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Progresses of BMB project in 2006

Progresses of BMB project in 2006. Y.-H. Kuo, X.Y. Huang, Y.-R. Guo, and Jiqin Zhong 28 June 2006. BMB B08 operational data assimilation system. NCAR provide Default BES and Software to derive BES. From NMC. Beijing local data. GPS TPW AWS Wind-profile …… Decoder OBS error.

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Progresses of BMB project in 2006

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  1. Progresses of BMB projectin 2006 Y.-H. Kuo, X.Y. Huang, Y.-R. Guo, and Jiqin Zhong 28 June 2006

  2. BMB B08 operational data assimilation system NCAR provide Default BES and Software to derive BES From NMC Beijing local data GPS TPW AWS Wind-profile …… Decoder OBS error Conventional data Decoder First guess WRF Converter Background Error Statistics (BES) Derivation ? WRF-Var 3D-Var cycle ? WRF Model Lateral boundaries WRF Converter 6h (3h) forecast From NMC Verification package ? NWP products Other applications 48-h forecast

  3. System configuration • Model domains D01: 27 km, 151×151×38 D02: 9 km, 142×184×38 D03: 3 km, 172×199×38 D04: 1 km, 211×211×38 (experimental) • Analysis scheme The latest released version of WRF 3D-Var (current OPR: MM5 3D-Var) D01 and D02: 6 hour cycles D03: 3 hour cycles D04: 1 hour cycles (experimental) • Forecast model The latest released version of WRF model (Current OPR: MM5 model) • Data Conventional data and Beijing local data

  4. Expected pre-operational* and operational** upgrades*** * Should follow the table Project started: March 2005 ** Depends on the pre-operational results *** This table is version 200512

  5. Progresses in 2006Huang, Zhong, and Guo • BES transfer from CV2 to CV5 • FG transfer from T213 to AVN • Local OBS data: Wind profiler, AWS, and GPS PW Obs data processing: wind profiler (u,v,w) to (ff,dd) AWS data SLP unit, observation error specifications GPS PW observation errors WRFVar code bug fixed: SFC_assi=2; rh_check for single OBS test • Forecast model transfer from MM5 to WRF Model settings : time-step, physics, interval for radiation Input files : MM5 REGRID to WRF_SI Static fields : Landuse table from MM5, 27-km single domain terrain. • Verification transfer to WRFVar-based VERIFY package WRFVar-based VERIFY applied to MM5 forecast.

  6. CV5 Background Error Statistics

  7. New BMB BE (cv_option=5) Psi first 5 global eigenvector Scale_length of control variances

  8. Single T obs test(O-B) = 1, so = 1.0 (b) (a) Increment and scale are too small (d) (c) Response in low level is unreasonable The T/V increments cross-section (West-East about 2000 km) for Single T OBS tests in BMB Domain1 (a) T response with old BE; (b) T response with new BE; (c) V response with old BE; (d) V response with new BE. The OBS value and error Is 1.0 located at x=76, y= 76, z=22. The domain is 151x151x37 with 27-km grid distance.

  9. Single T obs test(O-B) = 1, so = 1.0 Unreasonable U-wind response on δ=0.983 level (with old BE) Reasonable U-wind response on δ=0.983 level (with new BE)

  10. Single u obs test(O-B) = 1, so = 1.0 (b) (a) (c) (d) bad response (a) U response with old BE; (b) U response with new BE; (c) T response with old BE (onδ=0.983 level); (d) T response with new BE (onδ=0.983 level). The OBS value and error Is 1.0 located at x=76, y= 76, z=22.

  11. Conclusions • CV5 BES better than CV2 BES • FG=AVN12f better than T213, WRF12f equivalent to AVN12f • SFC_assi=1 equivalent to SFC_assi=2 • Upper air : WRF better than MM5, Surface : MM5 better than WRF • MM5 landuse table better than WRF landuse table.

  12. Experiment design • Exp.11: WRFVar, CV5 BES, FG=avn12f, MM5 model, passed=4, var_scaling=1, len_scaling=1, check_rh=2, sfc_assi=1, t_err=2C, p_err=100pa, v_err=1.1m/s rh_err=10% • Exp.15: Same as Exp.11 but sfc_assi=2 • Exp.17: Same as Exp.11 but with WRF model • Exp.18: Same as Exp.17 but FG= wrf12f

  13. Exp11 - mm5 sfc=1 anv12f Exp15 - mm5 sfc=2 anv12f Exp17 - wrf sfc=1 anv12f Exp18 - wrf sfc=1 wrf12f 优于 > ; 很接近但优于 ≥ ；相当 ≈；劣于 < ; 很接近但劣于≤ ； exp11 > exp15 exp17 ≈ exp18 exp11 > exp17

  14. Exp11 - mm5 sfc=1 anv12f Exp15 - mm5 sfc=2 anv12f Exp17 - wrf sfc=1 anv12f Exp18 - wrf sfc=1 wrf12f 优于 > ; 很接近但优于 ≥ ；相当 ≈；劣于 < ; 很接近但劣于≤ ； exp11 ≈ exp15 exp17 ≈ exp18 exp11 > exp17

  15. Exp11 - mm5 sfc=1 anv12f Exp15 - mm5 sfc=2 anv12f Exp17 - wrf sfc=1 anv12f Exp18 - wrf sfc=1 wrf12f 优于 > ; 很接近但优于 ≥ ；相当 ≈；劣于 < ; 很接近但劣于≤ ； exp11 > exp15 exp17 ≈ exp18 exp11 > exp17

  16. Exp11 - mm5 sfc=1 anv12f Exp15 - mm5 sfc=2 anv12f Exp17 - wrf sfc=1 anv12f Exp18 - wrf sfc=1 wrf12f 优于 > ; 很接近但优于 ≥ ；相当 ≈；劣于 < ; 很接近但劣于≤ ； exp11 < exp15 exp17 < exp18 exp11 < exp17

  17. Exp11 - mm5 sfc=1 anv12f Exp15 - mm5 sfc=2 anv12f Exp17 - wrf sfc=1 anv12f Exp18 - wrf sfc=1 wrf12f 优于 > ; 很接近但优于 ≥ ；相当 ≈；劣于 < ; 很接近但劣于≤ ； exp11 < exp15 exp17 ≈ exp18 exp11 < exp17

  18. Exp11 - mm5 sfc=1 anv12f Exp15 - mm5 sfc=2 anv12f Exp17 - wrf sfc=1 anv12f Exp18 - wrf sfc=1 wrf12f 优于 > ; 很接近但优于 ≥ ；相当 ≈；劣于 < ; 很接近但劣于≤ ； exp11 ≥ exp15 exp17 < exp18 exp11 < exp17

  19. Exp11 - mm5 sfc=1 anv12f Exp15 - mm5 sfc=2 anv12f Exp17 - wrf sfc=1 anv12f Exp18 - wrf sfc=1 wrf12f 优于 > ; 很接近但优于 ≥ ；相当 ≈；劣于 < ; 很接近但劣于≤ ； exp11 > exp15 exp17 ≈ exp18 exp11 < exp17

  20. Exp11 - mm5 sfc=1 anv12f Exp15 - mm5 sfc=2 anv12f Exp17 - wrf sfc=1 anv12f Exp18 - wrf sfc=1 wrf12f 优于 > ; 很接近但优于 ≥ ；相当 ≈；劣于 < ; 很接近但劣于≤ ； exp11 > exp15 exp17 ≤ exp18 exp11 > exp17

  21. Exp11 - mm5 sfc=1 anv12f Exp15 - mm5 sfc=2 anv12f Exp17 - wrf sfc=1 anv12f Exp18 - wrf sfc=1 wrf12f 优于 > ; 很接近但优于 ≥ ；相当 ≈；劣于 < ; 很接近但劣于≤ ； exp11 ≈ exp15 exp17 < exp18 exp11 < exp17

  22. Exp.19: the same to exp17, but radt=15, time_step=150, mp_physics=3

  23. Exp.17_newtbl: Same as the Exp17 but with landuse.tbl file used in MM5 model

  24. 27km domain TERRAIN DIRRERENCE: WRF-MM5_nest

  25. 27km domain INPUT DIFFERENCE (variable: sfcp): WRF-MM5

  26. 27km domain TERRAIN DIFFERENCE of MM5 : nest - single

  27. 27km domain TERRAIN DIRRERENCE: WRF-MM5_single

  28. Exp21: MM5 model with TERRAIN_DOMAIN1 of single configure

  29. Impact of the AWS data assimilation • According to the verification scores based on SYNOP and TEMP observations, AWS data have minor negative impact on analyses and neutral impact on forecasts. • According to the verification scores based on AWS observations, AWS data have clear positive impact on analyses and positive impact on forecasts of some variables, for example, mixing ratio.

  30. AWS assimilation Verification against the SYNOP Exp31, WRF MODEL , TEMP + SYNOP, Exp32, WRF MODEL , TEMP + SYNOP + METAR

  31. AWS assimilation Exp31, WRF MODEL , TEMP + SYNOP, Exp32, WRF MODEL , TEMP + SYNOP + METAR

  32. AWS assimilation Verification against AWS Exp31, WRF MODEL , TEMP + SYNOP, Exp32, WRF MODEL , TEMP + SYNOP + METAR

  33. Impact of the GPS PW assimilation • Exp31, WRF MODEL , TEMP + SYNOP, • Exp33, WRF MODEL , TEMP + SYNOP + GPSPW with default error of 0.2 cm • WRFVar-based VERIFY can be directly applied to non-conventional observations, such as GPS PW. • No matter RMS or Bias, GPS PW assimilation has the positive impact.

  34. The work remained • Nested run experiments (27/9/3km) for mesoscale data assimilation, 27-km single domain is not enough BES derivation for high resolution, or interpolate_stats Tuning and multiple outer-loops • Cycling mode experiments Warm-start for operational DFI implementation (MM5/WRF) • Verification package WRFVar-based 3D-VERIFY, Precipitation-VERIFY • Case selection Convective case in summer season • Real time GPS data processing and assimilation Implementation of ZTD assimilation in WRFVar

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