Short Range NWP Strategy of JMA and Research Activities at MRI

Kazuo SAITO Meteorological Research Institute,

ksaito@mri-jma.go.jp

1. Operational mesoscale NWP at JMA

2. Recent developments for operation

3. Near future plans

4. Research activities in MRI

IAMAS2005, 11 August 2005, Beijing

Essential factors in the mesoscale NWP

• Model (Domain, Resolution, Dynamics, Physical processes)

• Initial condition (Analysis method, Data)

• Boundary condition

1m 10m 100m 1km 100km 1000km 10000km10km

second

month

week

day

hour

minute

year

turbulence

front

extra-tropical cyclone

planetary wave

thunder storm

Scale of atmospheric phenomena

micro scale

mesoscale

Macro scale

local wind

heavy rain

typhoon

cumulus conventional aerological observation -300 km, 2/day

conventional NWP model 6x = 100-200 km, 2-4/day

Synoptic forcing

Mesoscale NWP at JMA (March 2001-)

MSM•10 km L40, 3600 km x 2880 km, 18 hours forecast, 4 times a day• Hydrostatic spectral model (March 2001-August 2004)• Nonhydrostatic (September 2004-) nested with RSMRSM •20 km L40, 6480 km x 5120 km, 51 hours , 2 times a day•Hydrostatic spectral model, nested with GSM (60 km L40)

RSM

MSM

Performance of JMA Mesoscale Model

Threat scores 40km 10mm/6hr Threat scores 10km 10mm/3hr

Performance of MSM has been improving for both weak and moderate rains

2. Recent developments for operational meso NWP

•Start of Mesoscale NWP (Mar. 2001)

•Wind profiler data (Jun. 2001)

•4D-Var in MSM (Mar. 2002)

•Domestic ACARS data (Aug. 2002)

•4D-Var in RSM (Jun. 2003)

•SSM/I precipitable amount (Oct. 2003)

•QuikSCAT Seawinds (Jul. 2004)

•Nonhydrostatic model (Sep. 2004)

•Doppler radar radial winds ( Mar. 2005)

Wind Profiler Network of JMA

JMA deployed 25 wind profilers in 2001, and their data have been assimilated since June 2001.

Wind profilers measure the low level winds up to 5 km with a vertical resolution of 300m .

Currently, 31 wind profilers measure wind successively in addition to the 18 aerological sondes.

Initial Assimilation System for MSM(March 2001-March 2002)

04 UTC

1-h Forecast with MSM

OI Analysis +Physical

Initialization

Conventional DataPrecipitation Data

05 UTC 06 UTC03 UTC

3-h Forecast with RSM (20km L40) from 00UTC

Physical Initialization

Precipitation Data

(For Analysis at 06 UTC)

Conventional DataPrecipitation Data

Conventional DataPrecipitation Data

OI Analysis +Physical

Initialization

OI Analysis +Physical

Initialization

1-h Forecast with MSM

1-h Forecast with MSM

18-h Forecast with

MSM

2 x 3 hour assimilation windows.

Incremental approach using a 20-km version of MSM for inner loop.

Inner forward : nonlinear full-physics model

Inner backward : reduced-physics adjoint model (grid-scale condensation, moist convective adjustment, vertical diffusion, simplified radiation)

Precipitation analysis by radar and AMeDAS observation are assimilated.

Boundary condition in assimilation window is controlled.

The Meso 4D-Var System(March 2002-)

First guess

analysis

observation

Jo

Jo

Jo

Jo

Jb

21UTC 00UTCAssimilation window

３ hrs

time

observation

observation

observation

Cost function 　 : c

oobb

cob

JyHMxyHMxxxxx

JJJJ

0

1T

0001T

00 R2

1B

2

1

cxob

cxoxbxx

JyHMxx

JJJJ

x

00

1TT00

1

0000

RHMB

Gradient of cost function :

Adjoint model

Model

Penalty term

initial time

Observation parameter

bx0

0x

x

Time integration of NWP model

Concept of Concept of ４４ D VarD Var

Radar-AMeDAS Precipitation Analysis•Hourly precipitation amount data with 2.5km resolution.

•Radar-observed precipitation intensity is accumulated, calibrated with 1,300 AMeDAS rain-gauges.

•More than 3,000 rain-gauges (not from JMA) added in 2003.

・： 4-elements・： Rain gauge

　 4D-Var in MSM

RUC with OI 4D-Var ObservationFT=15-18

3 hour accumulated rain for FT=18 hr Initial 12 UTC 9 September 2001

Ishikawa and Koizumi (2002)

0.25

0.30

0.35

0.40

0.45

0.50

0.55

3 6 9 12 15 180.00

0.05

0.10

0.15

0.20

0.25

0.30

3 6 9 12 15 18

Threat scores (40km verification grid)

June2001

0.25

0.30

0.35

0.40

0.45

0.50

0.55

3 6 9 12 15 180.00

0.05

0.10

0.15

0.20

0.25

0.30

3 6 9 12 15 18

Sep.2001

1mm/3h 10mm/3h

(h) (h)

(h) (h)

Red: 4D-VarBlue: routine

Domestic ACARS Data(August 2002-)

Domestic ACARS data from the Japan Air Line have been assimilated in addition to the conventional AIREP and AMDAR data.

The ANA data have been added since September 2003.

More than 10,000 reports per day.

Impact of ACARS Data

Observation (AMEDAS)

Shear line

Location of the observed local shear line near Tokyo is corrected with ACARS data.

WITHACARS

WITHOUTACARS DATA

Assimilation of precipitation and TPW data retrieved from TMI and SSM/I

(October 2003-)

Defense Meteorological Satellite ProgramSpecial Sensor Microwave / Imager

TRMM Microwave Imager

OSE for 00UTC, 25 Aug 2003

TPW by SSM/I and

TMI

With SSM/I and TMI

Without SSM/I and TMI

3 hour rain at FT=18

Observation

Water vapor field was improved

Sato (2003)

0.10

0.12

0.14

0.16

0.18

0.20

0.22

3 6 9 12 15 18

CNTL

TEST

0.28

0.30

0.32

0.34

0.36

0.38

0.40

3 6 9 12 15 18

CNTL

TEST

FT

FT

1m

m/3

hr

10m

m/3

hr

Period 2003 June 3 ～ 16 （ 2weeks 56 forecasts ） 10 km verification grid

Performance of MSM with TMI and SSM/I

Threat score

30 ﾟ N

T0207（ HALONG）

Observation

QuikSCAT

NASA

Assimilation of QuikSCAT SeaWinds July 2004 -

Threat scores 10km 30mm/3h, 3-19 June 2003

Precipitation FT=8-9. Initial: 12 UTC 18 July 2003

SeaWinds 10UTC 18 July 2003Ohashi (2004)

Non-hydrostatic MSM (JMA-NHM)September 2004-

Developed by joint work between MRI and NPD/JMA

HE-VI, stable computation with LF scheme t=40 secFully compressible, flux form 4th order advection with FCTDirect evaluation of buoyancy from density perturbation3-class bulk microphysics (water vapor, cloud water, rain, cloud ice, snow, graupel)Modified Kain-Fritsch convective parameterization schemeTargeted Moisture DiffusionBox-Lagrangian scheme for rain and graupel

Full paper submitted to M.W.R. (Saito et al., 2005)

Original K-F scheme. FT=12.

Modified K-F scheme. FT=12.

Observed 3 hour accumulated precipitation (mm) at 21 UTC.

Modification of the Kain-Fritsch convective parameterization

Several points (updraft property, trigger function, closure assumption) in the K-F scheme have been modified to prevent unnatural orographic rainfall and excessive stabilization . Submitted to MWR.

MSM NHM R/A

Case Study of Non-hydrostatic MSM

Hydrostatic MSM Radar-AMeDAS observation

Snowfall (13 January 2004, FT=18h)

Heavy rainfall event (18 July 2003, FT=15h)

Non-hydrostatic MSM

Performance of Non-hydrostatic MSM

Five-month total scores over forecast time 03, 06, 09, 12, 15, 18h against 3hourly rain analysis at 20 km grid

NH-MSM

MSM

Five-month total scores at FT=18h against analysis of height

Performance of JMA Mesoscale Model

Bias scores 10km 10mm/3hr

High bias scores in winter were removed by NHM

NHM

Without DPR wind FT=15

With DPR winds FT=15

Observation

Threat Scores for winter10mm/ 3hour

0.05

0.075

0.1

0.125

3 6 9 12 15 18

Forecast time [hour]

Threat Scores for summer

0.125

0.15

0.175

0.2

0.225

3 6 9 12 15 18

Forecast time[hour]

Assimilation of Doppler radar radial winds March 2005-

Koizumi and Ishikawa (2005)

10mm/ 3h 10kmスレットスコア（ メッシュ）

0

0.1

0.2

0.3

0.4

0.5

0.620

0103

2001

06

2001

09

2001

12

2002

03

2002

06

2002

09

2002

12

2003

03

2003

06

2003

09

2003

12

2004

03

2004

06

2004

09

2004

12

2005

03

2005

06

2001 0.13)年（ 2002 0.17年（ ） 2003 0.19年（ ） 2004 0.24年（ ）

Performance of MSM has been improved

0.11

0.17

0.23

4D-VarNHM

Threat scores 10 km, 10mm/3hr for FT=6-9

Major Operational Changes in GSM•Enhancement of vertical resolution from L36 to L40 (Mar. 2001)

•3D-Var (Sep. 2001)

•QuikSCAT Seawinds, ATOVS radiances (May 2003)

•Modification of the cumulus parameterization (May 2003, Jul. 2004)

•MODIS Arctic wind data (May 2004, Sep. 2004)

•4D-Var (Feb. 2005)

•Semi-Lagrangian scheme (TL319; Feb. 2005)

Boundary conditions for MSM

Major Operational Changes in RSM

• Enhancement of vertical resolution from L36 to L40 (Mar. 2001)

•4D-Var (Jun. 2003)

•Target moisture diffusion (Apr. 2004)

500 hPa Height

Improvement of GSM performance

500 hPa Temperature

3D-Var 4D-Var4D-Var

Cumulus, ATOVS,etc.

Significant improvement by major changes (cumulus, ATOVS, etc.) in May 2003.

Significant improvement by 3D-Var in September 2002.

Improvement in the recent 3 years (2002-2005) exceeds that in 10 years before 2002.

RMSE of 500 hPa Height 1991-20053 years11 years

Performance of GSM in RMSE region

Contributes to RSM forecast through the lateral B.C.

1 Day

2 Day

3D-OI 4D-Var Observation

6 hour accumulated precipitation for FT=6 (upper) and FT=12 (bottom) with RSM. Initial time 00UTC 17 June 2002.

4D-Var in RSM (June 2003-)

Threat Score R/ A 1mm/ 6hr（ ）

0.30

0.35

0.40

0.45

0.50

6 12 18 24 30 36 42 48Forecast Time

4D-Var

Threat Scores of RSM (Verified with 40km resolution, 1 month for June 2002)

Performance of RSM improved

4D-Var

Time series of RMSE for 500 hPa field

Contribute to MSM forecast through the lateral B.C.

3. Near Future Plans for 2006-2008

• Model High resolution MSM (5 km L50) (Mar. 2006-) - execute 8 times / day

• Boundary conditionHigh resolution GSM (TL959=20km L60) (2007-)- execute 4 times / day

• Initial condition Non-hydrostatic 4D-Var (JNoVA) (2008-) - 3 hour assimilation window execute 8 times / day,

inner 10 km

5 km Nonhydrostatic MSM (2006-)

Radar-AMeDAS obs. 5km Nonhydro. MSM 10km MSM

(18 July 2004 21UTC, FT=6-9)

- 10kmL40 → 5km L50 (Mar. 2006)- 4 times a day → 8 times a day (Mar. 2006)- 33-hr forecast (Mar. 2007)

- 60kmL40 → 20kmL60 (Mar. 2007)- Twice a day → 4 times a day (Mar. 2007)- Supply latest B.C. to MSM directly

60km GSM 20km GSM Radar-AMeDAS 12-h rain

(19 Jun 2001 12UTC, FT=12)

20km (TL959) Global Model (2007-)

5 km L50, 3 hour assimilation windows

Incremental approach using a 10-km version of nonhydrostatic MSM for inner loop

Nonhyd ｒ ostatic 4D-Var (2008-)

UL: Radar-AMeDAS 3-h rainUR: 12 hr forecast Meso 4DVarLL: Nonhydrostatic 4D-Var Initial time 12 UTC 17, July 2004

Honda et al. (2005)

4. Research activities at MRI• Model - Cloud resolving NWP model • Initial condition - GPS data, Direct assimilation of satellite data - Cloud resolving 4D-Var • Boundary condition - Global nonhydrostatic model

• Meso-ensemble

JMA AWSAMeDas・： 4-elements・： Rain gauge

Assimilation of GPS TPW data

AMeDAS (JMA) GPS Earth Observation Network(Geographical Survey Institute)

Assimilation of GPS TPW data

w/o GPS with GPS wsfc (with GPS) - wsfc (w/o GPS)

Analysis of TPW

Heavy rain event 30 June 2004

w/o GPS

with GPS

Observed heavy rain is predicted by assimilation of GPS TPW data. Shoji et al. (2005)

Impact of GPS TPW data

Assimilation of GPS occultation data

CHAMP/ISDC (GFZ) : Challenging Mini-Satellite Payload for Geoscientific Research and ApplicationInformation System and Data Center

GPS

CHAMP

occultation observation

Assimilation period 　 00-06 UTC 16 July 2004

grey ：１ｓｔ guess

black ；observation

Heig

ht

(ｋ

m)

Reflection ×106

CNTL

Radar AMeDAS 09-12UTC

Impact of CHAMP

The CHAMP occultation data moisten the lower atmosphere and yield observed precipitation in MSM.

CNTL+CHAMP

FT=6

Initial 06UTC 16 July 2004

Seko et al. (2005)

Further activities MRI/JMA

• Asian THORPEX

• WWRP Beijing Olympic 2008 Forecast Demonstration Program /Research and Development Program

- participate in MEP component

Meso ensemble experiment for Niigata heavy rain in July 2004

03UTCObservation 00UTC 13 July 2004

Routine hydrostatic MSM prediction from 12UTC 12 July 2004

06UTC

FT=12 FT=15 FT=18

Downscale experiment of weekly ensemble prediction Initial 12 UTC 12 July 2004　 T106 Global EPS

CONTROL

Member M03p

RA

control

0

2

4

6

8

10

12

14

16

18

20

0 10 20 30 40 50

コントロール

01p-12p

01m-12m

RA

M07m

M03p

RA

0

20

40

60

80

100

120

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180

200

0 20 40 60 80 100 120 140 160 180 200

コントロール

01p-12p

01m-12m

RA

Control

Precipitation in a rectangle over northern Japan 400×250km by Global EPS

FT=00-06

FT=

12-18

Mean precipitation extreme value

Only very weak rain in GSM

M03p

10 km MSM downscale experiment of EPS

10kmNHM Control

Member 'M03p'

FT=06 FT=18

RA

MARF

Control

0

2

4

6

8

10

12

14

16

18

20

0 10 20 30 40 50

コントロール01p-12p01m-12mm00m03p, m03pm00RAMARF

M03p

FT=00-06

FT=

12-18

Mean precipitation extreme value

RA

MARF

Control

0

20

40

60

80

100

120

140

160

180

200

0 20 40 60 80 100 120 140 160 180 200

コントロール01p-12p01m-12mm00m03p, m03pm00RAMARF

M03p

M07mM07m

Precipitation in a rectangle over northern Japan 400×250km by 10 km MSM downscale experiment of EPS

Location of precipitation is adjusted to south and line-shaped intense rain is reproduced

Downscaling experiment of the global EPS with MSM

FT=12 FT=15 FT=18

Observation 00UTC 13 July 2004 03UTC 06UTC

Summary

•JMA Mesoscale NWP started 2001. Several factors (model, initial and lateral boundary conditions) have been modified, and the performance has improved.

•Data assimilation of mesoscale data using variational method is the key factor.

•Significant improvement of GSM and RSM also contributed to MSM through the LBC.

•Further updates are scheduled in the operational system by 2008.

• Research and developments are underway to realize dynamical prediction of heavy rain.

•Mesoscale NWP is now entering a new stage.