1. Current status of AMSR-E data utilization in JMA/NWP Masahiro KAZUMORI Numerical Prediction Division Japan Meteorological Agency 14-16 July 2008 Joint AMSR Science Team Meeting, Telluride, CO

2. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 1 Contents Utilizations of AMSR-E data in JMA/NWP –Assimilation Radiance assimilation for Global model (GSM) Retrieval (TPW, Rain rate) assimilations for Mesoscale Model (MSM) –Verification of the forecast models Total Precipitable Water Monthly Rainfall Heavy rain and typhoon case study Case study : Cyclone Nargis in Myanmar 2008 –Impact of MW-Imager data in JMA operational system –Use of all weather wind speed from AMSR-E (on going development)

3. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 2 JMA/NWP models Model Global Spectral Model (GSM) Meso Scale Model (MSM) Horizontal res.20km5km Vertical res. (model top) 60 (0.1hPa)50 (21.8km) Forecast range (Initial time) 84h (00,06,18UTC) 216h (12UTC) 15h (00,06,12,18UTC) 33h (03,09,15,21UTC) frequency4/day8/day Target One-week forecast Short-range forecast Aeronautical forecast Disaster prevention information Data Assimilation4D-Var 20kmGSM Observation

4. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 3 Utilization of MW Imager data in GSM Assimilation of Microwave Radiometer (MWR) radiances from DMSP/SSMI, TRMM/TMI and Aqua/AMSR-E Less cloud-affected radiances over the ocean with SST > 5 deg.C Only vertical polarized channels at 19 – 89 GHz( to obtain moisture information) VarBC corrects biases against analysis Impacts on analyses/forecasts Better TPW (Total Precipitable Water) analysis verified against TRMM retrieved TPW Better precipitation forecasts: larger correlation between 1-day-forecast and GPCP (0.881=>0.891 for Aug2004) Better typhoon track forecasts 5 MayStart MWR radiance assimilation Global Analysis TRMM difference no MWR radiance Global Analysis TRMM difference use MWR radiance 25May Time sequence of TPW RMSE and bias between analysis and TRMM retrieval

5. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 4 Utilization of MW Imager data in MSM Retrieval assimilation (Total Precipitable Water and Rain Rate from AMSR-E, TMI and SSMI) Impacts : Better rainfall forecasts Conventional Data MWRs RR RA Obs. MWRs TCPW MWRs RR RA Obs. RA + MW TCPW & RR A case study : Fukui Heavy Rain in 2004 “Assimilation of the Aqua/AMSR-E data to Numerical Weather Predictions”, Tauchi et al., IGARSS04 Poster

6. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 5 Verification : Total Precipitable Water Initial TPW 3-day ForecastObservation (AMSR-E) Initial -Obs Forecast - Obs Monthly average for Aug. 2007 GSM : Dry bias in deep convective area and wet bias along the ITCZ. The biases increase with forecast time. Satellite measurements reveal the numerical model biases.

7. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 6 Verification : Rainfall forecast Rainfall forecast in JMA Global Model is excessive, especially for deep convective area in the early stage of forecast. Satellite measurements are essential to evaluate the performance of JMA Global model and provide important information for further forecast model improvement. AMSR-E RAIN GSM FT=24 GSM FT=72 Monthly Averaged R24 for Aug. 2007

8. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 7 Verification : Heavy rain and typhoon FT=14 Jul. 12 03UTC init. MSM Rain forecast Valid time : 17 UTC July 12, 2007 FT=08 Jul. 12 09UTC init. FT=02 Jul. 12 15UTC init. Rain from AMSR-E Measurement Fake rains predicted in subsidence area of subtropics high were identified by comparison with AMSR-E measurement and indicate issues on current cumulus parameterization scheme in MSM. Fake rain? MTSAT IR Image

9. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 8 Case study: Cyclone Nargis in May 2008 Forecast comparisons for several NWP centers (12UTC April 30, 2008 Initial forecast, Psea [hPa]) Observed cyclone track Cyclone Nargis : Strong tropical cyclone made landfall in Myanmar on May 2, 2008 JMAECMWFNCEP Day-2 Day-1 Most of NWP centers predicted the cyclone landfall in Myanmar. But, the intensity of JMA forecast is weak.

10. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 9 JMA GSM Forecast (Rain and TPW) Psea and Rain24TPW In the Bengal bay, foregoing moisture flow from south west was well analyzed. The direction forecast of the cyclone was “eastward”. The JMA global forecast predicted the May 2 landfall. JMA GSM 12UTC April 30 Init. Day-1 Forecast Day-2 Forecast JMA/GSM Analysis Psea [hPa] and Rain [mm/day]

11. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 10 Impact of MW-Imager data for TPW Initial Difference (W/ – W/O)Day-1 forecast diff (W/ – W/O) Data Coverage (MW-Imager) O-B [K] Case Study: W/: Same data usage as operational W/O : Without all MW Imager data in the analysis [mm] MW-Imager data (SSMI radiance) enhanced the moisture flow from south west and the impacts was retained for 24-hour TPW forecast. However, rain affected data were not used. Rain affected data

12. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 11 Use of all weather wind speed from AMSR-E JAXA’s research products developed by Dr. Shibata and Mr. Saitoh. Ocean surface wind speed retrievals under all weather condition using AMSR-E low frequency channels. JMA developed a QC scheme for the data assimilation in JMA global 4D-Var system and started to study the impact on analysis and forecast. Developed QC in JMA –Data selection (Above 7m/s) –Removal of land (and/or island) contaminated data. –Removal of sea ice contaminated data. –Thinning and averaging with 100km grid boxes. –Gross error check based on wind speed O-B.

13. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 12 AMSR-E all weather wind speed data JAXA L2 wind speedAll weather wind data Averaging with 100km grid boxes Strong winds in the cyclone core are available. Need to make average with grid boxes to reduce noise and fit to model resolution (4D-Var :Inner model res. 80km). 06UTC 29 April, 2008

14. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 13 The first analysis increment AMSR-E all weather wind speed data strengthen the intensity and the surface wind speed in the analysis. Used dataPsea (W AMSR-E)Psea (W/O AMSR-E) Increment of wind

15. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 14 Impacts on forecasts Forecast comparisons for several NWP centers (12UTC April 30, 2008 Initial forecast, Psea [hPa]) JMAECMWFNCEP Day-2 Day-1 JMA + AMSR-E The intensity in forecast was also strengthened.

16. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 15 Impacts on forecasts Cyclone Nargis Forecast and Analysis No significant improvement in the track forecast Central Pressure Max wind AMSR-E all weather wind speed data strengthen the intensity and wind. Red: W AMSR-E, Green: W/O AMSR-E 16 cases for each

17. 14-16 July 2008Joint AMSR Science Team Meeting, Telluride, CO 16 Conclusions AMSR-E data has been utilizing in JMA operational NWP Assimilation –Clear radiance assimilation for GSM –Retrieval (TPW and Rain rate) assimilations for MSM AMSR-E provide much information on rain and moisture for NWP Model Verifications –Total Precipitable Water –Rain distribution –Heavy rain and typhoon study MW imager measurements (Rain and TPW) reveal the forecast model errors (bias and accuracy) and the results lead to further forecast model improvement (Cumulus parameterization scheme) Case study : Cyclone Nargis in Myanmar 2008 MW-Imager data play important roles to provide moisture information in GSM Difficulty of cloud and rain affected radiance assimilation All weather wind speed data were investigated in JMA/NWP –Available under severe weather condition (e.g. Tropical cyclone) –Assimilation experiments Strengthen the intensity and the max wind speed No significant improvement in the track forecast –Valuable data to provide realistic observational information under severe weather condition