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Use of ATOVS at DAO

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amelia
26 diapositives · 432 Views · Aug 16, 2014

Use of ATOVS at DAO. Joanna Joiner, Donald Frank, Arlindo da Silva, Emily Liu, Clark Weaver Data Assimilation Office, NASA/GSFC. ITSC-12. Outline. Introduction: DAOTOVS 1DVAR assimilation Assessment of cloud- and land-affected data in DAS Use of OPTRAN OSSE simulations

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  1. Use of ATOVS at DAO Joanna Joiner, Donald Frank, Arlindo da Silva, Emily Liu, Clark Weaver Data Assimilation Office, NASA/GSFC ITSC-12

  2. Outline • Introduction: DAOTOVS 1DVAR assimilation • Assessment of cloud- and land-affected data in DAS • Use of OPTRAN • OSSE simulations • Use of TOVS for land-surface analysis/assimilation • Off-line skin temperature analysis (including bias correction) • Off-line skin temperature assimilation • AIRS dynamic channel selection based on cloud height • Incorporate effects of aerosol • Summary and Future Plans Joanna Joiner, DAO ITSC-12

  3. DAOTOVS attributes • DAOTOVS 1D-Var Assimilation of Radiances: • Uses Level 1b data • HIRS, MSU, SSU and AMSU-A radiances • Variational cloud-clearing (Joiner and Rokke, 2000) • Eigenvector FOV determination (AIRS ATBD) • Physically-based systematic error correction • GLATOVS, MIT -> OPTRAN (JCSDA) • Running in operational GEOS-DAS and next-generation Finite-volume DAS (fvDAS) currently running in parallel Joanna Joiner, DAO ITSC-12

  4. fvDAS Data Flow (PED coeff) Joanna Joiner, DAO ITSC-12

  5. DAOTOVS: What makes it different? • Uses cloud- and land-affected data (CERES land-emissivity data set based on satellite/laboratory measurements). • Uses all channels except HIRS 16, 17 AMSU 1,2 (IR bi-directional reflectance, mw emissivity in 1DVAR state vector) • Variational cloud-clearing (done simultaneously with retrieval); allows for internal quality control, consistency • Tuning using collocated radiosondes (not background). Updated daily via Kalman filter. • Errors in assimilation system include separate components with and without vertical/horizontal correlations Joanna Joiner, DAO ITSC-12

  6. How many cloud formations are seen in NOAA-K data? Answer: ~2 Look at eigenvectors of 3x3 array of HIRS pixels R1-Rn ~95% of cases explained by two modes (cloud-formations) Joanna Joiner, DAO ITSC-12

  7. O-F Statistics • Fit to Rawinsondes • Obs – 6h Forecast • Bias (spatial RMS, time mean) • Standard Deviations NW NE Tropics SW SE Joanna Joiner, DAO ITSC-12

  8. Cloud clearing has positive impact on 6 hour forecast, verified with radiosondes in finite-volume DAS green: NESDIS TOVS, red: DAOTOVS w/cloud-cleared, blue: DAOTOVS, no cloudy Joanna Joiner, DAO ITSC-12

  9. Forecast experiments, RMS error 500 hPa heightred: cloud-cleared,blue: no cloudy Joanna Joiner, DAO ITSC-12

  10. Impact of land-affected data (red-includes land,blue-no land) Joanna Joiner, DAO ITSC-12

  11. OPTRAN significantly reduces ATOVS radiance biases note: a) scale b) large reduction in channel 1 and 12 biases OPTRAN GLATOVS Joanna Joiner, DAO ITSC-12

  12. Observing System Simulation Experiments (OSSE) • Use fvCCM/Optran to simulate cloudy radiance • Use GEOSDAS/ GLATOVS for assimilation • Model has reasonable simulation of cloud/upper tropospheric humidity (use maximum overlap assumption) Joanna Joiner, DAO ITSC-12

  13. *The problem: Skin temperature biases over land (especially desert) causing clear-sky Outgoing Longwave Radiation (OLR) biases as compare with CERES; *Problem caused by emissivity used in land-surface model (LSM) and inconsistent definition of ground temperature

  14. Control fvDAS Ts Bias ECMWF Ts Bias |top|-|mid|

  15. Unbiased Analysis Equation Joanna Joiner, DAO ITSC-12

  16. Ts Bias and Anal. Increments Joanna Joiner, DAO ITSC-12

  17. Joanna Joiner, DAO ITSC-12

  18. New fvDAS Ts Bias Control fvDAS Ts Bias |top|-|mid|

  19. More TOVS marked “clear” by internal 1DVAR QC Red in bottom panel means more TOVS 1DVAR passes internal cloud checks And determined to be “clear” Joanna Joiner, DAO ITSC-12

  20. AIRS initial channel selection Joanna Joiner, DAO ITSC-12

  21. Channel selection based on retrieved cloud height Cloud: 50% at 200 hPa Yellow: Clear-Cloudy Green: Add noise, background errors 17 channels unaffected by cloud Joanna Joiner, DAO ITSC-12

  22. Channel selection based on retrieved cloud height Cloud: 10% at 700 hPa Yellow: Clear-Cloudy Green: Add noise, background errors 77 channels unaffected by cloud (If retrieve pressure of 525, get 58 channels) Joanna Joiner, DAO ITSC-12

  23. Using model-simulated aerosol in DAOTOVS (Weaver poster) Top: O-F HIRS 8 no dust in calculations Bottom: O-F HIRS 8 dust from transport model included in radiative transfer Joanna Joiner, DAO ITSC-12

  24. Summary and Future Work • Cloud- and land-affected data has positive impact on forecasts (6hrs-5 days) • OPTRAN reduces biases, but little overall impact due to tuning • OSSE simulations show reasonable model cloud • TOVS Ts analysis (including bias correction) improves OLR, clear-scene identification over land • AIRS channel selection good for cloudy situations (sharp weighting functions); Dynamic channel selection in cloudy scenes, cloud slicing-like approaches worthwhile • Aerosol effects are significant (see Weaver poster) Joanna Joiner, DAO ITSC-12

  25. In the future… • GOES sounder (JCSDA) • AMSU-B • Analyze pseudo-relative humidity instead of ln(q) in 1DVAR • Partial eigen-value decomposition/radiance assimilation • AIRS – more from Don Frank Joanna Joiner, DAO ITSC-12