1. Global and regional OSEs at JMA Ko KOIZUMI Numerical Prediction Division Japan Meteorological Agency

2. Contents Experiments with Global Spectral Model –Asia-Pacific RARS and EARS –MTSAT-1R Clear-Sky Radiance –BUFR AMV (incl. MTSAT-1R Hourly AMV) instead of SATOB Experiments with Meso-Scale Model –BUFR AMV (incl. MTSAT-1R Hourly AMV) instead of SATOB –Doppler radar radial wind –Ground-based GPS

3. Global Experiments Specification Model: Global Spectral Model TL319L40 Assimilation: –4D-Var method –Inner model resolution: T106L40 –Assimilation window: six hours –Six-hourly cycle Experiment period: one month each for summer and winter Forecasts: 216 hour forecasts once a day at 12 UTC

4. ATOVS used in Global Analysis Early Analysis Cycle Analysis Data cut-off time : 2h20min. Data cut-off time : 11h35min.(00 and 12 UTC) 5h35min.(06 and 18 UTC)

5. Coverage of RARS data EARS AP-RARS 2008.5.12

6. Analysis difference of 20hPa height (Early analysis – Cycle analysis) 06 UTC 25 Sep. 2006 Data from Beijing and Crib Point were provided by AP-RARS 06 UTC 25 Sep. 2006 Data from Beijing and Crib Point were provided by AP-RARS with AP-RARS w/o AP-RARS

7. Comparison of RMSE scores (winning % among 30 forecasts in September 2006) (forecast hours) Almost neutral for scores of troposphere

8. EARS (EUMETSAT Advanced Retransmission Service) EARS data (AMSU-A) at 12 UTC 17 June 2007 EARS data (AMSU-B) at 12 UTC 17 June 2007 Analysis difference of 500hPa height w/o EARSwith EARS

9. Comparison of RMSE scores (winning % among 30 forecasts in June 2007) (forecast hours) Positive impacts mainly on early hours of forecasts Difference of impacts of AP-RARS and EARS might be due to the difference of data amount

10. MTSAT-1R Clear-Sky Radiance Infrared 3 channel (6.5-7.0 μm) Averaging radiances of cloud- free pixels in a 16 x 16 pixel region (60km x 60km at nadir) Thinned to 2 x 2 degree longitude/latitude and to every two hours Variational bias correction applied

11. Comparison of RMSE scores (winning % among 31 forecasts in Aug. 2006 and Jan. 2007) August 2006 January 2007

12. Typhoon track forecasts (Typhoon center position errors in August 2006) RED: w/o MTSAT-1R CSR BLUE: with MTSAT-1R CSR

13. AMV in BUFR format (instead of SATOB) Larger amount of data, including hourly reports of MTSAT-1R AMV, are available Data selection using Quality Indicator (contained in the reports) is possible More strict data selection from larger amount of candidates improves the forecasts

14. Data selection strategy Thinning: One datum in a 2 degree x 2 degree box in the assimilation window (6 hours) Data not used mainly due to irremovable biases of data (or model) QI threshold

15. Comparison of RMSE scores (winning % among 30 forecasts in Sep. 2005 and Jan. 2006) September 2005 January 2006

16. Typhoon track forecasts (Typhoon center position errors in Sep. 2005) RED: with BUFR AMVs BLUE: with SATOB AMVs

17. Regional Experiments Specification (except for GPS experiment) Model: MesoScale Model –Non-hydrostatic grid model with 5km grid distance Assimilation: –4D-Var system based on a hydrostatic spectral model (former operational model) –Outer/ Inner resolution: 10km/20km –Assimilation window: six hours –Three-hourly cycle Experiment period: one or two weeks in a rainy season Forecasts: 33 hour forecasts were made six-hourly (03, 09, 15 and 21 UTC initials)

18. Data selection strategy Thinning: One datum in a 200 km x 200 km box, -in 6-hour assimilation window (test 1) -in every one hour (test 2) QI threshold Data not used mainly due to irremovable biases of data (or model)

19. Results of an experiment in 1-15 July 2007 RED: with SATOB AMVs GREEN: with BUFR AMVs (one datum per six hours) BLUE: with BUFR AMVs (one datum per one hour) Threat scores of 3-hour precipitation forecast against analyzed precipitation RMSE of wind speed forecasts at ft=3 against radiosonde observation in Japan

20. Weather Radars of JMA Kushiro Sendai Tokyo Shizuoka Nagano Nagoya Oosaka Murotomisaki Sapporo Akita Niigata Fukui Matsue Hiroshima Fukuoka Tanegashima Naze Okinawa Ishigaki-jima Hakodate PINK Doppler radar used in the analysis for MesoScale Model YELLOW Doppler radar planned to be used in the analysis for MesoScale Model CYAN Not yet Doppler-ized

21. Preprocessing of the data Original data 3D volume scan (resolution) -500m (radius) -0.7deg.(azimuth) -15 pre-set elevation angles Averaged data (resolution) -5km (radius) -5.625 deg.(azimuth) -15 pre-set elevation angles Thinning & Quality control

22. Thinning (2D or 3D) Considering only two- dimensional data distribution on a cone of an elevation angle Easy to implement but too dense near the radar Considering three- dimensional distribution of all data 20km horizontally 0.5km vertically

23. Quality Control Following data are rejected Number of samples in an averaging volume is smaller than or equal to 10 Range of velocity in an averaging volume is larger than 10m/s Departure from first-guess is larger than 10m/s Velocity is lower than 5m/s –Coherent MTI algorithm sometimes works wrong with slow- moving particles Within 10km from the radar –To avoid backscattering noise Elevation angle is larger than 5.9 degree –To avoid contamination from raindrop falling

24. Statistical scores (8-17 June 2006) Threshold value (mm/3hour) Green: with Doppler velocity of Tokyo radar (w. 3D thinning) Red: w/o Doppler velocity of Tokyo radar Threat scores of 3-hour precipitation RMSE of wind speed of six-hour forecasts against radiosondes RMSE (m/s) Height (hPa)

25. Impact of different thinning method Threshold value (mm/3hour) Green: 3D thinning Red: 2D thinning Threat scores of 3-hour precipitation

26. Observation w. Tokyo radar Doppler vel. (3D thinning) w/o Tokyo radar Doppler vel. Observation An example of 3-hour precipitation forecast FT=9 FT=12 w. Tokyo radar Doppler vel. (3D thinning)

27. Over 1,000 GPS receivers are owned by Geographical Survey Institute A real-time analysis system of ZTD and PW has been installed in JMA headquarter. Ground-based GPS observation

28. GPS real-time analysis shows good agreement with radiosonde observation (August 2005 and January 2006)

29. Quality control etc. PW value is modified according to model topography PW smaller than 1mm or larger than 90mm is rejected A datum is rejected when the departure from first guess is larger than 8mm A datum is rejected when the departure is larger than 5mm and differs from the averaged departures of surrounding data (within 20km) for 5mm or larger No thinning applied Actual topography Model topography A B C

30. Statistical scores for 3-hour precipitation (1 to 13 Sep. 2006) Positive impact at FT=9 and after Precipitation is suppressed in early stage The experiment was performed with the hydrostatic spectral version of MSM and the same 4D-Var as in the other experiments except for 3- hour assimilation window

31. An example of 3-hour precipitation forecast (FT=6-9 from 00 UTC 6 Sep. 2006) Observationwith GPS PW w/o GPS PW Seems good, however … Analysis increments of specific humidity (for positive departure of PW) -6 -4 -2 0 2 4 6 8 (g/kg) Height (km) 0 2 4 6 8 10 When an integrated value is assimilated, the increment distribution depends on the system insufficient(?)m

32. Summary RARS –Improve the operational forecast –Impact depends on the amount of available data CSR of MTSAT-1R –Improve the forecast especially in boreal summer –Improve typhoon track forecast BUFR AMV –Advantage to SATOB AMV in data amount and QI –“more strict data selection from larger volume of candidates” is preferable to the forecast Doppler velocity –Impact is sensitive to data thinning Ground-based GPS –Positive impact can be acquired even from the near real-time data –Since the vertical distribution of analysis increment from vertically integrated observation (such as ZTD or PW) depends on the assimilation system, some modifications to the assimilation system might be able to enhance the impacts of the data