1. Possible contributions of MRI to COPS Kazuo SAITO Head, 2 nd 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 NHM 3. Application of NHM-4DVAR to deep convection 4. Application to the WWRP Beijing Olympic 2008RDP 5. Possible contributions to COPS COPS 6 th Workshop, 2008, 26-29 August 2005, Beijing

2. 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 Experiment); 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.

3. 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.)

4. 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)

5. 2. Operational application of NHM Horizontal mesh (resolution) Mapping 721 x 577 (5 km) Lambert conformal Levels50 generalized hybrid Model top22060 m Horizontal discretizationArakawa C Horizontal advectionFlux form 4th order with advection correction and time splitting Gravity wavesTime splitting Sound wavesSplit-explicit (HE-VI) Forecast period33 hours (03, 09, 15, 21 UTC) 15 hours (00, 06, 12, 18 UTC) Initial conditionsMeso 4D-Var (hydrostatic) Lateral boundary20km GSM (TL959 L60) 6 hourly Prognostic variables U, V, W, P, , q v, q c, q i, q r, q s, q g, TKE,  l ’ 2, q w ’ 2,  l ’q w ’ Moist physics3 ice bulk microphysics with fall-out of cloud ice ConvectionKain-Fritsch scheme with water vapor trigger function Turbulent closureMellor 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.

6. Convective heavy rain in Kyushu on 22 July 2006

28. Observation 09 JST 21 July 3 hour precipitation on 22 July 2006 12 JST21 July

29. Observation 5 km NHM (MSM) 09 JST 12 hour forecast from 1200UTC 21 July 3 hour precipitation on 22 July 2006 12 JST 15 hour forecast from 1200UTC 21 July

30. Nonhydrostatic dynamics 5km50L 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 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.

31. Doppler radar rain Deep convectio n ＧＰＳ receiver ＧＰＳ satellite 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

32. 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)

33. 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.,)

34. 3500km 3000km 1100km General Requirements on Configuration of B08RDP MEPS Fine domain 1320km Tier 1 15 km mesoscale ensemble up to 36 hour Tier 2 2-3 km CRM experiments case study 4. Application to the WWRP Beijing Olympic 2008 RDP

35. ParticipantsModelICIC perturbationLBC NCEP* (USA) WRF-NMM (L60M5) WRF-ARW (L60M5) NCEP Global 3DVAR BreedingGlobal 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 MSC Global EPS ZAMG & Meteo-Fr. ALANDIN (L37M18) ECMWF Global 4DVAR ECMWF Global SV ECMWF Global EPS NMC/CMA (China) WRF-ARW (L31M15) WRF-3DVARBreedingGlobal EPS CAMS/CMA (China) GRAPES (L31M9) GRAPES- 3DVAR BreedingGlobal EPS The 2007 Tier-1 MEP *NCEP submitted results by global EPS in the 2007 experiment RMSE of 2m temperature RMSE of 2m RH MRI/JMA scored best performance for most indexes in the 2007 preliminary experiment..

36. 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 06UTC12UTC09UTC time Kunii (2007)

37. Effect of Meso 4D-VAR and surface rainfall assimilation OBS Initial : 2007 07 29 12UTC FT = 30 hour RAMAMA with srain Kunii (2007)

38. CNTL M02p M04p M03m 3km grid 15km grid 3km grid 15km grid 3km grid 15km grid 3km grid 15km grid Downscale cloud resolving EPS inTier-2 -Three hour rainfall of Tier-2 was projected to the grids of Tier-1. -Rainfall region of Tier-2 is similar to that of Tier-1, except M04p. Is Tier 1 enough for the rainfall forecast? 18UTC Rank of 3 hour rainfall Seko (2007)

39. 2000km 1500km 300km 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-4DVAR with 2 km 3. Possible contributions to COPS 20km JMA GSM or ECMWF global model

40. 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.