Short Range NWP Strategy of JMA and Research Activities at MRI IAMAS2005, 11 August 2005, Beijing 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

Essential factors in the mesoscale NWP Model (Domain, Resolution, Dynamics, Physical processes) Initial condition (Analysis method, Data) Boundary condition

extra-tropical cyclone Scale of atmospheric phenomena year Synoptic forcing planetary wave month extra-tropical cyclone week mesoscale typhoon day front micro scale heavy rain Macro scale thunder storm hour cumulus conventional aerological observation -300 km, 2/day local wind turbulence minute conventional NWP model 6Dx = 100-200 km, 2-4/day second 1m 10m 100m 1km 10km 100km 1000km 10000km

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 RSM RSM 20 km L40, 6480 km x 5120 km, 51 hours , 2 times a day Hydrostatic spectral model, nested with GSM (60 km L40) MSM RSM

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) 03 UTC 04 UTC 05 UTC 06 UTC 3-h Forecast with RSM (20km L40) from 00UTC 1-h Forecast with MSM 1-h Forecast with MSM 1-h Forecast with MSM 18-h Forecast with MSM Physical Initialization OI Analysis + Physical Initialization OI Analysis + Physical Initialization OI Analysis + Physical Initialization Precipitation Data Conventional Data Precipitation Data Conventional Data Precipitation Data Conventional Data Precipitation Data (For Analysis at 06 UTC)

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

Concept of ４D Var Jo Jo Jb Jo Cost function : Gradient of cost function : Adjoint model Model Penalty term Observation parameter observation Jo initial time Jo First guess observation Jo Jb Time integration of NWP model observation analysis Jo observation 21UTC 00UTC time Assimilation window ３hrs

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 Observation FT=15-18 3 hour accumulated rain for FT=18 hr Initial 12 UTC 9 September 2001 Ishikawa and Koizumi (2002)

Threat scores (40km verification grid) 1mm/3h 10mm/3h June 2001 (h) (h) Sep. 2001 Red: 4D-Var Blue: routine (h) (h)

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) WITHOUT ACARS DATA Observation (AMEDAS) Shear line WITH ACARS Location of the observed local shear line near Tokyo is corrected with ACARS data.

Assimilation of precipitation and TPW data retrieved from TMI and SSM/I (October 2003-) Defense Meteorological Satellite Program Special Sensor Microwave / Imager TRMM Microwave Imager

OSE for 00UTC, 25 Aug 2003 Water vapor field was improved Sato (2003) Without SSM/I and TMI 3 hour rain at FT=18 TPW by SSM/I and TMI Observation With SSM/I and TMI Sato (2003)

Performance of MSM with TMI and SSM/I Period 2003 June 3～16 （2weeks 56 forecasts）10 km verification grid Threat score 0.40 CNTL 0.38 TEST 0.36 1mm/3hr 0.34 0.32 0.30 0.28 3 6 9 12 15 18 FT 0.22 CNTL 0.20 TEST 0.18 10mm/3hr 0.16 0.14 0.12 0.10 3 6 9 12 15 18 FT

Assimilation of QuikSCAT SeaWinds July 2004 - NASA Observation 30ﾟN T0207 （HALONG）

Precipitation FT=8-9. Initial: 12 UTC 18 July 2003 Threat scores 10km 30mm/3h, 3-19 June 2003 SeaWinds 10UTC 18 July 2003 Ohashi (2004)

Non-hydrostatic MSM (JMA-NHM) September 2004- Developed by joint work between MRI and NPD/JMA HE-VI, stable computation with LF scheme Dt=40 sec Fully compressible, flux form 4th order advection with FCT Direct evaluation of buoyancy from density perturbation 3-class bulk microphysics (water vapor, cloud water, rain, cloud ice, snow, graupel) Modified Kain-Fritsch convective parameterization scheme Targeted Moisture Diffusion Box-Lagrangian scheme for rain and graupel Full paper submitted to M.W.R. (Saito et al., 2005)

Modification of the Kain-Fritsch convective parameterization Original K-F scheme. FT=12. Observed 3 hour accumulated precipitation (mm) at 21 UTC. 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. Modified K-F scheme. FT=12.

Case Study of Non-hydrostatic MSM Heavy rainfall event (18 July 2003, FT=15h) Snowfall (13 January 2004, FT=18h) We are replacing the present hydrostatic meso-scale model by a non-hydrostatic mesoscale model with explicit representation of cloud physics in this September. This figure demonstrates advantages of the non-hydrostatic model even though the spatial resolution is the same, 10 km and 40 levels.Those positive impacts are mainly due to the sophisticated precipitation processes in NHM, especially in snowfall prediction in winter. In MSM, condensed water is instantaneously removed from the atmosphere as precipitation. Radar-AMeDAS observation Hydrostatic MSM Non-hydrostatic MSM

Performance of Non-hydrostatic MSM NH-MSM Five-month total scores over forecast time 03, 06, 09, 12, 15, 18h against 3hourly rain analysis at 20 km grid These panels are statistical verifications. Red lines are NHM and black lines are MSM. The bias score is significantly reduced in winter, although it is still much larger than unity. The threat score is almost the same, but if one-grid difference in position is tolerated, the advantage of NHM is evident. This means that NHM predicts heavy precipitation areas closer to observed positions. Five-month total scores at FT=18h against analysis of height

Performance of JMA Mesoscale Model Bias scores 10km 10mm/3hr NHM High bias scores in winter were removed by NHM

Assimilation of Doppler radar radial winds March 2005- Without DPR wind FT=15 With DPR winds FT=15 Observation Koizumi and Ishikawa (2005)

Performance of MSM has been improved Threat scores 10 km, 10mm/3hr for FT=6-9 0.23 0.17 0.11 NHM 4D-Var

Major Operational Changes in GSM Boundary conditions for MSM 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) Major Operational Changes in RSM Enhancement of vertical resolution from L36 to L40 (Mar. 2001) 4D-Var (Jun. 2003) Target moisture diffusion (Apr. 2004)

Improvement of GSM performance 500 hPa Height 500 hPa Temperature Cumulus, ATOVS,etc. 4D-Var 3D-Var 4D-Var Significant improvement by major changes (cumulus, ATOVS, etc.) in May 2003. Significant improvement by 3D-Var in September 2002.

RMSE of 500 hPa Height 1991-2005 11 years 3 years Improvement in the recent 3 years (2002-2005) exceeds that in 10 years before 2002.

Performance of GSM in RMSE region 2 Day 1 Day Contributes to RSM forecast through the lateral B.C.

4D-Var in RSM (June 2003-) 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.

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 condition High 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-) - 10kmL40 → 5km L50 (Mar. 2006) - 4 times a day → 8 times a day (Mar. 2006) - 33-hr forecast (Mar. 2007) Radar-AMeDAS obs. 5km Nonhydro. MSM 10km MSM (18 July 2004 21UTC, FT=6-9)

20km (TL959) Global Model (2007-) - 60kmL40 → 20kmL60 (Mar. 2007) - Twice a day → 4 times a day (Mar. 2007) - Supply latest B.C. to MSM directly (19 Jun 2001 12UTC, FT=12) For the 20km global model, a semi-Lagrangian method will be adopted for high speed computation. The operation frequency will be increased from twice a day to four times a day. The assimilation method will be upgraded from 3D-Var to 4D-Var. In a preliminary result shown here, the 20km resolution global model reproduces a detailed structure of rainfall around Japan, compared to the current 60 km resolution model. 60km GSM 20km GSM Radar-AMeDAS 12-h rain

Nonhydｒostatic 4D-Var (2008-) 5 km L50, 3 hour assimilation windows Incremental approach using a 10-km version of nonhydrostatic MSM for inner loop UL: Radar-AMeDAS 3-h rain UR: 12 hr forecast Meso 4DVar LL: 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

Assimilation of GPS TPW data JMA AWS AMeDas ・：4-elements ・：Rain gauge AMeDAS (JMA) GPS Earth Observation Network (Geographical Survey Institute)

Assimilation of GPS TPW data Heavy rain event 30 June 2004 Analysis of TPW wsfc (with GPS) - wsfc (w/o GPS) w/o GPS with GPS

Impact of GPS TPW data Shoji et al. (2005) w/o GPS with GPS Observed heavy rain is predicted by assimilation of GPS TPW data. Shoji et al. (2005)

Assimilation of GPS occultation data CHAMP/ISDC (GFZ) : Challenging Mini-Satellite Payload for Geoscientific Research and Application Information System and Data Center grey：１ｓｔ guess black；observation Height (ｋm) Reflection ×106 occultation observation GPS Assimilation period 　00-06 UTC 16 July 2004 CHAMP

Impact of CHAMP FT=6 Initial 06UTC 16 July 2004 CNTL CNTL+CHAMP Radar AMeDAS 09-12UTC FT=6 Initial 06UTC 16 July 2004 The CHAMP occultation data moisten the lower atmosphere and yield observed precipitation in MSM. 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 Observation 00UTC 13 July 2004 03UTC 06UTC FT=12 FT=15 FT=18 Routine hydrostatic MSM prediction from 12UTC 12 July 2004

Downscale experiment of weekly ensemble prediction Initial 12 UTC 12 July 2004　T106 Global EPS CONTROL Member M03p ５図　６図と同じ。メンバー'M03p'

Precipitation in a rectangle over northern Japan 400×250km by Global EPS FT=12-18 M03p M03p Mean precipitation FT=00-06 extreme value Only very weak rain in GSM

10 km MSM downscale experiment of EPS 10kmNHM Control FT=06 FT=18 Member 'M03p' ５図　６図と同じ。メンバー'M03p'

Precipitation in a rectangle over northern Japan 400×250km by 10 km MSM downscale experiment of EPS FT=12-18 M03p M03p M07m M07m Mean precipitation FT=00-06 extreme value

Downscaling experiment of the global EPS with MSM Observation 00UTC 13 July 2004 03UTC 06UTC FT=15 FT=18 FT=12 Location of precipitation is adjusted to south and line-shaped intense rain is reproduced

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.