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Recent activities on AMSR-E data utilization in NWP at JMA

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. Outline.

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Recent activities on AMSR-E data utilization in NWP at JMA

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  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

  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 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. Analyzed precipitation With GPS Without GPS 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) . GPS data are delivered from Geospatial Information Authority of Japan (GSI) and converted to TPW products in JMA. Ground based GPS TPW data in Japan

  6. Verification of AMSR-E TPW products with global GPS TPW data Locations of collocated GPS Data (35 sites) ZTD ：Zenith Tropospheric Delay ZHD : Zenith Hydrostatic Delay ZWD : Zenith Wet Delay AMSR-E and GPS collocation criteria: GPS altitude <= 200m, Spatial diff. <= 20km, Time diff. <= 10 min. Period: 20 Jun. – 20 Aug. 2009 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. Verification of AMSR-E TPW productswith global GPS TPW data The National Snow and Ice Data Center(NSIDC) NEW JAXA-L2 Scatter diagram of TPW GPS vs. AMSR-E

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

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

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

  11. Impact on moisture analysis in July 20 Analyzed TPW field in Test (with SSMIS) 03UTC 09UTC 15UTC 21UTC TPW Analysis difference (Test-Cntl) Generally, assimilation of SSMIS intensify moisture flow in the analysis.

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

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

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

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

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

  17. FT=5 TEST TPW TPW DIFF (TEST-CNTL)

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

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

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

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

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

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

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

  25. 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 Impact on Rain Forecast Valid Time: Jul. 21 12JST TEST (with SSMIS) CNTL(w/o SSMIS) Radar observation 3hr rain FT=12 FT=12 TEST-CNTL TPW DIFF TEST TPW FT=12 FT=12 [mm] [mm]

  26. Observational Local Time Light Blue : Aqua/AMSR-E Purple : DMSP F-16/SSMIS Green : DMSP F-17/SSMIS Orange : Coriolis/WindSat 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. 00 06 18 13:30 12 Dark black points indicate WindSat data in 6-hrs time window

  27. Data Latency • 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. Data latency for ATOVS (MSC) Data latency for MTSAT Data latency for AMSR-E (JAXA) Data latency for AMSR-E (Global)

  28. Summary TPWdata 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 GPS satellite GPS satellite Procedure Pseudo Range GPS observationdata (RINEX) Pseudo Range ZTD GPS software（GIPSY） Wet Delay Conversion GPS ephemeris Receiver TPW Surface Pressure, Temperature Vapor Zenith Tropospheric Delay = Zenith Hydrostatic Delay ＋ Zenith Wet Delay

  32. Other data’s coverage in MSM

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

  34. Retrieval of TPW and CLW Theoretical calculation 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. a function of SST Determined to be maximize the correlation between TPW index and RAOB match-up TPW CLW 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) TPW Verification against RAOB (2009.1-5) Collocation criteria: Within 60min. 150km [mm] [mm] ***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 AMSR-E TPW AMSR-E TPW RAOB TPW RAOB TPW [mm] [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 data2007 - 2009 (mm) Ver. 004

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

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