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Center Report from CMA Short, Medium-range NWP Xueshun Shen Center for Numerical Weather Prediction China Meteorological Administration WGNE, Tokyo, Japan,

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Presentation on theme: "Center Report from CMA Short, Medium-range NWP Xueshun Shen Center for Numerical Weather Prediction China Meteorological Administration WGNE, Tokyo, Japan,"— Presentation transcript:

1 Center Report from CMA Short, Medium-range NWP Xueshun Shen Center for Numerical Weather Prediction China Meteorological Administration WGNE, Tokyo, Japan, 18-22 Oct. 2010

2 Outline Short & Medium-range NWP systems –Current status of operation system –New implementation –GRAPES_GFS: improvement toward operation –Research activities New organization for NWP development and operation

3 CMA headquarter decided: Freeze the current operational global NWP model: T L 639L60 Put most of the resources to improve and develop the GRAPES system Big transition of NWP-related policy Last WGNE presentation

4 Global Spectral Model (T L 639L60) Meso Scale Model (GRAPES_Meso) 10day Ensemble (T L 213L31) Typhoon deterministic & Ensemble forecast Forecast range Short- and Medium- range forecast Rainfall forecast Short-range forecast 10day forecastTyphoon forecast Forecast domain Global East Asia (8340km x 5480km) Global Horizontal resolution T L 639(0.28125 deg) 15km T213(0.5625 deg) Vertical levels / Top 60 0.1 hPa 31 10hPa 31 10 hPa Forecast Hours (Initial time) 240 hours (00 12 UTC) 72 hours (00, 12UTC) 240 hours (00 12 UTC) 15 members 120 hours (00, 06, 12, 18 UTC) 120 hours (00 12 UTC) 15 members Initial Condition Global Analysis (NCEP GSI) GRAPES_3DVAR NCEP SSI + Vortex relocation and Intensity adjustment with ensemble perturbations Perturbations are produced by Breeding-method Current NWP Operational models in CMA

5 1990-2010 NMC 500hPa Evolution of ACC of 500 hPa Z T42 T63T106T213T639 20021990 2008

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8 TC Mean Track Errors from JMA, CMA and EC global models

9 1993-2009 Limited area modelGlobal model Average Errors

10 GRAPES WRF Performance of operational GRAPES_Meso

11 New Implementation for pre-operational test GRAPES_MESO V3.0 with 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: T L 639L60 12-hours forecast Forecast Range: 48 hours Observatio n Data Data Processing GRAPES-4DVAR GRAPES-Model Background Analysis Fields Forecas t T L 639L60 Forecasts Si Since Aug.2010

12 1-month averaged Ts score of 24 hour precipitation forecast over whole China Light Moderate HeavyTorrential

13 48-hour accumulated precipitation 00Z03JUL2009-00Z04JUL2009 Initial time: 00Z02JUL2009 ObsCTRL 3DVAR4DVAR

14 dQv and w difference between 3DVAR and 4DVAR

15 Analysis increments along 115E, 4DVAR vs 3DVAR vector: dv;dw*20 (4DVAR); shaded: dqv (4DVAR) contour: dqv (3DVAR)

16 Setup of GRAPES global forecast system since Jul. 2007 GRAPES_GFS1.0 : medium-range global forecast –GRAPES_Global 50km L36 with model top at 10 hPa –GRAPES_3DVAR at 1.125 degree (global version) –6-hourly cycle –240 hour forecast (12UTC) –Assimilated Obs. GTS conventional data NOAA15 16 17 METEOSAT-9 & MTSAT AMV MODIS polar AMV Presented in last WGNE

17 GRAPES_GFS 1.0 global model SISL dynamical core with mass fixer Physics –Radiation: RRTMG LW( V 4.71)/SW( V 3.61) –Cumulus: Simplified Arakawa Schubert with modified entrainment and detrainment rates –Grid-scale precipitation: WSM-6 –Cloud: Xu & Randall diagnostic cloud –Land surface: CoLM –PBL: Modified Hong & Pan nonlocal PBL –Gravity wave drag: McFarlane 1987

18 GRAPES_GFS 1.0 global 3DVAR Incremental analysis Digital filter Recalculated background error covariance – NMC method 1.125x1.125 resolution, 17 standard pressure levels Bias correction scheme of satellite radiances based on simple linear regression (Harris and Kelly,2001): (1) 1000-300 hPa thickness, (2)200- 50 hPa thickness.

19 Efforts in improving the forecast skill of GRAPES_GFS -toward operation- Improve accuracy of initial values: data assimilation –ATOVS(NOAA-18,19,METOP,FY3) –GPS Reflectivity (COSMIC) –AIRS –IASI Improve model performance –Improve the accuracy of finite difference scheme, especially, for PGF calculation –Hybrid vertical coordinate: from terrain-following to terrain-following & Z –Tuning of physical processes Radiation: RRTMG Land surface: SLAB to CoLM GWD SSO replaces the effective roughness length Cumulus scheme tuning Radiative energy budget (cloud-radiation) –Improve the forecast of synoptic evolution and accuracy of local weather elements, particularly those which have large impact on East Asian weather

20 CAMS/CMA Time series of Innvoation and Residual: Height(Sonde) N.H. 500hPa 100hPa 850hPa

21 CAMS/CMA Using EC analysis as Reference(NCEP,GRAPES) 500hPa

22 old new Zonal mean temp. bias JJA (3d fcst.) By introducing the new radiation Scheme: RRTMG

23 New cloud cover parameterization New cloud water path parameterization Radiative effect of fractional cloud Radiative effect of cloud inhomogeneity Cloud-radiation interaction Improvement

24 Original scheme: binary cloud cloud=1, when QC+QI >1.0e-6 cloud=0, when QC+QI <1.0e-6 effective cloud drop radius: liquid 10µm ice 80µm New scheme Liang and Wang 1995 Cloud cover: Combine Slingo and Slingo 1991 and Kiehl et al. 1994 ; 4 cloud genus : convective cloud, anvil cirrus, inversion stratus and stratiform cloud. Fractional cloud cover and vertical cloud overlapping are considered effective cloud drop radius: liquid cloud: Savijarvi 1997 ice cloud: Kiehl et al. 1996 Cloud cover parameterization

25 Cloud cover compared with ISCCP satellite data Zonal mean total (TCC), high (HCC), middle (MCC) and low (LCC) cloud cover (%) of the ISCCP data (dashed) and the 5th day forecast by GRAPES using the ORG (thin solid) and NEW (thick solid) cloud scheme. Using ISCCP Simulator

26 CERESISCCPORGNEW Surface radiation balance (W m -2 ) Upwelling LW400405404408 Downwelling LW353357334365 Net LW-47-48-71-44 Upwelling LW CRF--201 Downwelling LW CRF--301632 Net LW CRF31281531 Upwelling SW22172521 Downwelling SW184177231182 Net SW162160206161 Upwelling SW CRF---5-3-7 Downwelling SW CRF---57-27-71 Net SW CRF-42-52-24-64 Comparison of surface radiation budget Errors reduce 14-471-8 Improvement on radiation budget

27 TOA radiation balance (W m -2 ) OLR244240278233 Net LW CRF27 335 Upwelling SW9610066106 Net SW235231265225 Net SW CRF-48-50-21-62 CERESISCCPORGNEW Comparison of TOA radiation budget Errors reduce from 29-343-8

28 Comparison of net flux

29 Statistical verification

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31 Low-level southerly bias GRAPES 3-day forecast To mitigate southerly bias: Introduce mountain blocking effect in GWD parameterization Introduce small scale orography-induced form drag

32 24/48hr V-wind difference (20090701-0720) By introducing the mountain blocking effect in GWD parameterization Partly alleviated the low-level southerly bias.

33 Impact of SSO on low-level wind prediction Partly alleviated the low-level southerly bias.

34 New version of GRAPES_GFS GRAPES_GFS1.2.0 : medium-range global forecast –GRAPES_Global 50km L36 with model top at 10 hPa –GRAPES_3DVAR at 1.125 degree –6-hourly cycle –240 hour forecast (00,12UTC) –Assimilated Obs. GTS conventional data NOAA15 16 17 18 19 METOP-2 AIRS FY-3 radiance METEOSAT-9 & MTSAT AMV MODIS polar AMV COSMIC Refraction More satellite data assimilated Improved model performance

35 grapes operation N. Hemsiphere S. Hemisphere ACC 500hPa Z JJA 2009

36 Near future upgrade activities Global GRAPES_3DVAR –Arakawa-A & pressure level to Model grid space analysis –RTTOV: RTTOV7->RTTOV93 –VarBC –More satellite data: IASI, GRAS etc. GRAPES global model –Hybrid vertical coordinate –Increase the vertical resolution –Conservative scalar SL advection: CSLR –Improve SL numerics SETTLS: Stable extrapolating two-time-level semi-Lagrangian scheme –Continuous improvement of model physics

37 Research Activities More satellite data Cloud microphysics parameterization Global GRAPES-4DVAR Yin-Yang GRAPES –To avoid polar singularity problem –More homogeneous grid size

38 Progress of GRAPES Yin-Yang grid The Helmholtz equation of GRAPES in the Yin-Yang overset grid are solved. The transplant of the whole GRAPES dynamical core is finished. However, some bugs exist and it need to be debuged in the next step. Helmholtz equation:

39 Finish the coding of tangent linear & adjoint model Finish the accuracy check Development of Global GRAPES_4DVAR aF(a) 10 -1 0.9993333298 10 -2 1.0025374212 10 -3 1.1345341283 10 -4 1.4307112695 10 -5 1.0000002065 10 -6 1.0000000959 10 -7 0.9999999878 10 -8 1.0000010258 10 -9 1.0000153359 10 -10 1.0000921071 10 -11 1.0007362130 10 -12 1.0499615795 wleft = 362468.822258871398 wright = 362468.822258874832 Adjoint code check

40 Office CMA Numerical Prediction Center Development Branch Operation Branch Staff 73

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