1 Impact study of AMSR-E radiances in NCEP Global Data Assimilation System Masahiro Kazumori (1) Q. Liu (2), R. Treadon (1), J. C. Derber (1), F. Weng (2), S. J. Lord (1) (1) NOAA/NCEP/EMC (2) NOAA/NESDIS

2 Contents Purpose of this study Development of Microwave Ocean Emissivity Model Data Assimilation Experiment Results Conclusions

3 Purpose of this study Image: JAXA/EORC Investigate the impact of AMSR-E radiance on NCEP global model (Advanced Microwave Scanning Radiometer for EOS) AMSR-E (Advanced Microwave Scanning Radiometer for EOS) observes the radiance from the Earth with 6 microwave dual-polarized channels. Frequency [GHz] PolarizationPhysical Observable 6.925V,HSST 10.65V,HSSW 18.7V,HWV 23.8V,HWV 36.5V,HSSW 89.0V,HRain These low frequency channels are sensitive to SST and SSW and less sensitive to hydrometeor in the atmosphere. They can be assimilated in the all weather condition. AMSR-E Sensor Unit Aqua

4 Development of Microwave Ocean Emissivity Model for AMSR-E Community Radiative transfer model (CRTM) has two options for Microwave Ocean Emissivity Model 1.FASTEM (Developed by UKMO) 2.NESDISEM (Developed by NESDIS) Necessary to develop a new microwave ocean emissivity model FASTEM NESDISEM 00z 16 August 2005 Both models have large bias(about 3K) in 10.65GHz (H). Comparison of TBcal - Tbobs operational use

5 Design of New Microwave Ocean emissivity model Wind speed dependent model: Fresnel Reflectivity in a calm sea Two-Scale Ocean roughness model Small Scale correction (Liu1998, Bjerkaas1979) Large Scale correction (Modified Storyn1972) Foam emissivity and foam fraction (Modified Storyn1972,Rose2004) Coefficients were derived from the fitting to satellite measurements (AMSR-E, SSMI and AMSU-A). TL and AD models with respect to SSW and SST

6 Comparison of (TBcal - Tbobs) [K] AMSR-E GHz (H) FASTEM(operational) NESDISEM New model 00z 16 August 2005 Biases are substantially reduced.

7 Comparison of (Tbcal-Tbobs) vs Wind Speed AMSR-E GHz (H) FASTEMNEWMDL Bias is depend on surface wind speed. New Model has smaller bias than operational (FASTEM).

8 Comparison of FASTEM & NEWMDL in AMSR-E channels Horizontal-polarization New model is better in the low frequency (< 20GHz). Statistic period:1-5 December 2005 Bar: BIAS Line:STD Vertical-polarization FASTEM New Model

9 Data Assimilation Experiment Configuration Analysis: NCEP GSI 3D-Var assimilation system Forecast: NCEP global model (as of May 2006) 00z Initial 180 hour forecast Resolution: T382L64 (same as operational, about 50km in horizontal) Cntl: Same as operational Test1: Cntl + AMSR-E with FASTEM ( all microwave frequency range) Test2: Cntl + AMSR-E with NEWMDL ( =20GHz) Period: 12 Aug.-11 Sep AMSR-E 6.925GHz channels(V,H) are not used because their FOV size are too large (43.2x75.4km)

10 Data Assimilation Experiment Quality Control of AMSR-E radiance data 1.Select ocean data and thin with 160km distance 2.Remove rain and cloud affected data( Criteria are based on CLW) 3.Remove land or ice contaminated data (FOV size is 29.4x51.4km at 10.65GHz) 4.Remove sun glint affected data in the ascending orbit 5.Gross error check (|Tbobs- Tbcal| < Threshold ) Tbcal-Tbobs [K] GHz (V) 00z 16 Aug TB bias correction term = FOV dependent + air-mass dependent 0.1% of all data are used for the assimilation. A few thousand / analysis

11 Results Impact on Analysis Test1 Mean difference Test-Cntl T & Q at 850hPa T [K] Q [g/kg] No systematic bias in temperature and moisture Period:Aug.12-Sep

12 Results Impact on Analysis Test2 Mean difference Test-Cntl T & Q at 850hPa T [K] Q [g/kg] Period:Aug.12-Sep Increase of Temperature (about 0.2K) in the high latitude. Decrease of moisture (about 0.1g/kg) over ocean.

13 Results Impact on Forecast (A.C. at 1000hPa Height) N.H. Almost Neutral S.H. Positive (Test1&Test2) Control Test1 Test2 AMSR-E radiance assimilation is positive for the S.H. Period:00z 12 Aug.-00z 11Sep. 2005

14 Results Impact on Forecast (A.C. at 500hPa Height) N.H. Almost Neutral S.H. Positive (Test1&Test2) Test2 is slightly better than Test1 Control Test1 Test2 Period:00z 12 Aug.-00z 11Sep. 2005

15 Results Impact on Forecast (Fits to RAOB wind) RMSE of 24 and 48 hour Vector Wind forecast are reduced in the S.H. Test1 Test2 dotted: Test solid : Cntl Black:24hr forecast Red :48hr forecast

16 Results Impact on Forecast RMSE Difference (Test – Cntl) Test1Test2 Blue color means improvements Zonal mean of 5-day Temperature Forecast RMSE against initial

17 Case study Hurricane Track Prediction (Katrina 2005) 5 samples in the experiment period (00z 25 August – 00z 29 August, 00Z initial forecast) Best Track(OBS) Control Test1 Test2 Test2 is better than Test1.

18 Conclusions(1/2) A MW Ocean emissivity model was developed for AMSR-E 1.The model is an empirical two scale roughness model, the coefficients were derived from the fitting to the satellite measurements. 2.The model has a better performance for low frequency channels than FASTEM. Impact study of AMSR-E radiances in NCEP global data assimilation system 1.The new MW ocean emissivity model was used in CRTM for the experiment. 2.Three cycle experiments were conducted. Cntl : same as operational Test1: Cntl + AMSR-E (with FASTEM) Test2: Cntl + AMSR-E (with New model =20GHz)

19 Conclusions(2/2) Impacts on analysis Increase of Temperature in high latitudes, decease of moisture over ocean at 850hPa. Impacts on forecast Positive for the S.H. (A.C., RMSE, Fits to RAOB) Neutral for the Tropic and the N.H. New emissivity model showed better results. The new emissivity model can extract the information on the ocean surface (SSW, SST) effectively from AMSR-E radiances in the data assimilation system.

20 Thank you

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22 Microwave Ocean emissivity In a calm sea, the ocean surface is specular. Reflectivity can be calculated by Fresnel law. ( p = h or v ) Total Reflectivity Frequency Zenith angle sea surface

23 Microwave Ocean emissivity When wind starts blowing, it makes small ripples on the ocean surface. The height variance is :Ocean roughness spectrum function (Bjerkaas1979) Small-scale height variance is :cutoff wave number Small Scale roughness correction ( p = h or v )

24 Microwave Ocean emissivity Large scale roughness correction A function of wind speed, incidence angle and frequency Large Scale roughness correction Coefficients were obtained from the fitting to the satellite measurements (AMSR-E,SSMI and AMSU-A)

25 Microwave Ocean emissivity Foam emissivity Foam fraction Total reflectivity Modified Stogryn[1972] function based on Rose[2004] FASTEM uses a constant (1.0) for both polarization. Stogryn[1972 ] FASTEM use Monahan(1986) 10m wind speed

26 Results Impact on Forecast (Fits to RAOB wind) For the N.H. and the Tropics, impacts are almost neutral for Test1 and Test2.

27 Zonal mean of RMSE of 500 hPa height forecast against initial. Difference ( Test – Cntl ) 5-day forecast 3-day forecast 1-day forecast Test1: (AMSRE with FASTEM) Cntl: (W/O AMSR-E) 1-day forecast 3-day forecast 5-day forecast Negative value indicate improvement

28 5-day forecast 3-day forecast 1-day forecast Zonal mean of RMSE of 500 hPa height forecast against initial. Difference ( Test – Cntl ) Test2: (AMSRE with NEWMDL) Cntl: (W/O AMSR-E) 1-day forecast 3-day forecast 5-day forecast Negative value indicate improvement

29 Conclusions Impact on analysis In Test1, no systematic bias in mean analysis field (850hPa temperature, humidity). In Test2, increase 850hPa temperature (0.2K) in the high latitude. decrease 850hPa humidity (0.1g/kg) over ocean. decrease guess TPW bias no significant difference mean 6-hour rain (not shown).

30 Conclusions Impact on forecast Positive A.C. of 500hPa for S.H., A.C. of 1000hPa N.H. and S.H. Fits to RAOB of 24, 48 hour vector wind forecast in the S.H. RMSE of 500hPa height for 3day and 5day forecast RMSE of temperature from 1000 to 100hPa for 3,5 day forecast (Test2 has larger improvement than Test1) RMSE of 200hPa vector wind (negative for FASTEM case) not shown Neutral A.C.500hPa of N.H. (Slightly positive for Test1 case) Fits to RAOB of 24 and 48 hour vector wind for the Tropics, N.H. Negative RMSE of 850hPa vector wind in the Tropics (not shown) A Case Study of Hurricane Track prediction (Katrina) Test1(FASTEM) degrade a hurricane track prediction. Test2(New model) keeps the accuracy

31 Results Impact on Analysis (Total Precipitable water [kg/m^2]) Test1Test2 Bias of total precipitable water in guess field are reduced slightly. Zonal mean Bias in guess

32 Results Impact on Forecast Zonal mean of 3-day Temperature Forecast RMSE against initial RMSE Difference (Test – Cntl) Test1Test2 Blue color means improvements