Possible contributions of MRI to COPS

Kazuo SAITO Head, 2nd Laboratory, Forecast Research Department

Meteorological Research Institute , ksaito@mri-jma.go.jp

1. Application of NHM to orographically-induced deep convection

2. Operational application of NHM3. Application of NHM-4DVAR to deep convection 4. Application to the WWRP Beijing Olympic 2008RDP5. Possible contributions to COPS

COPS 6th Workshop, 2008, 26-29 August 2005, Beijing

1. Application of NHM to orographically-induced deep convection

NHM; A community nonhydrostatic model for research and NWP developed by MRI/JMA

（ Ikawa and Saito, 1991: Tech. Rep. MRI, 28, 238pp.)

（ Saito et al., 2001: Tech. Rep. MRI, 42,133pp.)MCTEX (Maritime Continent Thunderstorm Experimen

t); Field campaign in 1995 by BMRC etc., (Keenan et al., 2000: Bull. AMS, 81, 2433-2455.)

Visible GMS image on 27 November 1995.

•Shallow convection in morning and sea-breeze front along the coast.

•Cloud merger along the east-west line-shaped convergence zone.

•Explosive growth of deep convection after the merging stage.

NHM was nested with the BMRC’s Limited Area Assimilation and Prediction System (LAPS).

Initial time 27 November 1995, 0830 CST

Left: Domain and orography of LAPS and 2.5 km-NHM. Inner rectangle shows the domain of the 1 km-NHM.

Right: Time sequence by 2.5 km-NHM.

• Maximum instantaneous surface rain intensity and the averaged rain rate.

• Maximum updraft and downdraft. • Maximum cloud top height and cloud amount (%).

Application of NHM (Saito et al., 2001: Mon. Wea. Rev. 129, 378-400.)

Result by 1 km NHM

Left: Visible GMS image on 27 November 1995.

Right: Corresponding numerical simulation by 1 km NHM.

•Shallow convection in morning and sea-breeze front along the coast.

•Cloud merger along the east-west line-shaped convergence zone.

•Explosive growth of deep convection after the merging stage.

Saito et al. (2001)

2. Operational application of NHM

Horizontal mesh

(resolution) Mapping721 x 577 (5 km) Lambert conformal

Levels 50 generalized hybrid

Model top 22060 m

Horizontal discretization Arakawa C

Horizontal advection Flux form 4th order with advection correction and time splitting

Gravity waves Time splitting

Sound waves Split-explicit (HE-VI)

Forecast period 33 hours (03, 09, 15, 21 UTC)15 hours (00, 06, 12, 18 UTC)

Initial conditions Meso 4D-Var (hydrostatic)

Lateral boundary 20km GSM (TL959 L60) 6 hourly

Prognostic variables U, V, W, P, , qv, qc, qi, qr, qs, qg,

TKE, l’2, qw’2, l’qw’

Moist physics 3 ice bulk microphysics with fall-out of cloud ice

Convection Kain-Fritsch scheme with water vapor trigger function

Turbulent closure Mellor Yamada Nakanishi Niino Level 3 (MYNN3)

•Start of operation with 10kmL40 (Mar. 2001)•Nonhydrostatic model with 3 ice microphysics (Sep. 2004)•Enhancement of resolution to 5kmL50 (Mar. 2006) •Implementation of MY3 closure model (May. 2007)

Domain and orography of MSM

The operational JMA nonhydrostatic mesoscale model. Saito et al., 2006: Mon. Wea. Rev., 134, 1266-1298. Saito et al., 2007; JMSJ, 85B, 271-304.

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Convective heavy rain in Kyushu on 22 July 2006

Observation

09 JST 21 July

3 hour precipitation on 22 July 2006

12 JST21 July

Observation 5 km NHM (MSM)

09 JST12 hour forecast from 1200UTC 21 July

3 hour precipitation on 22 July 2006

12 JST15 hour forecast from 1200UTC 21 July

Nonhydrostatic dynamics

5km50L

4D-Var

4D-Var

Nonhydrostatic dynamics

5km50L

Weak to moderate rain, (5mm/3hr, 40km)

QPF performance of operational MSM at JMA(Threat scores for 3 hour precipitation, Mar. 2001-Jan. 2008)

Intense rain, (10mm/3hr, 10km)

New physics

New physics

•Wind profiler data (Jun. 2001)•Radar precipitation analysis in 4D-Var (Mar. 2002) •Domestic ACARS data (Aug. 2002)•SSM/I precipitable amount (Oct. 2003)•QuikSCAT Seawinds (Jul. 2004) •Doppler radar radial winds (Mar. 2005)

QPF performance has been improving steadily in recent years by the virtue of implementation of NHM and the progress of data assimilation.

Doppler radar

rain

Deep convecti

on

ＧＰＳ receiver

ＧＰＳsatellite

Moist atmosphere

3. Application of NHM-4DVAR to deep convection (Kawabata et al.,2007: JMSJ, 85, 255-276.)

Doppler radar radial winds

PWV observed by GEONET

Doppler radar radial winds, GPS-PWV and surface AWS data are assimilated with 1-10 minute intervals in the 1 hour assimilation window to predict initiation of deep convection.

NHM-4DVAR; Cloud resolving 4D-VAR system based on TL/ADJ models of NHM developed by MRI/JMA

1.0 10 20 40 60

Forecast from the 2 km NHM-4DVAR analysis　 (15-16JST)

Observed Rain (16JST)

Deep convection

Observed deep convection and associated heavy rain were predictable with the 2 km 4D-VAR assimilation.

Gorecast (16JST)

Kawabata et al. (2007)

Assimilation of radar reflectivity with the 2 km NHM 4D-VAR

The warm rain cloud microphysical process has been implemented to ADJ model of NHM-4DVAR.

With the assimilation of the radar reflectivity and mesoscale data (Doppler radial winds, GPS-TPW and surface wind and temperature data ), location, horizontal size, and rainfall intensity of the observed heavy rain in Sep 2005 was reproduced.

POSTER DAP5 by T. Kawabata

For detail of NHM-4DVAR (control variables, observation operator, etc.,)

3500km

3000

km

110

0km

General Requirements on Configuration of B08RDP MEPS

Fine domain

1320km

Tier 115 km mesoscale ensemble up to 36

hour

Tier 22-3 km CRM experiments ca

se study

4. Application to the WWRP Beijing Olympic 2008 RDP

Participants Model IC IC perturbation LBC

NCEP*(USA)

WRF-NMM (L60M5)WRF-ARW (L60M5)

NCEP Global 3DVAR

Breeding Global EPS

MRI/JMA(Japan)

NHM(L40M11)

JMA Regional 4DVAR

Targeted Global SV

JMA RSM Forecast

MSC (Canada)

GEM(L28M16)

MSC Global 4DVAR

Targeted Global SV

MSCGlobal EPS

ZAMG & Meteo-Fr.

ALANDIN(L37M18)

ECMWF Global 4DVAR

ECMWF Global SV

ECMWF Global EPS

NMC/CMA(China)

WRF-ARW(L31M15)

WRF-3DVAR Breeding Global EPS

CAMS/CMA(China)

GRAPES(L31M9)

GRAPES-3DVAR

Breeding Global EPS

The 2007 Tier-1 MEP

*NCEP submitted results by global EPS in the 2007 experiment

0

5

10

15

20

25

6 12 18 24 30 36f orecast hours

RMS error

NMC/ CMACAMS/ CMANCEPMRI / J MAMSCZAMGcombi nati onspread

RMSE of 2m temperature RMSE of 2m RH

MRI/JMA scored best performance for most indexes in the 2007 preliminary experiment..

Application of Meso 4D-VAR Analysis toward the 2008 Experiment

Domain of Meso 4D-Var for B08RDP

System Meso 4D-Var for JMA meso-scale hydrostatic model

Grid number OUTER : 361 x 321 x 40 (Δx = 10km) INNER : 181 x 161 x 40 (Δx = 20km)

Assimilation window 3-hour (iteration MAX = 30)

Observation Data ・ Conventional Observation 　 (surface, ship, buoy, upper, etc.) ・ PWV, rainfall intensity observed by satellites (SSMI, TMI, AMSR-E) ・ Sea level wind of QuickSCAT ・ Analyzed rainfall distribution (Japan area) ・ Doppler Radar RW data (Japan area) ・ 3 hour rainfall amount (China area)

Assimilation (4D-Var)RANAL

NHM 36hour forecast

06UTC 12UTC09UTC

time

Kunii (2007)

Effect of Meso 4D-VAR and surface rainfall assimilation

OBS

Initial : 2007 07 29 12UTCFT = 30 hour

RA MA MA with srain

Kunii (2007)

2000km

1500

km

300k

m

Design of a supposed DA experiment to predict deep convection in COPS

COPS domain

300km

Meso 4D-VAR with 10 km　

(or MM-5 4D-VAR of Univ. Hohenheim)

NHM-4DVARwith 2 km

3. Possible contributions to COPS

20km JMA GSM or ECMWF global model

Summary

•High quality, high density data observed in COPS are very attractive and challenging for the cloud resolving 4D-Var. Collaboration between MRI and COPS scientists will be beneficial for both groups.

•The 2nd meeting of the WWRP WG on Mesoscale Weather Forecasting Research will be held in Tokyo on 17-18 March and data assimilation intercomparisons test-bed will be discussed. COPS observation field may become a strong candidate of the test-bed.