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  1. Study for the joint use of IASI, AMSU and MHS for OEM retrievals of temperature, humidity and ozone D. Gerber1, R. Siddans1, T. Hultberg2, T. August21RAL Rutherford Appleton Laboratory, Harwell Oxford, UK2EUMETSAT, Darmstadt, Germany

  2. Aim of the Study • Evaluate the benefit of adding microwave (MW) channels to the measurement vector of EUMETSAT’s optimal estimation method (OEM) scheme for retrieving temperature, humidity and ozone from the infra-red (IR) sounder IASI • Extend EUMETSAT’s baseline (IR-only) OEM scheme to: • Fit surface spectral emissivity (IR and MW) • Work in the presence of (some) cloud (but not precipitation)

  3. Methodology WP1000Assess measurement errors of AMSU+MHS (Input via consultancy from UK Met Office) WP2000Set up EUMETSAT OEM in RAL code; test retrievals (IR and MWIR) over clear sea WP3000OEM(MWIR/Metop-B) repeat over clear land WP4000OEM(MWIR/Metop-B) repeat over land; emissivity added to state WP5000OEM(MWIR/Metop-B) repeat, but test also cloud scenes and add cloud to the state-vector WP6000Test impact of missing AMSU channels Throughout, modifications to scheme are assessed via • Statistical comparisons to ECMWF analyses for 3 days of global data • OE diagnostics (estimated errors, information content etc.)

  4. EUMETSAT ODV Scheme • EUMETSAT’s operational OE scheme for IASI (temperature, humidity, O3) • Apriori the result of a piece-wise linear regression scheme applied to IASI+AMSU/MHS radiances (trained using ECMWF analyses) • Prior errors based on comparison of results to analyses • Uses 139 (optimally selected) channels from principle component based (noise filtered) L1 data • Radiances bias corrected (using fixed residual spectra with x-track dependence) • Uses RTTOV as Forward Model (FM) • Obtains ~8 DOFS for temperature, 4 for humidity and 2 for O3. MHS adds 1.5 DOFS for T and 0.5 for humidity, but main benefit expected in cloud-affected scenes

  5. Information Gain from Adding MW Channels Retrieval Degrees of Freedom (DOF) O3 Δ DoF≅ 0 H2O Δ DoF≅ ½ Temp. Δ DoF≅ 1½

  6. Benefit of MW in Practice • Comparatively weak improvement in Std. Dev. of Retrieval - Analysis • Bigger benefit expected for cloudy scenes (yet to be processed)

  7. Other Improvements • Fitting emissivity improves cost and O3over desert surfaces in particular • Fitting emissivity significantly improves lower tropospheric H2O • Fitting scale factor for bias correction spectrum leads to reduced cost in cold scenes (but does not affect retrieval performance otherwise) • Fitting emissivity and cloud improves lower tropospheric temperature

  8. Additions to ODV Scheme • Emissivity: • RTTOV atlas used in standard scheme (based on first 6 singular vectors of Borbas/Wisconsin set, mapped using MODIS to give global distributions) • Now extend retrieval to fit singular vectors of the emissivity spectra, with RTTOV model as prior • Also added pattern related to spectral shift of mean emissivity to the Wisconsin patterns (seems to be needed) • Cloud: • Cloud fraction and height added to scheme (as in RAL CH4 scheme) • Cloud retrievals only tested (so far) when MW radiances also used • Benefit of modifications tested via statistical comparisons of 3 days of global data to ECMWF analyses

  9. Improved O3 over Desert • Fitted emissivity lowers cost and improvesO3 over desert Fixed Emissivity: Fitted Emissivity:

  10. Improved Lower Trop. Humidity • Fitted emissivity significantly improved lower tropospheric H2O (more improvements to be expected for cloudy scenes) Fixed emissivity Fitted emissivity

  11. Improved Cold Scenes • Fitted bias correction reduces cost over cold scenes (land and sea ice) Original schemeFitted emissivityFitted emissivity Fitted Bias Correction

  12. Improved Lower Trop. Temperature • Cloud retrieval shows realistic lower tropospheric temperatures (Scheme is working in principle, but explicitly cloudy scenes not processed yet)

  13. Intermediate Conclusions • Fitting bias correction and surface emissivity improves IASI retrievals in specific situations (desert, ice) with no penalty • Fitting emissivity significantly improves LT humidity • Including cloud in nominally cloud-free scenes (marginally) improves lower tropospheric temperature • So far, MW channels have neutral impact on OEM results.More impact expected for cloudy scenes. Initial indications positive.


  15. Sensors

  16. AMSU/MHS Channels AMSU-A # Freq.GHz 1 23.80 2 31.40 3 50.30 4 52.80 5 53.60 6 54.40 7 54.94 8 55.50 9 57.29 10 57.29 11 57.29 12 57.29 13 57.29 14 57.29 15 89.00 MHS # Freq/ GHz 1 89.0 2 150.0 3 183.3 4 183.3 5 190.3

  17. AMSU Measurement Errors • Comparison of AMSU/MHS measurement error from UK Met Office (W. Bell), ECMWF (N. Bormann) and our own analysis ----- Desroziers ----- Hollingsworth/Lönneberg

  18. IASI Retrievals Results shown don’t include MW channels yet, but retrieving surface emissivity already benefits IR window channels With emissivity fitted First guessRetrieved No emissivity fitted

  19. Improvements Temperature (MWIR with fitted emissivity and cloud vs. standard OEM)

  20. Improvements Humidity (MWIR with fitted emissivity and cloud vs. standard OEM)

  21. Mid-Lat Land Retrieval (PWLR)

  22. Mid-LatLand Retrieval (Clim.)

  23. Task 4: Retrieval Simulations • Large set of retrievals conducted to asses benefit MW/IR and performance of emissivity retrieval: • standard: IR only, RAL retrieval ~ EUMETSAT OEM. • IR+MW: IR+MW retrieval (no emissivity, no cloud retrieval). • MW: MW only retrieval (no emissivity, no cloud retrieval). • IR+MW; Cloud: IR+MW retrieval with cloud fraction and height also retrieved. • Emis:[10/20/30]n: IR only retrieval, with 10/20/30 spectral emissivity patterns retrieved (no emissivity correlations between IR and MW). • IR+MW; Emis:20: IR+MW retrieval, with 20 spectral emissivity patterns retrieved. Spectral correlations assumed between IR and MW. • IR+MW; Emis:20n: As above, no spectral correlations IR/MW • MW; Emis:20: MW only retrieval, with 20 spectral emissivity patterns • IR+MW; Emis:20; Cloud: As above, also with cloud retrieved • Two versions of each; with PWLR as a priori and a new “climatological constraint”

  24. Stats: Lower tropospheric humidity

  25. Stats: Lower tropospheric temperature