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Ko KOIZUMI Numerical Prediction Division Japan Meteorological Agency

Assimilation of various observational data using JMA meso 4D-VAR and its impact on precipitation forecasts. Ko KOIZUMI Numerical Prediction Division Japan Meteorological Agency. Hydrostatic MSM Dynamics hydrostatic, spectral model primitive equation no acoustic mode model top at ~ 0 hPa

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Ko KOIZUMI Numerical Prediction Division Japan Meteorological Agency

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  1. Assimilation of various observational data using JMA meso 4D-VAR and its impact on precipitation forecasts Ko KOIZUMI Numerical Prediction Division Japan Meteorological Agency

  2. Hydrostatic MSM Dynamics hydrostatic, spectral model primitive equation no acoustic mode model top at ~ 0 hPa Moisture processes grid scale condensation cumulus parameterization Non-hydrostatic MSM(since Sep.2004) Dynamics non-hydrostatic, grid model fully compressible, non-hydrostatic equation specific treatment for acoustic mode model top at ~ 22 km Moisture processes bulk cloud microphysics (3-ice) cumulus parameterization JMA Mesoscale Model(input to VSRF system) • Common specifications • domain: 361 x 289 x 40, horizontal resolution 10 km • initial condition from 4D-VAR, boundary condition from RSM • forecasts are made within 1.5 hrs from initial time

  3. Model Areas RSM(20km L40) MSM(10km L40)

  4. Operational 4D-Var System • An incremental approach is taken with an inner loop model with resolution of 20 km L40. Inner forward : nonlinear full-physics model Inner backward : reduced-physics adjoint model(grid-scale condensation, moist convective adjustment, simplified vertical diffusion, simplified longwave radiation) • Consecutive 3-hour assimilation windows are adopted. • Minimization is limited up to 15 minutes of running time. • 40 nodes of Hitachi-SR8000E1 (80 nodes) are used.

  5. Radar-AMeDAS Precipitation Analysis JMA radar sites in Japan

  6. Radar-AMeDAS Precipitation Analysis 1. Radar echo intensity is converted to precipitation rate using. 2. Eight precipitation rates observed during one-hour are averaged to make estimation of one-hour precipitation amount. 3. The estimated precipitation amount is calibrated using rain-gauges and neighboring radar data.

  7. Scattering diagramof radar-AMeDAS and independent rain-gauge observation5808 cases during May to Sep. 1994

  8. Radar-AMeDAS Precipitation Analysis(as input to the data assimilation system) • Hourly precipitation amount data, provided with 2.5km resolution, are up-scaled to 20km resolution (inner-model resolution) and assimilated to MSM by the meso 4D-Var. • The same data are also used for verification of precipitation forecasts, after up-scaled to the model resolution (10km).

  9. Impact test of precipitation assimilation • 18-hour forecasts were made from 0,6,12 and 18UTC during 1-30 JUNE 2001. • Consecutive 3-hour forecast-analysis cycle was employed with 3-hour assimilation window. • Observational data : SYNOP, SHIP, buoys, aircraft data, radiosondes, AMVs, wind-profiler radars and temperature retrieved from TOVS by NESDIS • 3-hour precipitation forecasts are verified against radar-AMeDAS precipitation analysis

  10. Impacts of Precip. Assimilation(June 2001, 10km resolution) Threat score Bias Score 10mm /3h (h) (h) 30mm /3h Red: with Precip.Blue: w/o Precip. (h) (h)

  11. Statistical property of 3-hour precipitation of first 3 hour forecast [10km] (June 2001) w/o precip. assim. Appearance rate (log.) (mm/3hr forecast) Red: forecast Blue: observation 3-hour precipitation amount (mm/3 hour) 3-hour precipitation amount (mm/3 hour obs.)

  12. Statistical property of 3-hour precipitation of first 3 hour forecast [10km] (June 2001) with precip. assim. Appearance rate (log.) (mm/3hr forecast) Red: forecast Blue: observation 3-hour precipitation amount (mm/3 hour) 3-hour precipitation amount (mm/3 hour obs.)

  13. Limitation of precipitation Assimilationwith a variational method • Precipitation processes in NWP have “on-off” switches and it cannot be “turned on” by iterative calculation of 4D-Var if it started from “turned off” state (e.g. it is very dry in the first guess field). • For the successful precipitation assimilation, the background moisture field needs to be sufficiently accurate (e.g. moisture data seems to be more important).

  14. Precipitation assimilation does not always produce appropriate rain (Initial Time: 18UTC 23 March 2002) Observation 0-3 h forecast

  15. TCPW and rain-rate from satellite microwave imagers • Rain rate estimation: • Takeuchi (1997) • Empirical method • Only over the sea • TCPW estimation: • Takeuchi (1997) • Empirical method • Only over the sea • Using SST, SSW and 850hPa Temp. as external data. SSM/I(DMSP), TMI(TRMM) and AMSR-E(Aqua) TCPW RR

  16. Threat Score 1mm/3h Impact test of PW and rain-rate from SSM/I and TMI - 3-16 June 2003 - 18 hour forecasts made four times a day 10mm/3h (hour) w. SSM/I and TMI w/o SSM/I and TMI

  17. MWR Obs. (Local Time) 01:30JST (16:30UTC) 00JST Contribution of AMSR-E 12UTC 18UTC SSM/I 06 JST 18 JST • Coverage • Observation Time (Japan) • AMSR-E … 1:30 / 13:30 JST • 3 SSM/Is … 6-8 / 18-20 JST • Data availability • March - June, 2004 ( w/o AMSR-E ) • Very low … 03-06, 15-18UTC • March - June, 2005 ( with AMSR-E ) • Fill the data gap AMSR-E 00UTC 06UTC 13:30JST (04:30UTC) 12JST Analysis Time [ UT ]

  18. Impact Study of AMSR-E • Cycle Experiments • CNTL (without AMSR-E) … Operational MSM • TEST (with AMSR-E) … CNTL + AMSR-E • Data … TCPW and RR ( retrieved from AMSR-E) • Period • Summer … 15 samples ( July – August, 2004 ) • Winter … 15 samples ( January, 2004 ) • Case Study • Fukui Heavy Rain (2004) • “Assimilation of the Aqua/AMSR-E data to Numerical Weather Predictions”, Tauchi et, al., IGARSS04 Poster • Rainfall Verification • Threat Score • Summer • Heavy Rain (10mm/3hour) & Weak Rain (1mm/3hour) • Winter • Weak Rain (1mm/3hour)

  19. ---- w. AMSR-E ---- w/o AMSR-E Verification of Precipitation Forecasts Threat Score Summer 10mm/3hour Threat Score Summer 1mm/3hour • Threat score of heavy rain (summer) improved at almost all forecast time. • The score of weak rain was good or neutral for both summer and winter experiments. 3 6 9 12 15 18 3 6 9 12 15 18 Threat score Winter 1mm/3hour Y axis : Threat Score X axis : Forecast Time 3 6 9 12 15 18

  20. JMA wind-profiler network RAOB sites WPR sites (since 2001) WPR sites (since 2003) • 31 stations with about 100km distance • 1.3GHz wind-profiler radar observing up to about 5km every 10 min. • assimilated hourly • operational since spring 2001

  21. Heavy rain on Matsuyama city on 19th June 2001 w/o WPR with WPR observation FT=0-3 FT=3-6

  22. Wind at 850hPa level w/o WPR with WPR FT=0 FT=0 FT=0-3 FT=0-3

  23. Impact test on precipitation forecasts - 26 initials during 13 June and 7 July 2001 - forecast-analysis cycle was not employed - 25 WPR stations are used • Red line: 4D-Var with wind-profiler • Blue line: 4D-Var without wind-profiler Threat scores Forecast time (hour) Forecast time (hour)

  24. Doppler radars at eight airports

  25. Data selection policy of DPR radial wind- based on Seko et al. (2004) - • Data within 10km from radar are not used • Data of elevation angle > 5.9 degree are not used • Radar beam width is considered in the observation operator • Data thinning is made with about 20km distance

  26. Radar might observe several model levels at the same time Beam intensity is assumed as Gaussian function of distance from the beam center

  27. 動径風なし 動径風使用 風の解析 Forecast example (init. 2005/2/1 18UTC) FT=15 3 hour precipitation Observation with DPR w/o DPR 850hPa wind Analysis with DPR w/o DPR

  28. Statistical verification ofprecipitation forecasts - Winter experiment: 1-14 February 2004 - Summer experiment: 1-13 September 2004 Threat scores February experiment September experiment Forecast time (hour) Forecast time (hour) Red: with DPR Blue: w/o DPR - positive impact on moderate rain - impacts are not clear for weak rain (not shown)

  29. observation 19UTC Ongoing worksdevelopment of non-hydrostatic model-based 4D-Var Non-hydrostatic 4DVAR (FT=6-9) Hydrostatic 4DVAR (FT=6-9) 20UTC 21UTC 22UTC Non-hydrostatic 4DVAR (FT=9-12) Hydrostatic 4DVAR (FT=9-12) 23UTC 00UTC (init: 2004/7/17 12UTC)

  30. Summary • Assimilation of precipitation data improve precipitation forecasts, especially for the first few hours • Use of satellite microwave imager data (as TCPW and rain-rate) further improve the precipitation forecasts • Dense and frequent wind observation (WPR and DPR) have positive impact on moderate to heavy rain • Modification of assimilation method (hydrostatic based 4D-Var to non-hydrostatic based 4D-Var) could improve the forecasts even with the same observational data

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