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Global and regional OSEs at JMA

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

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Global and regional OSEs at JMA

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  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 Data cut-off time : 2h20min. Cycle Analysis 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) w/o AP-RARS 06 UTC 25 Sep. 2006 Data from Beijing and Crib Point were provided by AP-RARS with 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 EARS with 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 Data not used mainly due to irremovable biases of data (or model) QI threshold • Thinning: • One datum in a 200 km x 200 km box, • in 6-hour assimilation window (test 1) • in every one hour (test 2)

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

  20. Sapporo Kushiro Hakodate Akita Niigata Sendai Fukui Matsue Tokyo Hiroshima Nagano Fukuoka Shizuoka Nagoya Tanegashima Oosaka Murotomisaki Naze Okinawa Ishigaki-jima Weather Radars of JMA Doppler radar used in the analysis for MesoScale Model PINK 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 Thinning & Quality control • Averaged data • (resolution) • 5km (radius) • 5.625 deg.(azimuth) • 15 pre-set elevation angles

  22. All data2D thinning3D thinning 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) Threat scores of 3-hour precipitation RMSE of wind speed of six-hour forecasts against radiosondes Height (hPa) Threshold value (mm/3hour) RMSE (m/s) Green: with Doppler velocity of Tokyo radar (w. 3D thinning) Red: w/o Doppler velocity of Tokyo radar

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

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

  27. Ground-based GPS observation • 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.

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

  29. C B A Model topography Actual topography 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

  30. Statistical scores for 3-hour precipitation(1 to 13 Sep. 2006) 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 Positive impact at FT=9 and after Precipitation is suppressed in early stage

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

  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

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