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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

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Short Range NWP Strategy of JMA and Research Activities at MRI

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  1. 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

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

  3. Scale of atmospheric phenomena year Synoptic forcing planetary wave month extra-tropical cyclone week mesoscale typhoon front day 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 100km 1000km 1m 10m 100m 1km 10km 10000km

  4. 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

  5. Performance of JMA Mesoscale Model • Threat scores 10km 10mm/3hr • Threat scores 40km 10mm/6hr Performance of MSM has been improving for both weak and moderate rains

  6. 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)

  7. 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.

  8. Physical Initialization OI Analysis +Physical Initialization OI Analysis +Physical Initialization OI Analysis +Physical Initialization 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 Precipitation Data Conventional Data Precipitation Data Conventional Data Precipitation Data Conventional Data Precipitation Data (For Analysis at 06 UTC)

  9. 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.

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

  11. 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

  12. 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)

  13. Threat scores (40km verification grid) 1mm/3h 10mm/3h June2001 (h) (h) Sep.2001 Red: 4D-Var Blue: routine (h) (h)

  14. 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.

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

  16. 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

  17. OSE for 00UTC, 25 Aug 2003 Water vapor field was improved 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)

  18. 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

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

  20. 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)

  21. 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)

  22. 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.

  23. Case Study of Non-hydrostatic MSM Heavy rainfall event (18 July 2003, FT=15h) Snowfall (13 January 2004, FT=18h) Radar-AMeDAS observation Hydrostatic MSM Non-hydrostatic MSM

  24. Performance of Non-hydrostatic MSM MSM NH-MSM Five-month total scores over forecast time 03, 06, 09, 12, 15, 18h against 3hourly rain analysis at 20 km grid Five-month total scores at FT=18h against analysis of height

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

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

  27. 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

  28. 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)

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

  30. 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.

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

  32. 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.

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

  34. Performance of RSM improved 4D-Var Time series of RMSE for 500 hPa field Contribute to MSM forecast through the lateral B.C.

  35. 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

  36. 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)

  37. 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) • 60km GSM 20km GSM Radar-AMeDAS 12-h rain

  38. 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)

  39. 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

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

  41. 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

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

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

  44. Impact of CHAMP 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)

  45. Further activities MRI/JMA • Asian THORPEX • WWRP Beijing Olympic 2008 Forecast Demonstration Program /Research and Development Program - participate in MEP component

  46. 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

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

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

  49. 10 km MSM downscale experiment of EPS FT=06 FT=18 10kmNHM Control Member 'M03p' ５図　６図と同じ。メンバー'M03p'

  50. 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 extreme value FT=00-06

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