1. A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division Japan Meteorological Agency Joint AMSR Science Team Meeting 14-16 August 2007 Missoula, MT U.S.A.

2. Background JMA utilizes AMSR-E vertical polarized radiances (19, 23, 37 and 89GHz) in the global 4D-Var data assimilation (DA) system. T emperature and moisture jacobian from radiative transfer model(RTTOV-7) are included in the analysis. Measurements of microwave window channels (dual-polarized 6.9,10,19 and 37 GHz) contain the information of surface wind speed. Purpose of this study is to investigate we can use the AMSR-E brightness temperature (both V-pol. and H-pol.) to obtain the wind speed information in the JMA global DA system. And also the performance of radiative transfer model (RTTOV-8) for the AMSR-E data assimilation is investigated.

3. Microwave Ocean Emissivity model The performance of microwave ocean emissivity model is a key in the AMSR-E brightness temperature assimilation. The emissivity model uses wind speed, sea surface temperature and frequency as input parameters. The calculated emissivity are used following atmospheric radiative transfer calculation. The ocean emissivity determines the surface radiation in the radiative transfer model. Several microwave ocean emissivity models for operational radiance assimilation are suggested. Several microwave ocean emissivity models for operational radiance assimilation are suggested. (included in RTTOV-8) FASTEM-3 (included in RTTOV-8) (developed by NESDIS and included in CRTM) NESDIS MW Ocean Emissivity model (developed by NESDIS and included in CRTM) (Kazumori et al. 2007, MWR in press) Low frequency microwave ocean emissivity model (Kazumori et al. 2007, MWR in press) Current JMA global DA system uses RTTOV-7 including FASTEM-2 as microwave ocean emissivity model. JMA plans to upgrade RTTOV from version 7 to version 8 in this year. The performance of FASTEM-3 in RTTOV-8 is important for AMSR-E radiance assimilation in JMA DA system. In this study, FASTEM-3 is used.

4. Description of MW ocean emissivity model (FASTEM-3) Two scale ocean roughness model is used in the MW ocean emissivity model. ( p = h or v ) Total Reflectivity Frequency Zenith angle In a calm sea, the ocean surface is specular. Reflectivity can be calculated by Fresnel law. Small Scale roughness correction ( p = h or v ) A function of wind speed, incidence angle and frequency Large Scale roughness correction MW Ocean Emissivity When wind starts blowing, it makes small ripples on the ocean surface. e:Permittivity T s :Surface temperature Non specular correction (using a function of wind speed, frequency, zenith angle and atmospheric transmittance) Foam correction (depending on wind speed) Azimuth angle correction (using a function of angle between wind direction and satellite azimuthal view angle) (foam fraction)

5. Sensitivity Analysis Sensitivity to sea surface temperature and wind speed using RTTOV-8 (FASTEM-3). Features 6.9 and 10GHz vertical polarization channels have large sensitivity to SST. H-pol. channel’s sensitivity to wind speed are larger than those of V-pol. Sensitivity to wind speed increases when wind speed becomes large. In high wind speed condition, sensitivity of 6.9 and 10GHz V-pol channels become large rapidly. H-polarization V-polarization AMSR-E BT Sensitivity : dTB/d(SST), dTB/d(WS) using FASTEM-3

6. Comparison of O-B between RTTOV-8 and RTTOV-7 RTTOV-8 and RTTOV-7 are identical except emissivity model in the MW radiance calculation. O-B means Observed minus calculated brightness temperature. The JMA 6-hour global forecasts are used as Background. FASTEM-3 shows better performance (standard deviation) in the calculation for AMSR-E. Red: RTTOV-8 (FASTEM-3) Green: RTTOV-7 (FASTEM-2, JMA operational) Comparison of O-B between RTTOV-8 and RTTOV-7 for QC passed without bias correction AMSR-E data ( Statistics: 01 August, 2007 1day data set)

7. Data Quality Control Current radiative transfer model (RTTOV-7) in JMA does not include cloud/rain water effect in the microwave radiance calculation. Cloud/rain affected data should be excluded before the assimilation. Sun glint affected data in 6.9 and 10GHz are also needed to be removed. AMSR-E 10GHz H-Pol. channels O-B with BC AMSR-E Cloud liquid water retrievals by MSC algorithm All Ocean data set in 00UTC 01 August 2007.

8. Cloud liquid water dependence of O-B O-B statistics show biases depending on the amount of cloud liquid water (clw). Low frequency channels (6.9 and 10GHz) shows small clw dependency. Horizontal channel’s O-B (19,23 and 37GHz) show high correlation with clw. Cloud affected data need to be removed and/or bias correction using cloud liquid water as a predictor is necessary. O-B vs. CLW H-Pol.V-Pol. 6.9GHz 10GHz 19GHz 23GHz 37GHz [mm] All Ocean data set in 01 August 2007.

9. Current JMA operational cloud detection 1. Cloud detection based on comparisons of AMSR-E brightness temperature differences 2. |Retrieved TPW – guess TPW| < 10mm New method is based on the amount of cloud liquid water 1. Use cloud liquid water retrievals to exclude cloud/rain affected data. Set a threshold for each channels based on the cloud liquid water amount. (clw < 100g/m 2 ) 2. And use the cloud liquid water as a predictor in bias correction (VarBC) scheme to correct remaining biases. Improvement of cloud detection in QC Further cloud detection is required for the use of H-pol. channels. TB: Brightness temperature ES: Ocean Emissivity

10. Bias correction In the JMA global DA system, the biases in O-B are corrected by variational bias correction scheme (VarBC). The biases are estimated using linear function with some predictor and those coefficients are optimized in the analysis and updated every analysis cycle. Bias correction term is in the observation operator Coefficients:  Calculated in the system automatically  Time developing variables They show the relation between observations and model fields Predictors: p TCPW, T SRF, T SRF 2, WS SRF, 1/cos(Z ANG ), 1(Const) Current JMA VarBC scheme does not include cloud liquid water as predictor for AMSR-E. Therefore, the cloud affected data might be assimilated in the DA system. H-pol. channels have much affected by cloud liquid water than V-pol. channels. It is necessary to include cloud liquid water in the VarBC predictors. The biases depending on cloud liquid water are expected to be corrected properly.

11. AMSR-E O-B histogram With and Without BC Red: W BC, Blue: W/O BC Statistics: 01 August, 2007 1day AMSR-E ocean data VarBC predictors: Total precipitable water, SST, SST square, wind speed, and cloud liquid water FASTEM-3

12. Preliminary results Single analysis study in JMA global 4D-Var system (AMSR-E radiance data + conventional data (including SeaWinds data)) Used AMSR-E channels: 6.9GHz (V,H),10GHz(V,H),19GHz(V,H),23GHz(V),37GHZ(V,H),89GHz(V) Assimilation of AMSR-E radiance data adds some wind speed increments in data sparse area over the ocean. Data coverage of AMSR-E/Aqua (green) and SeaWinds/QuikSCAT (blue) in 6-hour assimilation time window in 00UTC 1 August, 2007. Surface wind speed (model 1 st layer) analysis increment for Control (Without AMSR-E) Surface wind speed analysis difference between Test and Control Surface wind speed (model 1 st layer) analysis increment for Test (With AMSR-E)

13. Summary and plan Summary The performance of microwave ocean emissivity model (FASTEM-3) in RTTOV-8 was investigated. Sensitivity to sea surface wind speed and surface temperature in MW ocean emissivity was reasonable. H-pol. channels have larger sensitivity to sea surface wind than V-pol. channels. FASTEM-3 shows better performance than FASTEM-2. Use of FASTEM-3 is promising. Use of cloud liquid water retrievals in the VarBC scheme improve the data quality control. A preliminary assimilation experiment showed AMSR-E added some impacts on surface wind speed analysis over the ocean. Need more experiments to confirm the impacts on analysis and forecasts statistically. Plan Assimilation experiments using AMSR-E horizontal polarized channels. Hope to make good impacts on surface wind speed analysis and forecasts.