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Infrared Satellite Data Assimilation at NCAR

Infrared Satellite Data Assimilation at NCAR. Tom Auligné , Hui-Chuan Lin, Zhiquan Liu, Hans Huang, Syed Rizvi, Hui Shao, Meral Demirtas, Xin Zhang National Center for Atmospheric Research. Work supported by AFWA, NASA, NSF, KMA. Outline. Introduction to satellite data assimilation at NCAR

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Infrared Satellite Data Assimilation at NCAR

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  1. Infrared Satellite Data Assimilation at NCAR Tom Auligné,Hui-Chuan Lin,Zhiquan Liu, Hans Huang, Syed Rizvi, Hui Shao, Meral Demirtas, Xin Zhang National Center for Atmospheric Research Work supported by AFWA, NASA, NSF, KMA

  2. Outline Introduction to satellite data assimilation at NCAR Practical issues with AIRS data assimilation Current developments on infrared radiances

  3. Introduction:Data assimilation at NCAR • WRF-ARW: Local Area Model (with global version) + TL/ADJ version • DART: Ensemble Data Assimilation (EnKF, ETKF, …) (no radiance yet) • WRF-Var: Variational Data Assimilation (3DVar, FGAT, 4DVar) + Hybrid system • Community support

  4. Satellite DA:WRF-Var capabilities • Retrievals (T / Q profiles) • SATEM (from AMSU) • AIRS retrievals (NASA version 5) • GPS Radio Occultation • Retrieved refractivity from COSMIC • Winds • Retrieved winds: polar MODIS, SATOB • Active sensors: Quikscat • Radiances(BUFR format from NCEP/NRL/AFWA/NESDIS) • HIRS from NOAA16, 17, 18 • AMSU-A from NOAA15, 16, 18, EOS-Aqua, METOP-2 • AMSU-B from NOAA15, 16, 17 • MHS from NOAA18, METOP-2 • AIRS from EOS-Aqua • SSMIS from DMSP16

  5. Satellite DA:WRF-Var capabilities • Retrievals (T / Q profiles) • SATEM (from AMSU) • AIRS retrievals (NASA version 5): NASA-EOS Project to assess impact over Antarctica • GPS Radio Occultation • Retrieved refractivity from COSMIC • Winds • Retrieved winds: polar MODIS, SATOB • Active sensors: Quikscat • Radiances(BUFR format from NCEP/NRL/AFWA/NESDIS) • HIRS from NOAA16, 17, 18 • AMSU-A from NOAA15, 16, 18, EOS-Aqua, METOP-2 • AMSU-B from NOAA15, 16, 17 • MHS from NOAA18, METOP-2 • AIRS from EOS-Aqua: AFWA Project to incorporate in operational 3DVar • SSMIS from DMSP16

  6. ModelTop ModelTop Solarcontamination Ozone AIRS T Jacobians AIRS Channel Selection:10hPa model top RTTOV (v 8.7) CRTM (REL-1.1) T Surface O3 Q T

  7. Observation Error:Tuning of statistics NCEP ECMWF NCEP (and most ECMWF) observation errors statistics consistent with innovations Error factor tuning from objective method (Desrozier and Ivanov, 2001) Channel number`

  8. Quality Control & Thinning • Pixel-level QC • Reject limb observations • Reject pixels over land and sea-ice • Cloud/Precipitation detection (NESDIS) • Synergy with imager (AIRS/VIS-NIR) • Channel-level QC • Grosscheck(innovations <15 K) • First-guess check(innovations < 3o). • ThinningWarmest Field of View Thinning (120km) 345 active data Warmest FoV 696 active data

  9. Parameters Predictors: • Offset • 1000-300mb thickness • 200-50mb thickness • Surface skin temperature • Total column water vapor • Scan, scan2, scan3 Cost Function Bias Correction:Static and Variational Modeling of errors in satellite radiances: “Offline” bias correction “Variational” bias correction

  10. VarBC:Issues with regional models No Inertia Constraint Inertia Constraint VarBC Timeseries Innovations for AIRS window channel #787 After BC Before BC

  11. Parameter estimation:in CRTM & RTTOV gmodulates atmospheric absorption to compensate for: • poor knowledge of gas concentrations (CO2, …) • errors in definition of ISRF • errors in mean absorption coefficient Gamma sensitivity Timeseries of  estimations -1(%) Analysis cycle

  12. Cloud Detection:MMR scheme AIRS 2378 channels … to identifying clear channels(insensitive to the cloud). From « hole hunting » (identifying clear pixels)… = Radiance calculated in clear sky RTM = Radiance calculated for overcast cloud at level k / Nk3 Nk2 Cloud fractionsNk are ajusted variationally to fit observations. Nk1 Vertical Level No Pixel Channel Number (LW band)

  13. Cloud Detection:Initial validation for AIRS MODIS NASA Level 2 Product AIRS Cloud Detection Cloud Top Pressure (hPa)

  14. Current Developments:Cloudy Radiances Cloud Top Pressure

  15. Current Developments:Observation Impact Analysis (xa) Observation (y) Forecast (xf) WRF-VAR Data Assimilation WRF-ARW Forecast Model Define Forecast Accuracy Background (xb) Forecast Accuracy (F) Observation Impact <y-H(xb)> (F/ y) Gradient of F (F/ xf) Observation Sensitivity (F/ y) Analysis Sensitivity (F/ xa) Adjoint of WRF-ARW Forecast TL Model (WRF+) Adjoint of WRF-VAR Data Assimilation Derive Forecast Accuracy Background Sensitivity (F/ xb) STATUS: DONE ONGOING Obs Error Sensitivity (F/ eob) Bias Correction Sensitivity (F/ k) Figure adapted from Liang Xu

  16. More plans… • AIRS/AMSU (v.5) Retrievals over Antarctica • Collocate with COSMIC retrievals • Assess impact in AMPS system • AFWA Cloud Analysis • Introduce cloud hydrometeors in control variable • Study background error covariances for clouds • Include cloud microphysics into WRF-ARW TL/ADJ • Assess the accuracy/linearity of radiative transfer in cloudy conditions • IASI, CrIS

  17. Thanks for your attention… auligne@ucar.edu

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