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

4 1 Current Operational NWP Systems at CMA

5 Current NWP Operational System in NMC
Models specified Global Spectral Model (TL639L60) 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 Global China/East Asia (8340km5480km) Horizontal resolution TL639( o) 15km T213 ( o) Vert. levels / Top 60 0.1hPa 33 10hPa 31 Forecast hours (initial time) 240hours (00, 12UTC) 72 hours (00, 12UTC) 15members 240hours+BGS 15members 10hPa Initialization Global GSI (NCEP) GRAPES_3VAR Initial Perturb. by BGM BGM+NCEP SSI + vortex relocation, intensity adjustments 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 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 (0.36o) MOM-sea flow model + 5d Tro. weather forecast + T. Cyclone forecast + SST, sea flow forecast GRAPES_TMM (0.12o) Storm surge + 36 hrs Meso-scale forecast + S.C. fine w. forecast + sea waves, surge forecast Sea waves GRAPES_TMM (0.03o) CHAF-1h-cyc + hourly rapid cycling anal. + 1~3 hrs nowcast + 3~12 hrs sort-term forecast SWIFT-nwcst Radar-extrap. (From Wan et al., 2010)

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

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

15 Initial time: 00Z22June2012 F. length: 48hrs
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 Implemented GRAPES_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 (Flooding height and volume; Initial time at 00UTC, 29th August, 2009.
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 Flow chart of GRAPES_GFS
Sat. data First Quess Pre-Processing and Forecast Assimilation Cycling GRAPES_3DVAR Quality Control Initial F. Conventnl. data Digital Filter Pre-Processing GRAPES_GFS 10 d forecast 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. ACC>0.6 2007 2008 2009 2011 N.H 5.5 5.8 6 6.5 S.H 4.0 4.7 5.3 6.9 GRAPES-GFS 2011 GRAPES-GFS 2011 N. Hemis. S. Hemis. reforecasts for ~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 Paral. Reg. M3DVAR/4DVAR Serial global 4DVAR 2013 Parallel global 4DVAR (From Gong et al., 2012)

26 GRAPES model level analysis (GRAPES_M3DVAR) and pressure level analysis (GRAPES_P3DVAR)
Vertical coordinate Charney-phillips , Z terrain following, vertical stagger grid Pressure level analysis, no stagger grid Horizontal grid Arakawa C, horizontal stagger grid Arakawa A grid Analysis variable Model 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 3rd 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 GRAPES 4DVAR OBS OBS OBS outer loop inner loop minimization process
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) 27 27

28 1-month running GRAPES_MESO V3.0 vs 4DVAR Model: GRAPES_MESO V3.0
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 1-month averaged Ts score of 24 hour precipitation forecast over whole China
Light Moderate Heavy Torrential (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 used radar reflectivity
Result of Cloud Cover background used Surface data used satellite tbb used satellite cta increment used radar reflectivity (From Zhu et al., 2012)

32 Without cloud analysis
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 With cloud analysis Without cloud analysis OBS 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 Model GRAPES_MESO3.0 Domain 90º~171ºE,0º~51ºN Grid points
541341 Initial time 00UTC、12UTC Initialization Bogus-relocated+intensity-adjustment F. lenth 72hrs Interval-out 3hrs Physical schemes Micro:WSM6 Cumul:SAS PBL:YSU LSM:SLAB (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 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 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: GRAPES: 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 NetCDF/parallel NetCDF
OASIS3 coupler OASIS: Ocean Atmosphere Sea Ice Soil Developed since 1991 in CERFACS performs: synchronisation of the component models coupling fields exchange and interpolation I/O actions A O Oasis3 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)

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 Typhoon SINLAKU – impact of coupling
Tropical Cyclone SINLAKU (2008) INITIAL TIME 12:00 UTC, 12 September 2008 NCEP AVHRR + AMSR-E SST analysis at 15/09/08, 00UTC Too strong in GRAPES_TYM model Coupling weaken the intensity (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 Intensity forecast of Nine TC in 2011 Number of cases (72,72,56,44)
Minimum sea level pressure forecast GRAPES_tym Minimum sea level pressure forecast Coupled model Maximum wind forecast GRAPES_tym Maximum wind forecast Coupled model The coupled model does not work on the intensity forecast which originally weaker than the observation in the GRAPES TC model, but could improve the forecast which originally strong. (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: with It 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: G.C.S. (Gal-Chen and Sommerville, 1974) SLEVE1 (Schar, 2002) h*: scale of ref-topography; h*1 and h*2: large and small-scale of ref-topogr. SLEVE2 SLEVE: Smooth LEvel VErtical coordinate; (similar to Klemp, 2011) “n>2”: an empirical number; zc : a reference height from which the coordinate surface becomes horizontal. COS (From Li et Chen, 2012)

60 1D test design 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 with (From Li et Chen, 2012)

61 Errors of PGF calculation induced by using TF coordinates
G.C.S SLEVE SLEVE COS top This slide shows that the errors of PGF calculation on different vertical levels over an artificial terrain along x-axe. The errors with SLEVE1, SLEVE2 and COS are smaller than those with GCS. Especially, the errors with COS coordinate are nearly zero from the lower level L20! bottom On different vertical levels: L2, L10, L20, L30 and L40 from bottom to top (From Li et Chen, 2012)

62 Relatively Reduced Errors: SLEVE1(SLEVE2, COS) against GCS
R.R.E. is defined as: Vertical levels SLEVE1 SLEVE2 COS L40 67% 99% 100% L30 62% L20 51% L10 31% 95% 75% L2 4% 30% 2% This table shows that the Relatively Reduced Errors with SLEVE1, SLEVE2 and COS against those with GCS coordinate. It can be seen from this table that more than 99% of the errors can be reduced since L20. (From Li et Chen, 2012)

63 Analysis density distribution :
2D test design (cont.) Initial wind: Analysis density distribution : before mount over after mount Given a initial flow from left to right, the air mass is advected from upstream of mountain to downstream of mountain. Analyzing the density at 3 reference-time: 0s (before mount), 5000s (over mount) and 10000s (after mount) with 4 different coordinates. flow from L to R density distribution (From Li et Chen, 2012)

64 Advection test : air mass moves over a topographic obstacle
GCS SLEVE1 SLEVE2 COS-zc=15km It can be seen that the results with COS coordinate are the best in comparison to those with other coordinates: the simulations with COS-zc=10km are well closed to those without topography: before, over and after the mountain! COS-zc=10km without topography left:density distribution at 0s,5000s,10000s right:the errors at 10000s after mountain (From Li et Chen, 2012)

65 The errors of the simulations
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 计算误差 12 and l with the new TF coordinates are much smaller than those with traditional GCS coordinate. Especially, 12 and l with the COS-zc=10km are nearly zero! 积分时间 left : temporal evolution of right : temporal evolution of (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. 2012 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 Obs. Beijing 24-hour accumulated rainfall GRAPES_Meso-3km ECMWF Fcst. Max=341mm/24hr (From Huanget al., 2012)

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

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) 72

73 3D advection results alpha=0. Instant image on the Yang grid 5 9 1 4 6
10 8 2 alpha=45. day12 7 3 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. alpha=90. (From Peng et al., 2012) 73

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 Solution points Constraint points 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 (From Li et al., 2012)

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

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

77 6 Future Plan

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

79 Global GRAPES_VAR Research & Operation Plan
GRAPES P3DVAR a new fixed version (res:1 deg—>0.5deg) FY-3A MWTS、FY-2E IR AMV GRAPES-M3DVAR FY3-B MWTS、FY3-A/B MWHS、FY-2D/F IR AMV GRAPES-M3DVAR oper. run NPP satellite data used More data from FY2/FY3 GRAPES-4DVAR real-time running 2012 2013 2014 2015 2020 GRAPES P3DVARM3DVAR; Conventional data QC re-check Data Preprocessing system re-design Satellite vertical sounding high level channel used GRAPES-4DVAR develop; New version data preprocessing used GRAPES-4DVAR real-time trial More FY satellite data (From Gong et al., 2012)

80 Regional GRAPES_VAR research and operation plan
GRAPES 3DVAR parallel version operation Radar VAD、AWS humidity data operational used GPS/PW oper used. Cloud analysis oper used GRAPES_RAFS quasi-oper. running 2012 2013 2014 2015 2020 3DVAR system improvement; B matrix re-estimate and tuning Conventional data QC recheck. Pressure-wind balance re-tunning; Eliminate boundary noise in B matrix; GPS/PW QC; Cloud analysis improvement; 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 Future HR GRAPES -DA and Prediction System
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 data assimilation control forecast Ensemble covariance Re-center EnKF analysis ensemble to control analysis …… First guess forecast 3DVAR observations Innovation GRAPES-DFL 0 20m 40m Multi model EPS H.R. Model DAS: EnKF/3DVAR Hybrid DA System; Multi Models: WRF, GRAPES_Meso (modified from talk of Xue et al., 2012)

82 GRAPES_3DVAR(or 4DVAR)-Hybrid: Method
Extended control variable method (Lorenc 2003; Wang 2010): Extra term associated with extended control variable Extra increment associated with ensemble (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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