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Impact study of AMSR-E radiances in NCEP Global Data Assimilation System

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. Contents. Purpose of this study Development of Microwave Ocean Emissivity Model

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Impact study of AMSR-E radiances in NCEP Global Data Assimilation System

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  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. Image: JAXA/EORC Purpose of this study Investigate the impact of AMSR-E radiance on NCEP global model AMSR-E (Advanced Microwave Scanning Radiometer for EOS) observes the radiance from the Earth with 6 microwave dual-polarized channels. AMSR-E Sensor Unit Aqua 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.

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

  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 10.65 GHz (H) 00z 16 August 2005 NESDISEM FASTEM(operational) New model Biases are substantially reduced.

  7. Comparison of (Tbcal-Tbobs) vs Wind Speed AMSR-E 10.65 GHz (H) FASTEM NEWMDL Bias is depend on surface wind speed. New Model has smaller bias than operational (FASTEM).

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

  9. Data Assimilation ExperimentConfiguration • 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 only) and FASTEM (>=20GHz) • Period: 12 Aug.-11 Sep. 2005 AMSR-E 6.925GHz channels(V,H) are not used because their FOV size are too large (43.2x75.4km)

  10. Data Assimilation ExperimentQuality Control of AMSR-E radiance data • Select ocean data and thin with 160km distance • Remove rain and cloud affected data(Criteria are based on CLW) • Remove land or ice contaminated data (FOV size is 29.4x51.4km at 10.65GHz) • Remove sun glint affected data in the ascending orbit • Gross error check • (|Tbobs- Tbcal| < Threshold ) Tbcal-Tbobs [K] 10.65 GHz (V) 00z 16 Aug. 2005 0.1% of all data are used for the assimilation. TB bias correction term = FOV dependent + air-mass dependent A few thousand / analysis

  11. ResultsImpact on Analysis T[K] Test1 Mean difference Test-Cntl T & Q at 850hPa Period:Aug.12-Sep.11 2005 No systematic bias in temperature and moisture Q[g/kg]

  12. ResultsImpact on Analysis T[K] Test2 Mean difference Test-Cntl T & Q at 850hPa Period:Aug.12-Sep.11 2005 Increase of Temperature (about 0.2K) in the high latitude. Decrease of moisture (about 0.1g/kg) over ocean. Q[g/kg]

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

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

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

  16. ResultsImpact on Forecast Zonal mean of 5-day Temperature Forecast RMSE against initial RMSE Difference (Test – Cntl) Blue color means improvements Test1 Test2

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

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

  21. backup

  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 ( p = h or v ) Small Scale roughness correction

  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] 10m wind speed FASTEM use Monahan(1986)

  26. ResultsImpact 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 ) 1-day forecast 1-day forecast Test1: (AMSRE with FASTEM) Cntl: (W/O AMSR-E) 5-day forecast 3-day forecast 5-day forecast 3-day forecast Negative value indicate improvement

  28. Zonal mean of RMSE of 500 hPa height forecast against initial. Difference ( Test – Cntl ) 1-day forecast 1-day forecast Test2: (AMSRE with NEWMDL) Cntl: (W/O AMSR-E) 5-day forecast 3-day forecast 5-day forecast 3-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. ResultsImpact on Analysis (Total Precipitable water [kg/m^2]) Test1 Test2 Zonal mean Bias in guess Bias of total precipitable water in guess field are reduced slightly.

  32. ResultsImpact on Forecast Zonal mean of 3-day Temperature Forecast RMSE against initial RMSE Difference (Test – Cntl) Blue color means improvements Test1 Test2

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