© 2014 RAL Space

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. August2

1RAL Rutherford Appleton Laboratory, Harwell Oxford, UK2EUMETSAT, Darmstadt, Germany

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

MethodologyWP1000 Assess measurement errors of AMSU+MHS

(Input via consultancy from UK Met Office)WP2000 Set up EUMETSAT OEM in RAL code; test retrievals (IR and MWIR) over clear sea WP3000 OEM(MWIR/Metop-B) repeat over clear landWP4000 OEM(MWIR/Metop-B) repeat over land; emissivity added to stateWP5000 OEM(MWIR/Metop-B) repeat, but test also cloud scenes and add cloud to the state-vector WP6000 Test impact of missing AMSU channels Throughout, modifications to scheme are assessed via i. Statistical comparisons to ECMWF analyses for 3 days of global data ii. OE diagnostics (estimated errors, information content etc.)

© 2014 RAL Space

EUMETSAT ODV Scheme• EUMETSAT’s operational OE scheme for IASI (temperature, humidity, O3)• 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 O3. MHS adds 1.5 DOFS for T and 0.5 for humidity, but main benefit expected in cloud-affected scenes

Information Gain from Adding MW Channels

© 2014 RAL Space

IR on

ly

MWIR

Δ DOF

0

2

4

6

8

10

12

Ozone

Temperature

OzoneHumidityTemperature

O3 Δ DoF ≅ 0H2O Δ DoF ≅ ½Temp. Δ DoF ≅ 1½

Retrieval Degrees of Freedom (DOF)

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)

Other Improvements

① Fitting emissivity improves cost and O3 over desert surfaces in particular

② Fitting emissivity significantly improves lower tropospheric H2O③ 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

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

Improved O3 over Desert• Fitted emissivity lowers cost and improves O3 over desert

© 2014 RAL Space

FixedEmissivity:

FittedEmissivity:

Improved Lower Trop. Humidity

© 2014 RAL Space Fixed emissivity Fitted emissivity

• Fitted emissivity significantly improved lower tropospheric H2O (more improvements to be expected for cloudy scenes)

Improved Cold Scenes• Fitted bias correction reduces cost over cold scenes

(land and sea ice)

Original scheme Fitted emissivity Fitted emissivityFitted Bias Correction

© 2014 RAL Space

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

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

SPARE SLIDES

© 2010 RAL Space

Sensors

© 2014 RAL Space

AMSU/MHS ChannelsAMSU-A# Freq.GHz1 23.802 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

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

IASI Retrievals

No emissivity fitted

With emissivity fitted

First guessRetrieved

Results shown don’t include MW channels yet, but retrieving surface emissivity already benefits IR window channels

ImprovementsTemperature

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

ImprovementsHumidity

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

Mid-Lat Land Retrieval (PWLR)

Mid-Lat Land Retrieval (Clim.)

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”

Stats: Lower tropospheric humidity

Stats: Lower tropospheric temperature