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GRAPES Model Research Progresses at CMA Chen D.H., Wang J.J., Shen X.S. et al. Numerical Weather Prediction Center China Meteorological Administration.

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Presentation on theme: "GRAPES Model Research Progresses at CMA Chen D.H., Wang J.J., Shen X.S. et al. Numerical Weather Prediction Center China Meteorological Administration."— Presentation transcript:

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

4 1 Current Operational NWP Systems at CMA

5 Models specified Global Spectral Model (T L 639L60) Meso Scale Model (GRAPES_Meso) Global Ensemble (T213L31) Typhoon Ensemble forecast Forecast Range Global Medium- range forecast Regional short- range foreecast 10 day forecast Typhoon forecast Forecast domain GlobalChina/East Asia (8340km 5480km) Global Horizontal resolution T L 639( o )15kmT213 ( o ) Vert. levels / Top hPa 33 10hPa 31 10hPa Forecast hours (initial time) 240hours (00, 12UTC) 72 hours (00, 12UTC) 240hours (00, 12UTC) 15members 240hours+BGS (00, 12UTC) 15members 33 10hPa Initialization Global GSI (NCEP) GRAPES_3VARInitial Perturb. by BGM BGM+NCEP SSI + vortex relocation, intensity adjustments 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 PhysicsPhysics –Cumulus KF-eta –PBL: YSU –Micro: NCEP cloud3 –LSM: SLAB scheme –Radia.: RRTM scheme Fig: Topography of the domain of GRAPES_TCM ConfigurationConfiguration –Domain: E90º~E170º,N0º~N50º –Hor. Res.: 0.25ºx0.25º –Grids: 321x201 –V. res.: 31(ztop: 35000m) (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 (From Wang et al., 2012) Bogus initialization + cumulus schemes

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

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

12 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 GRAPES_TMM (0.03 o ) CHAF-1h-cyc + hourly rapid cycling anal. + 1~3 hrs nowcast + 3~12 hrs sort-term forecast SWIFT-nwcst Radar-extrap. GRAPES_TMM (0.12 o ) Storm surge + 36 hrs Meso-scale forecast + S.C. fine w. forecast + sea waves, surge forecast Sea waves GRAPES_TMM (0.36 o ) MOM-sea flow model + 5d Tro. weather forecast + T. Cyclone forecast + SST, sea flow forecast (From Wan et al., 2010)

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

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

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

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 Implemented GRAPES_Meso forecast system 1.GRAPES_Meso: operation in NMC 2.GRAPES_RUC: quasi-operation in NMC 3.GRAPES_TCM: operation in Shanghai I. 4.GRAPES_TMM: operation in Guangzhou I. 5.GRAPES_SDM: operation in CAMS GRAPES Model GRAPES_VAR Extended to GRAPES_HMM: Basin flooding height and volume Prediction

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

20 2 Efforts in improvements of GRAPES_GFS

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

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

24 3 Progresses in GRAPES_VAR

25 Milestone of GRAPES variational data assimilation system Serial regional P3DVAR using pressure coordinate Serial global P3DVARSerial regional M3DVAR using height-based terrain following coordinate Serial regional 4DVARSerial global M3DVAR Serial global 4DVAR Parallel global P3DVAR Parallel global 4DVAR Paral. Reg. M3DVAR/4DVAR Black: developed Blue: in progress Orange: Operation Red: in the future Quai-operation running 2009 Serial regional 3DVAR operation running 2009 (From Gong et al., 2012)

26 GRAPES model level analysis (GRAPES_M3DVAR) and pressure level analysis (GRAPES_P3DVAR) GRAPES_M3DVARGRAPES_P3DVAR Vertical coordinate Charney-phillips, Z terrain following, vertical stagger grid Pressure level analysis, no stagger grid Horizontal gridArakawa C, horizontal stagger gridArakawa A grid Analysis variableModel state variable: π, θ, u, v, q Partial model state variable Φ, u, v, RH(q) Control variable Ψ, χ, Πu, RH/q/RH* Ψ, χ, Φu, RH/q Observation operator Physical variable transform, horizontal bi-linear interpolation, vertical linear interpolation or/and 3 rd spline interpolation Control variable transform order Vertical EOF transform firstly, then horizontal spectral transform Horizontal spectral transform firstly, then vertical EOF transform (From Gong et al., 2012)

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

28 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: T L 639L60 12-hours forecast Forecast Range: 48 hours Since Aug.2010 (From Zhang et al., 2012)

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

30 Flow Chart of Cloud Analysis Scheme (From Zhu et al., 2012)

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

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

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

34 6h forecast composite reflectivity Radar OBS Without cloud analysis With cloud analysis (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 ModelGRAPES_MESO3.0 Domain 90º~171ºE 0º~51ºN Grid points Initial time 00UTC 12UTC InitializationBogus- relocated+intensity- adjustment F. lenth72hrs Interval-out3hrs Physical schemes Micro WSM6 Cumul SAS PBL YSU LSM SLAB GRAPES_TYM (From Ma et al., 2012)

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

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

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

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

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

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

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

45 4.2 The Coupled Typhoon-Ocean Model Regional air-sea Coupled model Initial conditions/ Lateral boundary condition GFS Initial conditions/ boundary condition Global HYCOM Atmosphere Ocean GRAPES_TYM (0.15*0.15) Regional ECOM-si (0.25*0.25) Coupler (Oasis 3.0) SST Wind stress Heat flux Water flux (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 performs: synchronisation of the component models coupling fields exchange and interpolation I/O actions A A A O O O O Oasis3 OASIS: Ocean Atmosphere Sea Ice Soil Developed since 1991 in CERFACS OASIS3 coupler 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 The coupled model reproduces the sea surface cooling that is closed well to the analysis. 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 (From Sun et al., 2012)

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

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

51 Typhoon MUIFA intensity forecast Minimum sea level pressure forecast GRAPES_tym Minimum sea level pressure forecast Coupled model Maximum wind forecast GRAPES_tym Maximum wind forecast Coupled model (From Sun et al., 2012)

52 Tropical Cyclone SINLAKU (2008) INITIAL TIME 12:00 UTC, 12 September 2008 Too strong in GRAPES_TYM model Coupling weaken the intensity Typhoon SINLAKU – impact of coupling NCEP AVHRR + AMSR-E SST analysis at 15/09/08, 00UTC (From Sun et al., 2012)

53 Forecast verification for Typhoon SINLAKU Number of cases (21, 21,19,17) (From Sun et al., 2012)

54 Forecast verification of Nine TC in 2011 Number of cases (72,72,56,56,49,44,44) (From Sun et al., 2012)

55 Minimum sea level pressure forecast GRAPES_tym Minimum sea level pressure forecast Coupled model Maximum wind forecast GRAPES_tym Maximum wind forecast Coupled model Intensity forecast of Nine TC in 2011 Number of cases (72,72,56,44) (From Sun et al., 2012)

56 5 High resolution modeling activities

57 5.1 High Resolution Modeling Activities at CMA Based on GRAPES_Meso Recent activities Vertical coordinate from terrain-following Z to hybrid coordinate (Schar, 2002) Inclusion of thermal expansion effect in continuity equation Improve the interpolation accuracy in physics-dynamics interface Refinement of 2-moment microphysics scheme Some bug fix in land surface scheme Refinement of back ground error covariance in 3DVAR

58 Modification of TF coordinate In order to design a new TF coordinate, we rewrite the formulation of Gal-Chen and Sommerville (1975) in a common formulation: withIt is a decaying coefficient of the coordinate surface with height. It is possible to use different b to accelerate the decaying. (From Li et Chen, 2012)

59 New TF coordinates The different decaying coefficients b can be defined as: (Gal-Chen and Sommerville, 1974) (similar to Klemp, 2011) n>2: an empirical number; z c : a reference height from which the coordinate surface becomes horizontal. (Schar, 2002) h*: scale of ref-topography; h* 1 and h* 2 : large and small-scale of ref-topogr. G.C.S. SLEVE1 SLEVE2 COS (From Li et Chen, 2012)

60 Test Objective to compare the errors of PGF calculation of four coordinates in rest atmosphere over an artificial terrain. Test design Reference rest atmosphere Classical algorithm used for PGF calculation 1D test design with (From Li et Chen, 2012)

61 G.C.S SLEVE1 SLEVE2 COS Errors of PGF calculation induced by using TF coordinates bottom top On different vertical levels: L2, L10, L20, L30 and L40 from bottom to top (From Li et Chen, 2012)

62 Vertical levels SLEVE1SLEVE2COS L40 67%99%100% L30 62%99%100% L20 51%99% L10 31%95%75% L24%30%2% R.R.E. is defined as: Relatively Reduced Errors: SLEVE1(SLEVE2, COS) against GCS (From Li et Chen, 2012)

63 Initial wind: Analysis density distribution before mount overafter mount 2D test design (cont.) flow from L to Rdensity distribution (From Li et Chen, 2012)

64 left density distribution at 0s,5000s,10000s right the errors at 10000s after mountain Advection test : air mass moves over a topographic obstacle GCS SLEVE1 SLEVE2 COS-z c =15km COS-z c =10km without topography (From Li et Chen, 2012)

65 left : temporal evolution of Defining two parameters as following, according to Williamson (,1992) is numerical solution is analytical solution Gal.C.S SLEVE1 SLEVE2 COS(10KM) COS(15KM ) integral time error Gal.C.S COS(15KM) COS(10KM) SLEVE2 SLEVE1 integral time right : temporal evolution of The errors of the simulations (From Li et Chen, 2012)

66 The preliminary results with new TF coordinates in GRAPES_Meso The preliminary results with regional GRAPES (15km) are quite encouraging: Monthly mean of 24h forecast of geopotential height at 100hPa (From Li et Chen, 2012)

67 The torrential rain-storm occurred on 21 Jul in Beijing

68 24 h accumulated precipitation from 00UTC 21 Jul to 00UTC 22 Jul The torrential rain-storm occurred on 21 Jul in Beijing area: the worst the city has seen in more than 60 years, dumped an average of 215 millimeters of rain in 16 hours. Hebeizhen, a town in the suburban district of Fangshan (South-West), saw 460 millimeters for the same period. (From Chen et al., 2012)

69 Heavy rainfall event on Jul.21/2012 Beijing Mean=190.3mm/24hr Max=460mm/24hr 00z21Jul z22Jul2012 Initial: global analysis BC: global forecast Grid size:3km Physics: - microphysics: WSM6 - radiation RRTM S&L - pbl MRF - land surface NOAH Initial: global analysis BC: global forecast Grid size:3km Physics: - microphysics: WSM6 - radiation RRTM S&L - pbl MRF - land surface NOAH Fcst. 24-hour accumulated rainfall Max=341mm/24hr Beijing Obs. GRAPES_Meso-3km ECMWF (From Huanget al., 2012)

70 Comparison of precipitation every 6-hour forecasts against Obs. Obs.0-6hrObs.6-12hr Obs.12-18hr Obs.18-24hr Fcst.0-6hrFcst.6-12hrFcst.12-18hr Fcst.18-24hr (From Huanget al., 2012) (GRAPES_Meso-3km)

71 5.2 other Research activities at CMA –GRAPES Yin-yang dynamic core –SV-based GRAPES ensemble forecast system –New algorithms of dynamic core

72 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: (From Peng et al., 2012)

73 3D advection results alpha=0. alpha=90. alpha=45. Instant image on the Yang grid The tracer follow the wave motion and undergo Three oscillations in the vertical direction. After one revolution(12 days), the tracer is back to the initial state. day (From Peng et al., 2012)

74 High order Multi-moment Constrained finite Volume (MCV) method We define the moments within single cell, i.e. the cell-averaged value, the point-wise value and the derivatives of the field variable Constraint conditons: Approximate Riemann solvers The unknowns (solution points) are updated in a fourth order mcv scheme, for example, The same in multi-dimension, for example, y direction Solution points Constraint points (From Li et al., 2012)

75 Height-based terrain-following vertical coordinate (Gal-chen & Somerville 1975) is used. is transformation Jacobian. MCV4 results A nonhydrostatic atmospheric governing equation sets in the Cartesion system (From Li et al., 2012)

76 Discontinuous Galerkin results (Giraldo & Restelli, JCP, 2008) Fourth order MCV results Linear nonhydrostatic mountain case Analytic solution: red dash line zero contours (From Li et al., 2012)

77 6 Future Plan

78 Strategic Plan ~ 2015 GRAPES_GFS 3DVAR, 10 d Δx=25km, L60 GRAPES_Meso + RAFS GRAPES_TYM GRAPES_EPS 3DVAR, 24-60h Δx=3-10km, L45 3DVAR/Bogus, 72h, Δx=10km, L45 SV+Sto.Phy, 10d Δx=50km, L60

79 GRAPES P3DVAR a new fixed version (res:1 deg>0.5deg) FY-3A MWTS FY-2E IR AMV Global GRAPES_VAR Research & Operation Plan GRAPES P3DVAR M3DVAR; Conventional data QC re-check Data Preprocessing system re-design GRAPES- M3DVAR FY3-B MWTS FY3-A/B MWHS FY-2D/F IR AMV Satellite vertical sounding high level channel used GRAPES-M3DVAR oper. run NPP satellite data used More data from FY2/FY3 GRAPES- 4DVAR real- time running GRAPES-4DVAR develop; New version data preprocessing used GRAPES- 4DVAR real-time trial More FY satellite data (From Gong et al., 2012)

80 GRAPES 3DVAR parallel version operation Regional GRAPES_VAR research and operation plan DVAR system improvement B matrix re-estimate and tuning Conventional data QC recheck. Radar VAD AWS humidity data operational used Pressure-wind balance re-tunning; Eliminate boundary noise in B matrix GPS/PW QC Cloud analysis improvement GPS/PW oper used. Cloud analysis oper used GRAPES_RAFS quasi-oper. running Radar precipitation heating profile Radar reflectivity QC Wind profile QC Continue GRAPES_4DVAR? GRAPES-4DVAR real-time test; Radar data QC Unified global / regional 3DVAR Satellite data used in regional model Regional GRAPES_VAR: 4DVAR or 3DVAR+EnKF? (From Gong et al., 2012)

81 member 1 forecast member 2 forecast member k forecast GRAPES forecast 3DVAR-ECV EnKF GRAPES analysis EnKF analysis k EnKF analysis 2 EnKF analysis 1 member 1 analysis member 2 analysis member k analysis member 1 forecast member 2 forecast data assimilation control forecast Ensemble covariance Re-center EnKF analysis ensemble to control analysis Re-center EnKF analysis ensemble to control analysis …… First guess forecast 3DVAR observations Innovation member k forecast GRAPES- DFL 0 20m 40m GRAPES -DFL 0 20m 40m GRAPES- DFL 0 20m 40m GRAPE S-DFL 0 20m 40m Future HR GRAPES -DA and Prediction System Multi model EPS DAS: EnKF/3DVAR Hybrid DA System; Multi Models: WRF, GRAPES_Meso H.R. Model (modified from talk of Xue et al., 2012)

82 82 Extended control variable method (Lorenc 2003; Wang 2010): Extra term associated with extended control variable Extra increment associated with ensemble GRAPES_3DVAR(or 4DVAR)-Hybrid: Method (modified from talk of Xue et al., 2012)

83 Blending Nowcast with GRAPES_HR Implement a very-short term forecast system with 3km resolution based on multi-model ensemble including GRAPES_Meso, WRF and ARPS (collaborate with Nanjing University) Data assimilation: hybrid DA (3DVAR+EnKF) (collaborate with Ming Xue, Oklahoma Univ.) (from Chen et al., 2012)


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