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GRAPES Model Research Progresses at CMA

GRAPES Model Research Progresses at CMA. Chen D.H., Wang J.J., Shen X.S. et al. Numerical Weather Prediction Center China Meteorological Administration with thanks to our colleagues who contribute to the presentation. (The 4th THORPEX-ASIA Science Workshop and ARC-8 Meeting

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GRAPES Model Research Progresses at CMA

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  1. GRAPES Model Research Progresses at CMA Chen D.H., Wang J.J., Shen X.S. et al. Numerical Weather Prediction Center China Meteorological Administration with thanks to our colleagues who contribute to the presentation (The 4th THORPEX-ASIA Science Workshop and ARC-8 Meeting 30Oct.~3 Nov., 2012, Kunming, China )

  2. Outline • 1 Current Operational NWP Systems • 2 Efforts for improving GRAPES_GFS • 3 Progresses in GRAPES_VAR • 4 Implementation of GRAPES_TYM • 5 High resolution modeling activities • 6 Future Plan

  3. Data assimilation group Ensemble prediction Group Dynamic process group Typhoon prediction group Parallel computing group Observation data quality control group Physical process group Post process and products development group Regional model group System pre-operational test group Model version manage and information technology group Numerical Weather Prediction Center of CMA Director: Dr. WANG Jianjie Chief Engineer: Dr. CHEN Dehui Deputy-directors: Dr. GONG Jiandong and Dr. SHEN Xueshun System & Operation Division General Office R&D Division Model verification group The restructured organization of Numerical Prediction Center

  4. 1 Current Operational NWP Systems at CMA

  5. Current NWP Operational System in NMC In general, there were no big changes in the operational NWP systems

  6. GRAPES_TCM at Shanghai Typhoon Institute for East C.S.

  7. Fig: Topography of the domain of GRAPES_TCM • Configuration • Domain: E90º~E170º,N0º~N50º • Hor. Res.: 0.25ºx0.25º • Grids: 321x201 • V. res.: 31(ztop: 35000m) • Physics • Cumulus：KF-eta • PBL: YSU • Micro: NCEP cloud3 • LSM: SLAB scheme • Radia.: RRTM scheme (From Wang et al., 2010)

  8. Assessment of TC forecast methods • TRaP: extrapolating method based satellite-estimated precipitation • TAPT: tropical cyclone precipitation analogue method • GRAPES_TCM: numerical forecast (From Wang et al., 2010)

  9. Evolution of yearly mean track errors hrs hrs Bogus initialization + cumulus schemes (From Wang et al., 2012)

  10. GRAPES_TMM at Guangzhou Tropical Meteor. Institute for S. C. S.

  11. Domains of GRAPES_TMM 0.12o 0.36o 0.03o GRAPES_TMM(Tropical Meteorological Model), which is a three-nested model system (From Wan et al., 2010)

  12. + 5d Tro. weather forecast + T. Cyclone forecast + SST, sea flow forecast GRAPES_TMM (0.36o) MOM-sea flow model + 36 hrs Meso-scale forecast + S.C. fine w. forecast + sea waves, surge forecast Storm surge GRAPES_TMM (0.12o) Sea waves CHAF-1h-cyc + hourly rapid cycling anal. + 1~3 hrs nowcast + 3~12 hrs sort-term forecast GRAPES_TMM (0.03o) SWIFT-nwcst Radar-extrap. Since 2003， GZ began to operationally implement GRAPES_3DVAR, and then GRAPES_Meso for establishment of GRAPES_TMM, which is a three-nested model system: Global model (From Wan et al., 2010)

  13. Evolution of Yearly Mean Track Errors 48 hrs F. 24 hrs F. TL Grapes DAS/optimal use of data+ cumulus/PBL schemes Mean Track errors2012 24h 48h 72h 96.5 km 176.7km 235.6km (From Chen et al., 2012)

  14. Obs. Initial time: 00Z21June2012 F. length: 48hrs (From Chen et al., 2010) GRAPES_TMM

  15. Obs. Initial time: 00Z22June2012 F. length: 48hrs (From Chen et al., 2010) GRAPES_TMM

  16. Complicated Track of Prapiroon-2012 (From Chen et al., 2012)

  17. Inter-comparison to ECMWF, JMA, T639 and GRAPES_TMM (Initial Time: 12UTC, 00UTC) (From Chen et al., 2012)

  18. ImplementedGRAPES_Meso forecast system • GRAPES_Meso: operation in NMC • GRAPES_RUC: quasi-operation in NMC • GRAPES_TCM: operation in Shanghai I. • GRAPES_TMM: operation in Guangzhou I. • GRAPES_SDM: operation in CAMS Extended to GRAPES_HMM: Basin flooding height and volume Prediction • GRAPES Model • GRAPES_VAR

  19. Prediction of 6hrs precipi. accumulated Obs. Obs. GRAPES prediction Estimated on hydro. stat (Flooding height and volume; Initial time at 00UTC, 29th August, 2009. from Wang and Chen, 2010) (From Wang et Chen, 2012)

  20. 2 Efforts in improvements of GRAPES_GFS

  21. Sat. data First Quess Flow chart of GRAPES_GFS Pre-Processing Cycling Assimilation and Forecast GRAPES_3DVAR Quality Control Initial F. Conventnl. data Digital Filter Pre-Processing 10 d forecast GRAPES_GFS Quality Control

  22. Efforts in improving the forecast skill of GRAPES_GFS-toward operation- More satellite data ATOVS(NOAA-19,METOP,FY3) AIRS IASI Assimilation from pressure level to model grid space Improve model performance The dynamic core refinement: conservation issue Hybrid vertical coordinate: from terrain-following to terrain-following & Z Increase the vertical resolution and model top lift-up Tuning of physical processes Land surface: CoLM GWD SSO Microphysics + fractional cloud treatment Cumulus scheme tuning cloud-radiation interaction issue (From shen et al., 2012)

  23. GRAPES Global Forecast System(pre-operational) S. Hemis. N. Hemis. GRAPES-GFS 2011 GRAPES-GFS 2011 S. Hemis. N. Hemis. reforecasts for 200906~200908 (From shen et al., 2012)

  24. 3 Progresses in GRAPES_VAR

  25. Milestone of GRAPES variational data assimilation system 2001 Serial regional P3DVAR using pressure coordinate 2005 2005 Serial global P3DVAR Serial regional M3DVAR using height-based terrain following coordinate 2008 2009 Parallel global P3DVAR Serial regional 3DVAR operation running 2009 2005 2010 Quai-operation running Serial regional 4DVAR Serial global M3DVAR 2010 2010 Black: developed Blue: in progress Orange: Operation Red: in the future Serial global 4DVAR Paral. Reg. M3DVAR/4DVAR 2013 Parallel global 4DVAR (From Gong et al., 2012)

  26. GRAPES model level analysis (GRAPES_M3DVAR) and pressure level analysis (GRAPES_P3DVAR) (From Gong et al., 2012)

  27. OBS OBS OBS forward integration using non-linear model at the higher resolution outer loop observation increments observation increments observation increments cost function J forward integration using tangent-linear model at the lower resolution inner loop forcing term forcing term forcing term backward integration using adjoint model at the lower resolution minimization process GRAPES 4DVAR analysis incrementδx analysis results (From Zhang et al., 2012)

  28. Since Aug.2010 1-month running GRAPES_MESO V3.0 vs 4DVAR Model:GRAPES_MESO V3.0 Resolution: 15 km (502x330), 31 levels Time Step: 300 seconds Analysis System: GRAPES-4DVAR Outer loop resolution: The same resolution as the model Inner loop resolution: 45 km (167x111), 31 levels Physics process: LSP; MRF PBL; CUDU convection Outer loop: 1 iteration Obs: TEMP, SYNOP, AIREP, SHIPS Assimilation Window: [-3, 0] Analysis Time: 00UTC and 12UTC Background Fields:TL639L60 12-hours forecast Forecast Range: 48 hours (From Zhang et al., 2012)

  29. Light Heavy Torrential Moderate 1-month averaged Ts score of 24 hour precipitation forecast over whole China (From Zhang et al., 2012)

  30. Flow Chart of Cloud Analysis Scheme Data used: (1)NWP background; (2)Doppler Mosaic (3)reflectivity data; (4)sounding data collected per minute vertical interval; (5)surface obs; (6)Sat TBB; (7)Sat cloud total amount (From Zhu et al., 2012)

  31. Result of Cloud Cover background used Surface data used satellite tbb used satellite cta increment used radar reflectivity (From Zhu et al., 2012)

  32. 3h forecast With cloud analysis Without cloud analysis observation 6h forecast 12h forecast (From Zhu et al., 2012)

  33. The first hour precipitation 12：00-13：00 Hourly accumulated precipitation Hourly accumulated precipitation Without cloud analysis OBS With cloud analysis Hourly accumulated precipitation (From Zhu et al., 2012)

  34. 6h forecast composite reflectivity Without cloud analysis With cloud analysis Radar OBS (From Zhu et al., 2012)

  35. 4 Implementation of GRAPES_TYM

  36. 4.1 Quasi-operational implementation in NMC (From Ma et al., 2012)

  37. GRAPES_TYM (From Ma et al., 2012)

  38. Development of GRAPES_TYM for Typhoon intensity forecast Track error Minimum SLP error Maximum V10m error Mean track errors of GRAPES_TYM to GRAPES_TMM、GRAPES_TCM (From Ma et al., 2012)

  39. Case of 2012-13 KAITAK (From Ma et al., 2012)

  40. Case of 2012-13 KAITAK (From Ma et al., 2012)

  41. Case of 2012-13 KAITAK (From Ma et al., 2012)

  42. Case of 2012-11 HAIKUI (From Ma et al., 2012)

  43. Case of 2012-11 HAIKUI (From Ma et al., 2012)

  44. Case of 2012-11 HAIKUI (From Ma et al., 2012)

  45. Regional air-sea Coupled model Atmosphere Ocean Wind stress Heat flux Water flux GRAPES_TYM (0.15*0.15) Regional ECOM-si (0.25*0.25) Coupler (Oasis 3.0) SST Initial conditions/ Lateral boundary condition GFS Initial conditions/ boundary condition Global HYCOM 4.2 The Coupled Typhoon-Ocean Model (From Sun et al., 2012)

  46. Model domain ECOM: Horizontal resolution: 0.25°x 0.25° Domain：104°E~145°E, 8°N~43°N Provided：SST GRAPES: Horizontal resolution : 0.15°x 0.15° Domain：100°E~150°E, 5°N~45°N Provided: wind stress, solar flux, heat flux, water flux; Fluxes are exchanged every 360s. (From Sun et al., 2012)

  47. O A O A Oasis3 O A O OASIS3 coupler OASIS:OceanAtmosphereSeaIceSoil ---------Developed since 1991 in CERFACS • performs: • synchronisation of the component models • coupling fields exchange and interpolation • I/O actions • External library and module used: • NetCDF/parallel NetCDF • libXML, mpp_io, SCRIP • MPI1 and/or MPI2 (From Sun et al., 2012)

  48. SST forecasted by the coupled model ---Typhoon Muifa NCEP AVHRR + AMSR-E SST analysis at 08/08/11, 00UTC 72 hour forecasted SST by the coupled model Initialized at 00UTC 05 AUGUST, 2011 • The coupled model reproduces the sea surface cooling that • is closed well to the analysis. (From Sun et al., 2012)

  49. Typhoon Muifa – impact of coupling Tropical Cyclone Muifa (2011) INITIAL TIME 00:00 UTC, 5 August 2011 Black –observation Red-Uncoupled model Green-Coupled model • Too strong in GRAPES_tym • Coupling weaken the intensity (From Sun et al., 2012)

  50. Forecast verification for MUIFA Number of cases (21, 21,19,17) (From Sun et al., 2012)

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