grapes model research progresses at cma

GRAPES Model Research Progresses at CMA

Chen D.H., Wang J.J., Shen X.S. et al.Numerical Weather Prediction CenterChina Meteorological Administration

with thanks to our colleagues who contribute to the presentation

(The 4th THORPEX-ASIA Science Workshop and ARC-8 Meeting30 Oct.~3 Nov., 2012, Kunming, China )

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

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

1 Current Operational NWP Systems at CMA

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(8340km5480km

)

Global

Horizontal resolution

TL639(0.28125o) 15km T213 (0.5625 o)

Vert. levels / Top

600.1hPa

3310hPa

3110hPa

Forecast hours (initial

time)

240hours(00, 12UTC)

72 hours(00, 12UTC)

240hours(00, 12UTC) 15members

240hours+BGS(00, 12UTC)15members

3310hPa

Initialization

Global GSI(NCEP)

GRAPES_3VAR Initial 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

GRAPES_TCM at Shanghai Typhoon Institute for East C.S.

• 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)

Assessment of TC forecast methods

• TRaP: extrapolating method based satellite-estimated precipitation

• TAPT: tropical cyclone precipitation analogue method

• GRAPES_TCM: numerical forecast

0

0. 05

0. 1

0. 15

0. 2

0. 25

0. 3

0. 35

0mm 0. 1mm 10mm 25mm 50mm 100mmET

S

GRAPES_TCM

TRaP

TAPT

(From Wang et al., 2010)

Evolution of yearly mean track errors

hrs hrs

(From Wang et al., 2012)

Bogus initialization + cumulus schemes

GRAPES_TMM at Guangzhou Tropical Meteor. Institute for

S. C. S.

Domains of GRAPES_TMM

0.36o

0.12o

0.03o

GRAPES_TMM(Tropical Meteorological Model), which is a three-nested model system

(From Wan et al., 2010)

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(0.03o)

CHAF-1h-cyc + hourly rapid cycling anal.+ 1~3 hrs nowcast

+ 3~12 hrs sort-term forecastSWIFT-nwcst

Radar-extrap.

GRAPES_TMM(0.12o)

Storm surge + 36 hrs Meso-scale forecast+ S.C. fine w. forecast

+ sea waves, surge forecastSea waves

GRAPES_TMM(0.36o)

MOM-sea flow model

+ 5d Tro. weather forecast+ T. Cyclone forecast

+ SST, sea flow forecast

(From Wan et al., 2010)

Mean Track errors2012 24h 48h 72h

96.5 km 176.7km 235.6km

050

100150200250300350400450

2003 2004 2005 2006 2007 2008 2009 2010 2011逐年变化

(km)

路径

误差

24小时预报 48小时预报

TL Grapes

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

Initial time: 00Z21June2012F. length: 48hrs

Obs.

GRAPES_TMM


http://113.108.192.91/qx/show_photo.asp?photo_name=GIF/GRAPES_36km/2012062100/GZLMSufPCP2048.gif

Initial time: 00Z22June2012F. length: 48hrs

Obs.

GRAPES_TMM


http://113.108.192.91/qx/show_photo.asp?photo_name=GIF/GRAPES_36km/2012062300/GZLMSufPCP2048.gif

Complicated Track of Prapiroon-2012(From Chen et al., 2012)

Inter-comparison to ECMWF, JMA, T639 and GRAPES_TMM (Initial Time: 12UTC, 00UTC)


ImplementedGRAPES_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 ModelGRAPES_VAR

Extended to GRAPES_HMM: Basin flooding height and volume Prediction

(Flooding height and volume; Initial time at 00UTC, 29th August, 2009. from Wang and Chen, 2010)

Prediction of 6hrs precipi. accumulated

Obs.

Estimated on hydro. stat

Obs.GRAPES prediction

(From Wang et Chen, 2012)

2 Efforts in improvements of GRAPES_GFS

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

Cyclin

g

Assim

ilation

an

d F

orecast

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)

N. Hemis. S. Hemis.

forecast verification 200906-200908 12UTCgeopotential 500hPa

Correlation coefficent of forecast anomalyNH Extratropics Lat 20.0 to 90.0 Lon -180.0 to 180.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 2 3 4 5 6 7 8

Forecast days

AC

C

T639

GRAPES N. Hemis.

forecast verification 200906-200908 12UTCgeopotential 500hPa

Correlation coefficent of forecast anomalySH Extratropics Lat -20.0 to -90.0 Lon -180.0 to 180.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1 2 3 4 5 6 7 8

Forecast days

AC

C

T639

GRAPES S. Hemis.

GRAPES-GFS 2011 GRAPES-GFS 2011

GRAPES Global Forecast System(pre-operational)

reforecasts for 200906~200908

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

(From shen et al., 2012)

3 Progresses in GRAPES_VAR

Milestone of GRAPES variational data assimilation system

Serial regional P3DVAR using pressure coordinate

Serial global P3DVAR Serial regional M3DVAR using height-based terrain following coordinate

Serial regional 4DVAR Serial global M3DVAR

Serial global 4DVAR

Parallel global P3DVAR

Parallel global 4DVAR

Paral. Reg. M3DVAR/4DVAR

2001

2005

2005

2010

2010

2008

2005

2010

2013

Black: developedBlue: in progress

Orange: OperationRed: in the future

Quai-operation running

2009

Serial regional 3DVAR operation running

2009

(From Gong et al., 2012)

GRAPES model level analysis (GRAPES_M3DVAR) and pressure level analysis (GRAPES_P3DVAR)

GRAPES_M3DVAR 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


cost function J

forward integration using tangent-linear model at the lower resolutionforward integration using tangent-linear model at the lower resolution

backward integration using adjoint model at the lower resolutionbackward integration using adjoint model at the lower resolution

J

forward integration using non-linear model at the higher resolutionforward integration using non-linear model at the higher resolution

OBS

minimization process

analysis incrementδx

analysis results

outer loop

inner loop

OBS OBS

GRAPES 4DVAR

observation increments

forcing term forcing term forcing term



(From Zhang et al., 2012)

1-month running

GRAPES_MESO V3.0 vs 4DVAR

Model: GRAPES_MESO V3.0Resolution: 15 km (502x330), 31 levelsTime Step: 300 seconds

Analysis System: GRAPES-4DVAROuter loop resolution: The same resolution as

the modelInner loop resolution: 45 km (167x111), 31 levels

Physics process: LSP; MRF PBL; CUDU convection

Outer loop: 1 iterationObs: TEMP, SYNOP, AIREP, SHIPSAssimilation Window: [-3, 0]Analysis Time: 00UTC and 12UTC

Background Fields: TL639L60 12-hours forecast

Forecast Range: 48 hours

Since Aug.2010


1-month averaged Ts score of 24 hour precipitation forecast over whole China

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1 2 3 4

3DVAR

4DVAR

LightLight ModerateModerate HeavyHeavy TorrentialTorrential


Flow Chart of Cloud Analysis Scheme

(From Zhu et al., 2012)

Result of Cloud Cover

background used Surface data used satellite tbb

used radar reflectivity used satellite cta increment


3h forecast

observation

With cloud analysis

6h forecast

12h forecast

Without cloud analysis


The first hour precipitation 12 ： 00-13 ：00

Without cloud analysis

OBS

With cloud analysis

Hourly accumulated precipitation Hourly accumulated precipitation

Hourly accumulated precipitation


6h forecast composite reflectivity

Radar OBS

Without cloud analysis With cloud analysis


4 Implementation of GRAPES_TYM

4.1 Quasi-operational implementation in NMC

(From Ma et al., 2012)

Model GRAPES_MESO3.0Domain 90º~171ºE ， 0º~51ºNGrid points 541341Initial time 00UTC 、 12UTCInitialization Bogus-

relocated+intensity-adjustment

F. lenth 72hrsInterval-out 3hrs

Physical schemes Micro ： WSM6 Cumul ： SASPBL ： YSULSM ： SLAB

GRAPES_TYM


Development of GRAPES_TYM for Typhoon intensity forecast

0/218 24/179 48/144 72/1100

5

10

15

20

25

GRAPES_TYM T213

Min

SLP

Err

or (

hPa)

FstHour/Samples

0/218 24/179 48/144 72/1100

50

100

150

200

250

300

350

GRAPES_TYM T213

FstHour/Samples

Tra

ck E

rror

(km

)

0/218 24/179 48/144 72/1100

5

10

15

20

Max

V10

m E

rror

(m/s

)

FstHour/Samples

GRAPES_TYM NCEP_GFS GRAPES_TYM T213

0/173 24/148 48/107 72/770

100

200

300

400

FstHour/Samples

GRAPES_TYM GRAPES_TCM GRAPES_TMM

Tra

ck E

rror (k

m)

Mean track errors of GRAPES_TYM to

GRAPES_TMM 、 GRAPES_TCM

Minimum SLP errorMaximum V10m error

Track error


0/11 24/9 48/7 72/50

100200300400500600700800900

Mea

n T

rack

Err

or (k

m)

FstHour/Samples

GRAPES_TYM GRAPES_PHY GRAPES_NEW

Case of 2012-13 KAITAK


0/11 24/9 48/7 72/50

4

8

12

16

20

MA

E o

f PSL (hPa)

FstHour/Samples


13/00 14/00 15/00 16/00 17/00 18/00950955960965970975980985990995

1000

Min

PSL (hPa)

Valid Time (DD/HH)

13/00 14/00 15/00 16/00 17/00 18/00950955960965970975980985990995

1000

Min

PSL (hPa)

Valid Time (DD/HH)13/00 14/00 15/00 16/00 17/00 18/00

950955960965970975980985990995

1000

Min

PSL (hPa)

Valid Time (DD/HH)



0/11 24/9 48/7 72/50

4

8

12

16

20

MA

E o

f SPD

(m

/s)

FstHour/Samples


13/00 14/00 15/00 16/00 17/00 18/00

10

15

20

25

30

35

40

45

50

Max

SPD

(m

/s)

Valid Time(DD/HH)

13/00 14/00 15/00 16/00 17/00 18/00

10

15

20

25

30

35

40

45

50

Max

SPD

(m

/s)

Valid Time(DD/HH) 13/00 14/00 15/00 16/00 17/00 18/00

10

15

20

25

30

35

40

45

50

Max

SPD

(m

/s)

Valid Time(DD/HH)



0/14 24/12 48/10 72/80

100

200

300

Mea

n T

rack

Err

or (km

)

FstHour/Samples


Case of 2012-11 HAIKUI


0/14 24/12 48/10 72/80

4

8

12

16

20M

AE o

f PSL (hPa)

FstHour/Samples


03/0004/0005/0006/0007/0008/0009/00

940

950

960

970

980

990

1000

1010

Min

PSL (hPa)

Valid Time (DD/HH)

03/0004/0005/0006/0007/0008/0009/00

940

950

960

970

980

990

1000

1010

Min

PSL (hPa)

Valid Time (DD/HH)

03/0004/0005/0006/0007/0008/0009/00

940

950

960

970

980

990

1000

1010

Min

PSL (hPa)

Valid Time (DD/HH)



0/14 24/12 48/10 72/80

2

4

6

8

10M

AE o

f SPD

(m

/s)

FstHour/Samples


03/0004/0005/0006/0007/0008/0009/00

10

15

20

25

30

35

40

45

50

Max

SPD

(m

/s)

Valid Time(DD/HH)

03/0004/0005/0006/0007/0008/0009/00

10

15

20

25

30

35

40

45

50

Max

SPD

(m

/s)

Valid Time(DD/HH)

03/0004/0005/0006/0007/0008/0009/00

10

15

20

25

30

35

40

45

50

Max

SPD

(m

/s)

Valid Time(DD/HH)



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)

RegionalECOM-si

(0.25*0.25)

Coupler(Oasis 3.0)

SST

Wind stressHeat flux

Water flux

(From Sun et al., 2012)

Model domain

ECOM:Horizontal resolution: 0.25°x 0.25°

Domain ： 104°E~145°E, 8°N~43°NProvided ： SST

GRAPES:Horizontal resolution : 0.15°x 0.15°

Domain ： 100°E~150°E, 5°N~45°NProvided: wind stress, solar flux, heat

flux, water flux; Fluxes are exchanged every 360s.


•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


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


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 –observationRed-Uncoupled modelGreen-Coupled model


Forecast verification for MUIFA Number of cases (21, 21,19,17)


Typhoon MUIFA intensity forecastMinimum sea level pressure forecast

GRAPES_tymMinimum sea level pressure forecast

Coupled model

Maximum wind forecast GRAPES_tym

Maximum wind forecast Coupled model


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 couplingNCEP AVHRR + AMSR-E SST analysis

at 15/09/08, 00UTC


Forecast verification for Typhoon SINLAKUNumber of cases (21, 21,19,17)

0

2

4

6

8

10

12

14

16

Max

imum

win

d er

ror (

m/s

)

Forecast hour

Uncoupled

Coupled


Forecast verification of Nine TC in 2011 Number of cases (72,72,56,56,49,44,44)

0

5

10

15

20

25

0 12 24 36 48 60 72

Mea

n m

inim

um se

a le

vel

pres

sure

err

or (h

pa)

Forecast hour

Uncoupled

Coupled

02468

10121416

0 12 24 36 48 60 72

Mea

n m

axim

um w

ind

erro

r (m

/s)

Forecast hour

UncoupledCoupled


Minimum sea level pressure forecast GRAPES_tym

Minimum sea level pressure forecast Coupled model

Maximum wind forecast GRAPES_tym

Maximum wind forecast Coupled model

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Fore

cast

Observation

T+0h

T+24h

T+48h

T+72h

Intensity forecast of Nine TC in 2011 Number of cases (72,72,56,44)


5 High resolution modeling activities

5.1 High Resolution Modeling Activities at CMABased 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

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:

),(

),(ˆ

yxZZ

yxZzZz

sT

sT

),(ˆ yxZbzz s

with )ˆ

1(TZ

zb 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)

New TF coordinates

• The different decaying coefficients “b” can be defined as:

)ˆ

1(TZ

zb (Gal-Chen and Sommerville, 1974)

]/sinh[

]/)ˆsinh[(*

*

hZ

hzZb

T

Th

2

1*

*

]/sinh[

]/)ˆsinh[(

i iT

iTH hZ

hzZb

c

c

n

cTC

zz

zzz

z

Z

zb

ˆˆ0

ˆˆˆ

ˆ

2cos

ˆ1

(similar to Klemp, 2011)“n>2”: an empirical

number; zc : 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


•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

0

0

00

exp

g 9.81,T 287.0

exp ,

p

p

gz

C T

gzT

C T

ˆ ˆ ( ( , ))ˆp z p z p b z SC C C J b Z x yz

with z

zJ b

ˆ


-50000 0 50000-0.1

-0.050

0.050.1

-50000 0 500000

5000

10000

15000L2

-50000 0 50000-0.1

-0.050

0.050.1

-50000 0 500000

5000

10000

15000L10

-50000 0 50000-0.05

-0.0250

0.0250.05

-50000 0 500000

5000

10000

15000L20

-50000 0 50000-0.02-0.01

00.010.02

-50000 0 500000

5000

10000

15000L30

-50000 0 50000-0.005

-0.00250

0.00250.005

SLEVE2 coordinate

-50000 0 500000

5000

10000

15000L40

-50000 0 50000-0.1

-0.050

0.050.1

-50000 0 500000

5000

10000

15000L2

-50000 0 50000-0.1

-0.050

0.050.1

-50000 0 500000

5000

10000

15000L10

-50000 0 50000-0.05

-0.0250

0.0250.05

-50000 0 500000

5000

10000

15000L20

-50000 0 50000-0.02-0.01

00.010.02

-50000 0 500000

5000

10000

15000L30

-50000 0 50000-0.005

-0.00250

0.00250.005

SLEVE1 coordinate

-50000 0 500000

5000

10000

15000L40

-50000 0 50000 -0.1

-0.050

0.050.1

-50000 0 50000 0

5000

10000

15000L2

-50000 0 50000 -0.1

-0.050

0.050.1

-50000 0 50000 0

5000

10000

15000L10

-50000 0 50000 -0.05

-0.0250

0.0250.05

-50000 0 50000 0

5000

10000

15000

heig

ht

L20

-50000 0 50000 -0.02-0.01

00.010.02

-50000 0 50000 0

5000

10000

15000L30

-50000 0 50000 -0.005

-0.00250

0.0025 0.005

COS coordinate

-50000 0 50000 0

5000

10000

15000L40

-50000 0 50000-0.1

-0.050

0.050.1

-50000 0 500000

5000

10000

15000L2

-50000 0 50000-0.1

-0.050

0.050.1

-50000 0 500000

5000

10000

15000L10

-50000 0 50000-0.05

-0.0250

0.0250.05

pres

sure

gra

dien

t for

ce e

rror

-50000 0 500000

5000

10000

15000L20

-50000 0 50000-0.02-0.01

00.010.02

-50000 0 500000

5000

10000

15000L30

-50000 0 50000-0.005

-0.00250

0.00250.005

Gal.C.S coordinate

-50000 0 500000

5000

10000

15000L40

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)

1/ 2/ 100%Gal SLEVE SLEVE COS

Gal

E E

E

Vertical levels SLEVE1 SLEVE2 COS

L40 67% 99% 100%

L30 62% 99% 100%

L20 51% 99% 99%

L10 31% 95% 75%

L2 4% 30% 2%

R.R.E. is defined as:

Relatively Reduced Errors: SLEVE1(SLEVE2, COS) against GCS


2 1

2 1

1 5

( ) 10 sin 42

0 4

km z

z zu z km z z

z z

z km

20 0

0

cos ( ) 1( , ) 2

0 1 x z

rr x x z z

x z rR R

r

,

Initial wind:

Analysis density

distribution ：

before mount over after mount

2D test design (cont.)

flow from L to R density distribution


-75000 -50000 -25000 0 25000 50000 75000 0

2500

5000

7500

10000

12500

15000

x

he

ig

ht

k regular grid

-75000 -50000 -25000 0 25000 50000 75000 0

2500

5000

7500

10000

12500

15000

x

he

ig

ht

e SLEVE2 coordinate

-75000 -50000 -25000 0 25000 50000 75000 0

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x

he

ig

ht

f SLEVE2 coordinate

-75000 -50000 -25000 0 25000 50000 75000 0

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he

ig

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c SLEVE1 coordinate

-75000 -50000 -25000 0 25000 50000 75000 0

2500

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x

he

ig

ht

l regular grid

-75000 -50000 -25000 0 25000 50000 750000

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a Gal.C.S coordinate

-75000 -50000 -25000 0 25000 50000 750000

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b Gal.C.S coordinate

-75000 -50000 -25000 0 25000 50000 75000 0

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he

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g COS coordinate(Zc=15km)

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i COS coordinate(Zc=10km)

-75000 -50000 -25000 0 25000 50000 75000 0

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j COS coordinate(Zc=10km)

-75000 -50000 -25000 0 25000 50000 75000 0

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h COS coordinate(Zc=15km)

-75000 -50000 -25000 0 25000 50000 750000

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d SLEVE1 coordinate

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-zc=15km

COS-zc=10km

without topography


1 12 22 2

, , , ,1 1 1 1

2 12 22

, ,1 1 1 1

1 2

( ) ( ) ( ) ( )

( ) ( )

m n m n

i k num i k ana i k num i k anak i k i

m n m n

i k ana i k anak i k i

SLEVE SLEVE COS

1

2

wi thout terrai n

, , , ,

, ,1 2

( ) ( ) ( ) ( )

( ) ( )i k num i k ana i k num i k ana

i k ana i k anaSLEVE SLEVE COS

MAX MAX

MAX MAX

wi thout terrai n

、

left : temporal evolution of

计算误差

积分时间

Defining two parameters as following, according to Williamson (et.al,1992)

,( )i k num,( )i k anais numerical solution is analytical solution

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6Gal.C.S coordinateSLEVE1 coordinateSLEVE2 coordinateA coordinate(Zc=15km)A coordinate(Zc=10km) Gal.C.S

SLEVE1

SLEVE2

COS(10KM)

COS(15KM)

integral time

erro

r

2

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6Gal.C.S coordinateSLEVE1 coordinateSLEVE2 coordinateA coordinate(Zc=15km)A coordinate(Zc=10km)

Gal.C.S

COS(15KM)

COS(10KM)

SLEVE2

SLEVE1

integral time

right : temporal evolution of2

The errors of the simulations


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


The torrential rain-storm occurred on 21 Jul. 2012 in Beijing

24 h accumulated precipitation from 00UTC 21 Jul to 00UTC 22

Jul

“The torrential rain-storm occurred on 21 Jul. 2012 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)

Heavy rainfall event on Jul.21/2012 Beijing

Mean=190.3mm/24hrMax=460mm/24hr

00z21Jul2012-00z22Jul201200z21Jul2012-00z22Jul2012Initial: global analysisBC: global forecast

Grid size:3kmPhysics:

- microphysics: WSM6 - radiation ： RRTM S&L

- pbl ： MRF - land surface ： NOAH

Initial: global analysisBC: global forecast

Grid size:3kmPhysics:

- 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)

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

(From Huanget al., 2012)

(GRAPES_Meso-3km)

5.2 other Research activities at CMA

– GRAPES Yin-yang dynamic core– SV-based GRAPES ensemble forecast system– New algorithms of dynamic core

Progress of GRAPES Yin-Yang gridThe 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.

2 2 H Helmholtz equation:

(From Peng et al., 2012)

3D advection resultsalpha=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 backto the initial state.

200 , 60, 1.0z m nlev d

day12

( 12) ( 0)q day q day

1

2

3

4

5

6

7

8

9

10

(From Peng et al., 2012)

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)

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


Discontinuous Galerkin results (Giraldo & Restelli, JCP, 2008)

Fourth order MCV results

Linear nonhydrostaticmountain case

Analytic solution:red dash line

zero contours


6 Future Plan

Strategic Plan （ ~2015）

GRAPES_GFS

GRAPES_GFS3DVAR, 10 d

Δx=25km, L60

GRAPES_Meso + RAFS

GRAPES_TYM GRAPES_EPS

3DVAR, 24-60hΔx=3-10km, L45

3DVAR/Bogus, 72h, Δx=10km, L45

SV+Sto.Phy, 10dΔx=50km, L60

GRAPES P3DVAR a new fixed version

(res:1 deg—>0.5deg)FY-3A

MWTS 、 FY-2E IR AMV

2012 2013 2015

Global GRAPES_VAR Research & Operation Plan

2014 2020

•GRAPES P3DVARM3DVAR;•Conventional data QC re-check•Data Preprocessing system re-design

GRAPES-M3DVARFY3-B MWTS 、 FY3-A/B MWHS 、 FY-2D/F IR AMV

•Satellite vertical sounding high level channel used

GRAPES-M3DVAR oper. runNPP satellite data usedMore 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


GRAPES 3DVAR parallel version operation

2012 2013 2015

Regional GRAPES_VAR research and operation plan

2014 2020

•3DVAR 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 usedGRAPES_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)

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 E

nKF

analysis ensemble

to control analysisR

e-center EnK

F analysis ensem

ble to control analysis

……

……

…… …

…

First guess forecast

3DVAR

observations

Innovation

member k forecast

GRAPES-DFL0 20m 40m

GRAPES-DFL0 20m 40m

GRAPES-DFL0 20m 40m

GRAPES-DFL0 20m 40m

Future HR GRAPES -DA and Prediction System

Mu

lti mo

del

EP

S

DAS: EnKF/3DVAR Hybrid DA System; Multi Models: WRF, GRAPES_Meso

H.R

. M

od

el

(modified from talk of Xue et al., 2012)

''1''1

2'1

1'11

211'1

2

1

2

1

2

1

,

HxyHxyαCαxBx

αx

oToTT

oe JJJJ

R

K

k

ekk

1

'1

' xαxx

82

B 3DVAR static covariance; R observation error covariance; K ensemble size; C correlation matrix for ensemble covariance localization; e

kx kth ensemble perturbation; '1x 3DVAR increment; 'x total (hybrid) increment; 'oy innovation vector;

H linearized observation operator; 1 weighting coefficient for static covariance;

2 weighting coefficient for ensemble covariance; α extended control variable.

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)

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)

THANK YOU

grapes model research progresses at cma

Documents

domain of grapes

domains of grapes

establishment of grapes

var4 implementation

nested model system

tmmfrom chen

numerical forecastfrom

mfrom wang