xa32 guess_mean Blend guess_mean Blend analy_mean Guess Blending WRF 72hr

  5. Ensemble Forecast 6hr guess analysis EA K F Xb1 Xa1 Xa1 RECENTER Xb2 Xa2 Xa2 Xb32 Xa32 Xa32 Blend Blend analy_mean analy_mean Blend Analysis Blending WRF 72hr

  6. Experimental Design • Model Version：CWB OP24 • Run：Full Cycling • Domain：CWB WRF Domain 1 (45KM) • Period： 2008/09/04 00UTC ~ 2008/09/23 00 UTC T-PARC • Experiments： • Without Blending (woBL, CTL) • Guess Blending (guessBL) • Analysis Blending (analyBL) • analyBL + Ensemble Mean Analysis with Bleinding (analyBL_mBL)

  7. Ensemble Performance T U V woBL small guessBL over analyBL

  8. SCORE of Ensemble Mean Analysis Forecast 72hr (against NCEP) woBL：代表無Blending guessBL：Guess Blneding analyBL：Analysis Blending analyBL_m：blending again on analyBL ensemble mean 24hr T H U V

  9. SCORE of Ensemble Mean Analysis Forecast 72hr (against NCEP) woBL：代表無Blending guessBL：Guess Blneding analyBL：Analysis Blending analyBL_m：blending again on analyBL ensemble mean T H U V 48hr

  10. SCORE of Ensemble Mean Analysis Forecast 72hr (against NCEP) woBL：代表無Blending guessBL：Guess Blneding analyBL：Analysis Blending analyBL_m：blending again on analyBL ensemble mean T H U V 72hr

  11. TC Track Forecasts - SINLAKU (1) woBL：代表無Blending guessBL：Guess Blneding analyBL：Analysis Blending analyBL_m：blending again on analyBL ensemble mean woBL guessBL analyBL analyBL_m from 2008/09/09 00UTC to 2008/09/13 00UTC

  12. TC Track Forecasts - SINLAKU (2) woBL：代表無Blending guessBL：Guess Blneding analyBL：Analysis Blending analyBL_m：blending again on analyBL ensemble mean from 2008/09/09 00UTC to 2008/09/13 00UTC

  13. TC Track Forecasts - HAGUPIT (1) woBL：代表無Blending guessBL：Guess Blneding analyBL：Analysis Blending analyBL_m：blending again on analyBL ensemble mean woBL guessBL analyBL analyBL_m from 2008/09/20 00UTC to 2008/09/23 00UTC

  14. TC Track Forecasts - HAGUPIT (2) woBL：代表無Blending guessBL：Guess Blneding analyBL：Analysis Blending analyBL_m：blending again on analyBL ensemble mean from 2008/09/20 00UTC to 2008/09/23 00UTC

  15. Summary • Guess-blending outperforms the other experiments, especially for the typhoon track • More detail analysis is undergoing to answer why guess blending is the best • The effect of the blending scheme is bounded by the GFS performance, the blending strategy should be re-evaluated as the GFS was improved, e.g. 2012 NCEP GFS

  16. Performance of the hybrid system • Single Observation Tests in 3DVAR-Hybrid - Tests of Tuning Factors in Hybrid • Localization Scale • Ensemble Covariance Weighting • Vertical localization • The use of the ensemble perturbation

  17. Single Observation Tests in 3DVAR-Hybrid • Observation Setting • Tuning Factors in Hybrid • Localization Scale • Ensemble Covariance Weighting • Temperature • innov = 1 K，obs_err = 1 K • Lon ~ 137.143 ( x = 150 ) • Lat ~ 28.2 ( y = 69 ) • Lev ~ 860 mb ( z = 11 )

  18. Localization Scale 200 km 375 km 750 km Ensemble System：EAKF Shade：Increment Green Line：Geopotential Height of Ensemble Forecast 6hr Mean.

  19. Ensemble Covariance Weighting Factor VAR 50% 75% Full Ensemble System：EAKF Shade：Increment Green Line：Geopotential Height of Ensemble Forecast 6hr Mean.

  20. Lev=11 (~860mb) Y-Z Plane EnSRF HGSI EAKF HVAR

  21. Lev=21 (~520mb) Y-Z Plane EnSRF HGSI EAKF HVAR

  22. Lev=30 (~250mb) Y-Z Plane EnSRF HGSI EAKF HVAR

  23. Summary • Localization scale of 200 km and 75% ensemble BE are used in the hybrid analysis. • Vertical localization in WRF VAR-Hybrid should be tuned. • The moisture field has the most dramatic change as including the ensemble BE.

  24. Evaluation of the 3DVAR-Hybrid • PART-1：Ensemble Members of EAKF without/with Blending • PART-2：3DVAR vs. Hybrid-EAKF • PART-3：Hybrid-WEPS vs. Hybrid-EAKF • PART-4：WEPS + EAKF

  25. Evaluation of the 3DVAR-Hybrid3DVAR vs. Hybrid-EAKF Experimental Design： • Model Version：CWB OP25 • Run：Partial Cycling • Domain：CWB WRF Domain 1 (45KM) • Period： 2008/09/09 00UTC ~ 2008/09/30 12 UTC T-PARC • Ensemble members of Hybrid-EAKF from “analyBL” case. • Verified against the NCEP GFS analysis and RAOB/dropsound

  26. 3DVARHEAKF ana H T U V

  27. 3DVARHEAKF H T U V 12hr

  28. 3DVARHEAKF H T U V 24hr

  29. 3DVARHEAKF H T U V 72hr

  30. One Month Mean 100 mb V U ana 12hr Shade： Difference ( 3DVAR - HEAKF ) Green Line：Geopotential Height of HEAKF 72hr

  31. One Month Mean 300 mb V U ana 12hr Shade： Difference ( 3DVAR - HEAKF ) Green Line：Geopotential Height of HEAKF 72hr

  32. One Month Mean 850 mb Q T ana 12hr Shade： Difference ( 3DVAR - HEAKF ) Green Line：Geopotential Height of HEAKF 72hr

  33. OBS Location

  34. 12hr 3DVARHEAKF T WIND Qs

  35. 24hr 3DVARHEAKF T WIND Qs

  36. 72hr 3DVARHEAKF T WIND Qs

  37. Summary • Compare to the 3DVAR, the forecast performance is slightly better in the hybrid system. • The major differences are high level wind, low level temperature and moisture. • The reason to cause the difference is not clear yet.

  38. Evaluation of the 3DVAR-HybridHybrid-WEPS vs. Hybrid-EAKF Spin-up Hybrid-EAKF Hybrid-WEPS ϐe : ϐb = 0.75 : 0.25

  39. SCORE of Ensemble Mean Analysis Forecast 72hr (against NCEP) HWPESHEAKF T 24hr ANA. 48hr 72hr

  40. Analysis Difference in SINLAKU Period T 850 mb 3DVAR HEAKF HWEPS HEAKF Qv

  41. SINLAKU Typhoon Track 3DVAR HEAKF HWEPS from 2008/08/09 12UTC to 2008/08/13 00UTC

  42. HAGUPIT Typhoon Track 3DVAR HEAKF HWEPS from 2008/08/20 00UTC to 2008/08/23 00UTC

  43. JANGMI Typhoon Track 3DVAR HEAKF HWEPS from 2008/08/24 12UTC to 2008/08/28 00UTC

  44. Evaluation of the 3DVAR-HybridHybrid-EAKF+WEPS vs. Hybrid-EAKF 32 members EAKF 52 members EAKF+WEPS 3DVAR-Hybrid 20 members WEPS

  45. Analysis Difference in SINLAKU Period 850 mb T, Qv HEKWP - HEAKF HEKWP T Qv HWEPS - HEAKF

  46. SCORE of Ensemble Mean Analysis Forecast 72hr (against DROPSONDES_SPECIFIC_HUMIDITY) HEKWP 12hr 24hr HEAKF 48hr 72hr

  47. 𝛽e 1.0 Lev=11 (~860mb) Full ensemble mode T Q V U HEKWP HEAKF HWEPS

  48. 𝛽e Lev=30 (~250mb) 1.0 Full ensemble mode T Q V U HEKWP HEAKF HWEPS

  49. Summary The difference between HEAKF, HWEPS, and HEKW is limited. HWEPS has the moisture spin-up issue.

More Related