1. Recent activities on AMSR-E data utilization in NWP at JMA Masahiro Kazumori, Koichi Yoshimoto, Takumu Egawa Numerical Prediction Division Japan Meteorological Agency 2-3 June, 2010 AMSR-E Science Team Meeting, Huntsville, AL, U.S.A.

2. Outline Status of JMA NWP models and Microwave imager data utilization Verification of AMSR-E TPW retrieval algorithm with global GPS TPW data Application to SSMIS TPW retrieval and the assimilation experiment in JMA NWP Expectations for Microwave imager data Observational local time Data latency Summary

3. JMA NWP models Global Model (GSM)Meso Scale Model (MSM) PurposesShort- and medium-range forecastVery-short-range forecast Forecast domainGlobeJapan and its surrounding areas Grid size and/or number of grids 0.1875 deg. (TL959)5 km / 721 x 577 Vertical levels / Top60 / 0.1 hPa50 / 21,800 m Forecast hours (Initial time) 84 hours (00, 06, 18 UTC) 216 hours (12 UTC) 15 hours (00, 06, 12, 18 UTC) 33 hours (03, 09, 15, 21 UTC) Analysis4D-Var

4. MW Imager data utilization in JMA For Global Model: Radiance assimilation Brightness Temperature in clear sky condition For Meso scale Model: Retrieval Assimilation Total Precipitable Water(TPW) and Rain Rate (RR) Data thinning : 200km grid box QC : cloud screening and bias correction Colored point data are actually assimilated.

5. Recent update in MSM Ground based GPS TPW data in Japan GPS TPW data in Japan was introduced in operational JMA MSM DA system in Oct. 2009. The GPS data provide accurate and periodic TPW information over land. Improvements of rain prediction were confirmed in heavy rain cases. Atmospheric moisture information is essential to produce better rain forecast. Also global GPW TPW data set are available in JMA for verifications of NWP model’s TPW and satellite TPW products. GPS data are delivered from Geospatial Information Authority of Japan (GSI) and converted to TPW products in JMA. Without GPS With GPS Analyzed precipitation Three-hourly accumulated precipitation of 3-hour forecasts from 20 Jul. 2009 at an initial time of 21 UTC. From the left, analyzed precipitation, the forecast of Test (with GPS TPW) and that of Control (without GPS TPW).

6. Verification of AMSR-E TPW products with global GPS TPW data AMSR-E and GPS collocation criteria: GPS altitude <= 200m, Spatial diff. <= 20km, Time diff. <= 10 min. Period: 20 Jun. – 20 Aug. 2009 ZTD ：Zenith Tropospheric Delay ZHD : Zenith Hydrostatic Delay ZWD : Zenith Wet Delay Locations of collocated GPS Data (35 sites) GPS analysis ・ GPS satellite ephemeris : final ephemeris of International Global Navigation Satellite System Service (IGS). ・ GPS data (RINEX) : IGS station ・ Software : GIPSY/OASIS-II

7. JAXA-L2 Verification of AMSR-E TPW products with global GPS TPW data Scatter diagram of TPW GPS vs. AMSR-E NEW The National Snow and Ice Data Center (NSIDC)

8. Verification of AMSR-E TPW products by global GPS TPW data set TPW’s time sequences for NEW, JAXA-L2, and NSIDC products CHICHIJIMA Chatham Island

9. Time sequence of observed hourly rain fall in Yamaguchi prefecture Hourly Rainfall (left axis) Total Rainfall (right axis) A case study: Assimilation of SSMIS TPW & RR in MSM Heavy rain case in Japan July 19 – 26, 2009 00UTC Jul. 21, 2009 24hr observed rainfall MTSAT IR image The average year value for July’s one month rainfall

10. Data coverage of Microwave Imager data in JMA MSM MSM analyses were executed in every 3 hour (00,03,06,09,12,15,18 and 21UTC) SSMIS TPW and RR assimilation period : July 19 to 26, 2009 33 hours forecasts were produced from 03,09,15 and 21UTC initial. SSMIS data is available in these analysis time Cntl (W/O SSMIS) Test (With SSMIS) 0003060912 15 1821 0003060912 15 1821 Red : F13 SSMI Blue : TRMM TMI Light Blue : Aqua AMSR-E Green : F16 SSMIS Purple : F17 SSMIS

11. Impact on moisture analysis in July 20 TPW Analysis difference (Test-Cntl) Generally, assimilation of SSMIS intensify moisture flow in the analysis. Analyzed TPW field in Test (with SSMIS) 03UTC09UTC15UTC21UTC

12. Jul. 20 15UTC INITIAL FT=0 12 TPW DIFF (TEST-CNTL)TEST TPW

13. FT=1 [hour] 13 TPW DIFF (TEST-CNTL)TEST TPW

14. FT=2 14 TPW DIFF (TEST-CNTL)TEST TPW

15. FT=3 15 TPW DIFF (TEST-CNTL)TEST TPW

16. FT=4 16 TPW DIFF (TEST-CNTL)TEST TPW

17. FT=5 1-4 March 2010 17 TPW DIFF (TEST-CNTL)TEST TPW

18. FT=6 18 TPW DIFF (TEST-CNTL)TEST TPW

19. FT=7 19 TPW DIFF (TEST-CNTL)TEST TPW

20. FT=8 20 TPW DIFF (TEST-CNTL)TEST TPW

21. FT=9 21 TPW DIFF (TEST-CNTL)TEST TPW

22. FT=10 22 TPW DIFF (TEST-CNTL)TEST TPW

23. FT=11 23 TPW DIFF (TEST-CNTL)TEST TPW

24. FT=12 24 TPW DIFF (TEST-CNTL)TEST TPW

25. Impact on Rain Forecast Radar observation CNTL(w/o SSMIS)TEST (with SSMIS) TEST-CNTL TPW DIFF TEST TPW [mm] Valid Time: Jul. 21 12JST FT=12 3hr rain FT=12 Strong rain band appeared, but, the crossing time of the rain band was not improved. Increased TPW in moist area, decreased in dry area. SSMIS intensified the moisture flow in the forecast

26. Observational Local Time For the purpose of operational use of satellite microwave imager data in NWP, observational local time is a key element. NWP centers use 6hrs assimilation time window. Continuity of MW measurements in A-train is indispensable. 12 18 06 00 13:30 Light Blue : Aqua/AMSR-E Purple : DMSP F-16/SSMIS Green : DMSP F-17/SSMIS Orange : Coriolis/WindSat Dark black points indicate WindSat data in 6-hrs time window

27. Data Latency Data latency for AMSR-E (JAXA) Data latency for ATOVS (MSC) Data latency for AMSR-E (Global) Data latency for MTSAT Timely data delivery is also important for the use of satellite data in operational NWP. Especially, regional analysis demand strict cut off time for data receiving. MSM requires 50min cut off time after the analysis time for every analysis (8 time/day). Direct receiving in the frame work of WMO RARS and EARS are suitable for the regional data use for ATOVS.

28. Summary TPW data from MW-Imager play important role for accurate rain forecasts in MSM. TPW retrieval algorithm was verified with ground based GPS TPW data. Improvement was found compared with current JAXA L2 product, however, there is room for further improvement. NSIDC products showed better accuracy in GPS TPW verification. The algorithm was applied for F-16 and F-17 SSMIS. The retrieved TPW and RR were assimilated in JMA MSM for a heavy rain case in Japan. Assimilation of new SSMIS TPW data produced strong rain band forecast, but the forecasted rain band location was not improved. Data coverage is a key issue for satellite data utilization in operational NWP. Large coverage in each analysis is expected with timely data delivery. AMSR-E observation in afternoon orbit (A-train) is indispensable.

29. Backup slides

30. Comparison between RAOB and GPS (Spatial diff.<30km, altitude diff. < 200m)

31. GPS Remote Sensing Receiver GPS satellite Vapor Pseudo Range Wet Delay Zenith Tropospheric Delay = Zenith Hydrostatic Delay ＋ Zenith Wet Delay GPS ephemeris GPS software （ GIPSY ） GPS observation data (RINEX) ZTD Surface Pressure, Temperature TPW Conversion Procedure

32. Other data’s coverage in MSM

33. : Observed brightness temperature : Atmospheric Transmittance Theoretical basis of the algorithm Vertical mean temperature of atmosphere and ocean surface system Determination of by pre-defined LUT as a function of frequency, incidence angle, SST and SSW Step1 Step3 Step2 Step4 Step5 Step6 Initial atmospheric transmittance is set as exp(-0.2) Calculation of mean emission temperature by using Eq. (1-4) Calculation of Transmittance (V pol. & H pol.) by using Eq. (1-3) Calculation of new transmittance Determination of by pre-defined LUT of and T850 based on RAOB Iteration calculation of Step 3 – 6 to obtain optimized Transmittance : Mean emission temperature : Ocean surface emissivity Ta is defined as the average of upward Tu and downward Td Water vapor Ta is equal to cloud liquid water Ta (1.1) (1.2) (1.3) (1.4) Microwave Brightness temperature Eq.

34. Retrieval of TPW and CLW a function of SST Determined to be maximize the correlation between TPW index and RAOB match-up TPW From Eq.(1.2) TPW can be derived by absorption coefficients of water vapor kv and cloud liquid water kl by using two different frequency. However, it is not able to calculate kv and kl because these depend on vertical profile of temperature, water vapor and liquid water. TPW CLW Theoretical calculation A function decreased with TPW A constant Theoretically estimated

35. Updated TPW algorithm for AMSR-E LUT in the algorithm was updated by using 3-yr RAOB and AMSR-E collocated dataset (2006-2008). Updated LUTs : T850, Transmittance and Mean atmospheric temperature table Wind speed correction table and extended to strong wind condition beyond 20m/s Conversion table PWI (Precipitable water index) to TPW Correction coefficients on SST, SSW dependency of emissivity No use of internal Tb conversion from ver.2 to ver.1 (JAXA L1B Tb version) ***NEW Num: 1349 Min: -18.836 Max: 19.008 Ave: -0.135 Std: 3.355 *** Current Num: 1344 Min: -18.532 Max: 15.366 Ave: 0.817 Std: 4.071 RAOB TPW AMSR-E TPW RAOB TPW AMSR-E TPW TPW Verification against RAOB (2009.1-5) Collocation criteria: Within 60min. 150km [mm]

36. V003 vs GPS_PWV （ 2009 年 6 月 20 日～ 8 月 20 日） (mm) Ver. 003

37. Optimized by 3years RAOB TPW data 2007 - 2009 (mm) Ver. 004

38. Optimized by 3months GPS TPW data Jun.20 – Aug. 20, 2009 (mm) Ver. 005 (preliminary)