Infrared Satellite Data Assimilation at NCAR

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Presentation transcript:

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 Practical issues with AIRS data assimilation Current developments on infrared radiances

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

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

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

AIRS Channel Selection: 10hPa model top Ozone Solar contamination RTTOV (v 8.7) AIRS T Jacobians CRTM (REL-1.1) T Surface O3 Q T

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`

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 Gross check (innovations <15 K) First-guess check (innovations < 3o). Thinning Warmest Field of View Thinning (120km) 345 active data Warmest FoV 696 active data

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

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

Parameter estimation: in CRTM & RTTOV g modulates 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

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

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

Current Developments: Cloudy Radiances Cloud Top Pressure

<y-H(xb)> (F/ y) Adjoint of WRF-ARW Forecast Current Developments: Observation Impact Observation (y) Analysis (xa) 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) Observation Sensitivity (F/ y) Analysis Sensitivity (F/ xa) Gradient of F (F/ xf) 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

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

Thanks for your attention… auligne@ucar.edu