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 (et.al,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 4 610 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)

84. THANK YOU