1. © 2014 RAL Space Study for the joint use of IASI, AMSU and MHS for OEM retrievals of temperature, humidity and ozone D. Gerber 1, R. Siddans 1, T. Hultberg 2, T. August 2 1 RAL Rutherford Appleton Laboratory, Harwell Oxford, UK 2 EUMETSAT, 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) © 2014 RAL Space

3. Methodology © 2014 RAL Space

4. EUMETSAT ODV Scheme EUMETSAT’s operational OE scheme for IASI (temperature, humidity, O 3 ) A priori 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 © 2014 RAL Space 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 O 3. 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 © 2014 RAL Space O 3 Δ DoF ≅ 0 H 2 O Δ DoF ≅ ½ Temp. Δ DoF ≅ 1½ Retrieval Degrees of Freedom (DOF)

6. Benefit of MW in Practice © 2014 RAL Space Comparatively weak improvement in Std. Dev. of Retrieval - Analysis Bigger benefit expected for cloudy scenes (yet to be processed)

7. Other Improvements ① Fitting emissivity improves cost and O 3 over desert surfaces in particular ② Fitting emissivity significantly improves lower tropospheric H 2 O ③ Fitting scale factor for bias correction spectrum leads to reduced cost in cold scenes (but does not affect retrieval performance otherwise) ④ Fitting emissivity and cloud improves lower tropospheric temperature © 2014 RAL Space

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 CH 4 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 O 3 over Desert Fitted emissivity lowers cost and improves O 3 over desert © 2014 RAL Space Fixed Emissivity: Fitted Emissivity:

10. Improved Lower Trop. Humidity © 2014 RAL Space Fixed emissivityFitted emissivity Fitted emissivity significantly improved lower tropospheric H 2 O (more improvements to be expected for cloudy scenes)

11. Improved Cold Scenes Fitted bias correction reduces cost over cold scenes (land and sea ice) Original schemeFitted emissivityFitted emissivity Fitted Bias Correction © 2014 RAL Space

12. Improved Lower Trop. Temperature Cloud retrieval shows realistic lower tropospheric temperatures (Scheme is working in principle, but explicitly cloudy scenes not processed yet) © 2014 RAL Space

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. © 2014 RAL Space

14. SPARE SLIDES © 2010 RAL Space

15. Sensors © 2014 RAL Space

16. AMSU/MHS Channels AMSU-A #Freq.GHz 123.80 231.40 350.30 452.80 553.60 654.40 754.94 855.50 957.29 1057.29 1157.29 1257.29 1357.29 1457.29 1589.00 MHS #Freq/ GHz 1 89.0 2150.0 3183.3 4183.3 5190.3

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 © 2010 RAL Space

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

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-Lat Land 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