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Short, Medium-range NWP

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Presentation on theme: "Short, Medium-range NWP"— Presentation transcript:

1 Short, Medium-range NWP
Center Report from CMA Short, Medium-range NWP Xueshun Shen Center for Numerical Weather Prediction China Meteorological Administration WGNE, Tokyo, Japan, 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:
Last WGNE presentation CMA headquarter decided: Freeze the current operational global NWP model: TL639L60 Put most of the resources to improve and develop the GRAPES system Big transition of NWP-related policy

4 Current NWP Operational models in CMA
Global Spectral Model (TL639L60) Meso Scale (GRAPES_Meso) 10day Ensemble (TL213L31) Typhoon deterministic & Ensemble forecast Forecast range Short- and Medium-range forecast Rainfall forecast Short-range forecast 10day forecast Typhoon forecast Forecast domain Global East Asia (8340km x 5480km) Horizontal resolution TL639( deg) 15km T213( deg) Vertical levels / Top 60 0.1 hPa 31 10hPa 10 hPa Forecast Hours (Initial time) 240 hours (00、12 UTC) 72 hours (00, 12UTC) 15 members 120 hours (00, 06, 12, 18 UTC) Initial Condition Global Analysis (NCEP GSI) GRAPES_3DVAR NCEP SSI + Vortex relocation and Intensity adjustment with ensemble perturbations Perturbations are produced by Breeding-method This table shows specifications of the models. 4

5 1990-2010年NMC全球数值预报模式北半球500hPa高度场距平相关系数
Evolution of ACC of 500 hPa Z 年NMC全球数值预报模式北半球500hPa高度场距平相关系数 This is the time series of performance of global model from 1990 to present. The capability has been improved. The correlation coefficient more than 0.6 could reach 7 days. 1990 2002 2008 T42 T63 T106 T213 T639 5

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

9 NMC Typhoon track numerical prediction errors (annual average)
年台风路径误差图 Average Errors This figure show the typhoon track forecast errors. Since 2003, we transfer typhoon track model from regional model to global model. The forecast skill has been improved year by year since that time. Now we output the 4 days track prediction. Limited area model Global model 9

10 Performance of operational GRAPES_Meso
WRF GRAPES

11 New Implementation for pre-operational test
Since Aug.2010 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: TL639L60 12-hours forecast Forecast Range: 48 hours Observation Data Data Processing GRAPES-4DVAR GRAPES-Model Background Analysis Fields Forecast TL639L60 Forecasts Si

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

13 48-hour accumulated precipitation
00Z03JUL Z04JUL2009 Initial time: 00Z02JUL2009 Obs CTRL 3DVAR 4DVAR

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 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(V4.71)/SW(V3.61) 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) hPa thickness, (2) 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 Time series of Innvoation and Residual: Height(Sonde) N.H.
100hPa 500hPa 850hPa

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

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

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

24 Cloud cover parameterization
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

25 Cloud cover compared with ISCCP satellite data
Using ISCCP Simulator 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.

26 Improvement on radiation budget Comparison of surface radiation budget
CERES ISCCP ORG NEW Surface radiation balance (W m-2) Upwelling LW 400 405 404 408 Downwelling LW 353 357 334 365 Net LW -47 -48 -71 -44 Upwelling LW CRF -- 2 1 Downwelling LW CRF 30 16 32 Net LW CRF 31 28 15 Upwelling SW 22 17 25 21 Downwelling SW 184 177 231 182 Net SW 162 160 206 161 Upwelling SW CRF -5 -3 -7 Downwelling SW CRF -57 -27 Net SW CRF -42 -52 -24 -64 Errors reduce 14-47 1-8

27 Comparison of TOA radiation budget
CERES ISCCP ORG NEW TOA radiation balance (W m-2) OLR 244 240 278 233 Net LW CRF 27 3 35 Upwelling SW 96 100 66 106 Net SW 235 231 265 225 Net SW CRF -48 -50 -21 -62 Errors reduce from 29-34 3-8

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. 24/48hr V-wind difference ( )

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

34 New version of GRAPES_GFS
Improved model performance GRAPES_GFS1.2.0 : medium-range global forecast GRAPES_Global 50km L36 with model top at 10 hPa GRAPES_3DVAR at 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

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

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: 38

39 Development of Global GRAPES_4DVAR
Finish the coding of tangent linear & adjoint model Finish the accuracy check a F(a) 10-1 10-2 10-3 10-4 10-5 10-6 10-7 10-8 10-9 10-10 10-11 10-12 Adjoint code check wleft = wright =

40 CMA Numerical Prediction Center
Staff:73 Development Branch Operation Branch Office 模式及应用检验 动力过程组 诊断与图形处理组 资料同化组 集合预报组 物理过程组 后处理与产品开发 并行计算组 观测资料预处理、质量控制 台风预报组 系统中试与运行 版本管理 与信息技术 区域模式组 系统集成与测试组

41 Thanks for your attention


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