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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 with thanks to our colleagues who contribute to the presentation. (The 4th THORPEX-ASIA Science Workshop and ARC-8 Meeting

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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 Center

China Meteorological Administration

with thanks to our colleagues who contribute to the presentation

(The 4th THORPEX-ASIA Science Workshop and ARC-8 Meeting

30Oct.~3 Nov., 2012, Kunming, China )

outline
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
slide3
Data assimilation group

Ensemble prediction Group

Dynamic process group

Typhoon prediction group

Parallel computing group

Observation data quality control group

Physical process group

Post process and products development group

Regional model group

System pre-operational test group

Model version manage and information technology group

Numerical Weather Prediction Center of CMA

Director: Dr. WANG Jianjie

Chief Engineer: Dr. CHEN Dehui

Deputy-directors:

Dr. GONG Jiandong and Dr. SHEN Xueshun

System & Operation Division

General Office

R&D Division

Model verification group

The restructured organization of Numerical Prediction Center

slide5
Current NWP Operational System in NMC

In general, there were no big changes in the operational NWP systems

slide7
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)

assessment of tc forecast methods
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)

evolution of yearly mean track errors
Evolution of yearly mean track errors

hrs

hrs

Bogus initialization + cumulus schemes

(From Wang et al., 2012)

domains of grapes tmm
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)

slide12
+ 5d Tro. weather forecast

+ T. Cyclone forecast

+ SST, sea flow forecast

GRAPES_TMM

(0.36o)

MOM-sea flow model

+ 36 hrs Meso-scale forecast

+ S.C. fine w. forecast

+ sea waves, surge forecast

Storm surge

GRAPES_TMM

(0.12o)

Sea waves

CHAF-1h-cyc

+ hourly rapid cycling anal.

+ 1~3 hrs nowcast

+ 3~12 hrs sort-term forecast

GRAPES_TMM

(0.03o)

SWIFT-nwcst

Radar-extrap.

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

(From Wan et al., 2010)

slide13
Evolution of Yearly Mean Track Errors

48 hrs F.

24 hrs F.

TL

Grapes

DAS/optimal use of data+ cumulus/PBL schemes

Mean Track errors2012

24h 48h 72h

96.5 km 176.7km 235.6km

(From Chen et al., 2012)

slide14
Obs.

Initial time: 00Z21June2012

F. length: 48hrs

(From Chen et al., 2010)

GRAPES_TMM

slide15
Obs.

Initial time: 00Z22June2012

F. length: 48hrs

(From Chen et al., 2010)

GRAPES_TMM

slide16
Complicated Track of Prapiroon-2012

(From Chen et al., 2012)

slide17
Inter-comparison to ECMWF, JMA, T639 and GRAPES_TMM (Initial Time: 12UTC, 00UTC)

(From Chen et al., 2012)

implemented grapes meso forecast system
ImplementedGRAPES_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
slide19
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)

flow chart of grapes gfs
Sat. data

First Quess

Flow chart of GRAPES_GFS

Pre-Processing

Cycling

Assimilation

and Forecast

GRAPES_3DVAR

Quality Control

Initial F.

Conventnl. data

Digital Filter

Pre-Processing

10 d forecast

GRAPES_GFS

Quality Control

efforts in improving the forecast skill of grapes gfs toward operation
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)

slide23
GRAPES Global Forecast System(pre-operational)

S. Hemis.

N. Hemis.

GRAPES-GFS 2011

GRAPES-GFS 2011

S. Hemis.

N. Hemis.

reforecasts for 200906~200908

(From shen et al., 2012)

slide25
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

Serial global 4DVAR

Paral. Reg. M3DVAR/4DVAR

2013

Parallel global 4DVAR

(From Gong et al., 2012)

grapes model level analysis grapes m3dvar and pressure level analysis grapes p3dvar
GRAPES model level analysis (GRAPES_M3DVAR) and pressure level analysis (GRAPES_P3DVAR)

(From Gong et al., 2012)

slide27
OBS

OBS

OBS

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)

1 month running
Since Aug.20101-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)

slide29
Light

Heavy

Torrential

Moderate

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

(From Zhang et al., 2012)

slide30
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)

result of cloud cover
Result of Cloud Cover

background

used Surface data

used satellite tbb

used satellite cta

increment

used radar reflectivity

(From Zhu et al., 2012)

slide32
3h forecast

With cloud analysis

Without cloud analysis

observation

6h forecast

12h forecast

(From Zhu et al., 2012)

slide33
The first hour precipitation 12:00-13:00

Hourly accumulated precipitation

Hourly accumulated precipitation

Without cloud analysis

OBS

With cloud analysis

Hourly accumulated precipitation

(From Zhu et al., 2012)

slide34
6h forecast composite reflectivity

Without cloud analysis

With cloud analysis

Radar OBS

(From Zhu et al., 2012)

slide37
GRAPES_TYM

(From Ma et al., 2012)

development of grapes tym for typhoon intensity forecast
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)

slide39
Case of 2012-13 KAITAK

(From Ma et al., 2012)

slide40
Case of 2012-13 KAITAK

(From Ma et al., 2012)

slide41
Case of 2012-13 KAITAK

(From Ma et al., 2012)

slide42
Case of 2012-11 HAIKUI

(From Ma et al., 2012)

slide43
Case of 2012-11 HAIKUI

(From Ma et al., 2012)

slide44
Case of 2012-11 HAIKUI

(From Ma et al., 2012)

slide45
Regional air-sea

Coupled model

Atmosphere

Ocean

Wind stress

Heat flux

Water flux

GRAPES_TYM

(0.15*0.15)

Regional

ECOM-si

(0.25*0.25)

Coupler

(Oasis 3.0)

SST

Initial conditions/

Lateral boundary

condition

GFS

Initial conditions/

boundary condition

Global HYCOM

4.2 The Coupled Typhoon-Ocean Model

(From Sun et al., 2012)

slide46
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)

slide47
O

A

O

A

Oasis3

O

A

O

OASIS3 coupler

OASIS:OceanAtmosphereSeaIceSoil

---------Developed since 1991 in CERFACS

  • performs:
    • synchronisation of the component models
    • coupling fields exchange and interpolation
    • I/O actions
  • External library and module used:
  • NetCDF/parallel NetCDF
  • libXML, mpp_io, SCRIP
  • MPI1 and/or MPI2

(From Sun et al., 2012)

sst forecasted by the coupled model typhoon muifa
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)

typhoon muifa impact of coupling
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)

typhoon muifa intensity forecast
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)

typhoon sinlaku impact of coupling
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)

forecast verification for typhoon sinlaku number of cases 21 21 19 17
Forecast verification for Typhoon SINLAKUNumber of cases (21, 21,19,17)

(From Sun et al., 2012)

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

(From Sun et al., 2012)

intensity forecast of nine tc in 2011 number of cases 72 72 56 44
Intensity forecast of Nine TC in 2011Number of cases (72,72,56,44)

Minimum sea level pressure forecast

Coupled model

Minimum sea level pressure forecast

GRAPES_tym

Maximum wind forecast

GRAPES_tym

Maximum wind forecast

Coupled model

(From Sun et al., 2012)

5 1 high resolution modeling activities at cma based on grapes meso
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
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)

new tf coordinates
New TF coordinates

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

(Gal-Chen and Sommerville, 1974)

G.C.S.

SLEVE1

(Schar, 2002)

h*: scale of ref-topography; h*1 and h*2:

large and small-scale of ref-topogr.

SLEVE2

(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)

slide60
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)

slide61
Errors of PGF calculation induced by using TF coordinates

G.C.SSLEVE1SLEVE2COS

top

bottom

On different vertical levels: L2, L10, L20, L30 and L40 from bottom to top

(From Li et Chen, 2012)

slide62
Relatively Reduced Errors: SLEVE1(SLEVE2, COS) against GCS

R.R.E. is defined as:

(From Li et Chen, 2012)

slide63
2D test design (cont.)

Initial wind:

Analysis density distribution :

over

after mount

before mount

flow from L to R

density distribution

(From Li et Chen, 2012)

slide64
Advection test : air mass moves over a topographic obstacle

GCS

SLEVE1

SLEVE2

COS-zc=15km

COS-zc=10km

without topography

left:density distribution at 0s,5000s,10000s right:the errors at 10000s after mountain

(From Li et Chen, 2012)

slide65
The errors of the simulations

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

is analytical solution

is numerical solution

Gal.C.S

Gal.C.S

SLEVE1

计算误差

SLEVE1

SLEVE2

SLEVE2

error

COS(15KM)

COS(15KM)

COS(10KM)

COS(10KM)

积分时间

integral time

integral time

right : temporal evolution of

left : temporal evolution of

(From Li et Chen, 2012)

the preliminary results with new tf coordinates in grapes meso
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)

slide68
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
Heavy rainfall event on Jul.21/2012 Beijing
    • Initial: global analysis
    • BC: global forecast
    • Grid size:3km
    • Physics:
    • - microphysics: WSM6
  • - radiation: RRTM S&L
  • - pbl : MRF
  • - land surface :NOAH

Obs.

00z21Jul2012-00z22Jul2012

Beijing

24-hour accumulated rainfall

Mean=190.3mm/24hr

Max=460mm/24hr

GRAPES_Meso-3km

ECMWF

Fcst.

Max=341mm/24hr

(From Huanget al., 2012)

slide70
Comparison of precipitation every 6-hour forecasts against Obs.

Fcst.0-6hr

Fcst.6-12hr

Fcst.12-18hr

Fcst.18-24hr

Obs.12-18hr

Obs.18-24hr

Obs.0-6hr

Obs.6-12hr

(GRAPES_Meso-3km)

(From Huanget al., 2012)

5 2 other research activities at cma
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 grid
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)

3d advection results
3D advection results

alpha=0.

Instant image on the Yang grid

5

9

1

4

6

10

8

2

alpha=45.

day12

7

3

alpha=90.

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.

(From Peng et al., 2012)

slide74
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

Solutionpoints

Constraint points

Constraint conditons:

Approximate Riemann solvers

The unknowns (solution points) are updated in a fourth order mcv scheme, for example,

(From Li et al., 2012)

The same in multi-dimension, for example, y direction

slide75
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)

slide76
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)

slide78
3DVAR, 24-60h

Δx=3-10km, L45

3DVAR/Bogus,

72h, Δx=10km, L45

SV+Sto.Phy, 10d

Δx=50km, L60

GRAPES_Meso

+ RAFS

GRAPES_EPS

GRAPES_TYM

GRAPES_GFS

Strategic Plan

(~2015)

GRAPES_GFS

3DVAR, 10 d

Δx=25km, L60

global grapes var research operation plan
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

2015

2013

2012

2020

2014

  • Satellite vertical sounding high level channel used
  • GRAPES-4DVAR develop;
  • New version data preprocessing used
  • GRAPES-4DVAR real-time trial
  • More FY satellite data
  • GRAPES P3DVARM3DVAR;
  • Conventional data QC re-check
  • Data Preprocessing system re-design

(From Gong et al., 2012)

regional grapes var research and operation plan
Regional GRAPES_VAR research and operation plan
  • GPS/PW oper used.
  • Cloud analysis oper used
  • GRAPES_RAFS quasi-oper. running
  • GRAPES 3DVAR parallel version operation
  • Radar VAD、AWS humidity data operational used

2015

2013

2012

2020

2014

  • Radar precipitation heating profile;
  • Radar reflectivity QC;
  • Wind profile QC
  • Continue GRAPES_4DVAR?
  • 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;
  • 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)

slide81
EnKF analysis 2

EnKF analysis k

EnKF analysis 1

GRAPES analysis

GRAPES forecast

Future HR GRAPES -DA and Prediction System

observations

Re-center EnKF analysis ensemble

to control analysis

member 1 forecast

member 1 analysis

member 1 forecast

GRAPES-DFL

0 20m 40m

3DVAR

Multi model EPS

Innovation

member 2 forecast

member 2 analysis

member 2 forecast

EnKF

GRAPES-DFL

0 20m 40m

……

……

……

……

member k forecast

member k analysis

member k forecast

Ensemble covariance

GRAPES-DFL

0 20m 40m

control forecast

GRAPES-DFL

0 20m 40m

3DVAR-ECV

H.R. Model

First guess forecast

data assimilation

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

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

slide82
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)

blending nowcast with grapes hr
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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