Developing Spectral Corrections / SRF Retrievals Tim Hewison Special Issue of the IEEE TGRS on “Inter-Calibration of Satellite Instruments”: Special Issue of the IEEE TGRS on “Inter-Calibration of Satellite Instruments”: 12 January 2019 Developing Spectral Corrections / SRF Retrievals Tim Hewison

GSICS Corrections – Radiometric Recap So far, GSICS Corrections are radiometric corrections Essentially, empirical fixes to calibration Radiancecorrected =  f (Radianceuncorrected) based on series of comparisons Regardless of underlying cause of bias Good for linear biases introduced by calibration references Assume: small biases can be approximated by linear functions

GSICS Corrections – Spectral Corrections? But, biases can have other causes Spectral calibration errors – Changes to SRF e.g. Ice contamination, detector ageing, ... Maybe better handled using spectral correction? Implications for users? E.g. re-tuning RTMs

Ice Absorption: Model Transmittance spectra of ice layers of different thicknesses (black): 0.1 to 1.0 µm (thickest layers have lowest transmittance) and Spectral Response Functions (SRFs) of Meteosat-8 infrared channels (red).

Ice Absorption: Apparent SRF Shift Modifying SRF from assumed value introduces an apparent bias Depends on scene spectrum Usually worse in clear sky Stronger Spectral Contrasts Evaluate bias for range of modelled scene spectra, Lν, including US Standard Atmosphere (clear) Thick Cloud, tops at 194hPa By convolving assumed SRF with transmission spectra of ice layers 0.1 to 1.0μm thick SRF of Meteosat-9 13.4μm channel Black: Nominal SRF, from pre-launch tests Red: SRF convolved with transmission spectrum of ice layer 2μm thick Red dashed: Normalised SRF w/2μm ice layer => 2μm ice layer appears to shift SRF ~2cm-1

GSICS Corrections – SRF retrieval recap Manik Bali presentation 2016 GRWG meeting AX=B SRF = A-1 B Convolution SRF Hyperspectral Radiances Rep Radiances Action GIR.2016.3r.1: Manik to circulate draft manuscript on SRF retrieval method Recommendation: Manik to apply SRF retrieval to CO2 or water vapour channels Recommendation: Manik to check how many singular vectors are greater than 1 in SRF retrieval. Recommendation: Manik to evaluate uncertainties on the SRF retrieval.

GSICS Corrections – FIDUCEO SRF retrieval Developed within FIDUCEO for Meteosat/SEVIRI HRV band & MVIRI VIS band uses comparison with spectral reflectance models Applies inverse problem theory Applies advanced gradient and uncertainty and covariance computation methods enabled by algorithmic differentiation Generalises existing broad-band and narrow-band vicarious calibration concepts Includes sensor degradation models motivated in terms of physics Takes into account and measures biases arising from small systematic errors in the spectral radiance simulation of different target types Takes prior information into account Yields a maximum posterior probability estimate (mean, uncertainty and spectral covariance matrix) of the absolute sensor spectral response Courtesy of Yves Govaerts Courtesy of Ralf Quast

Discussion on Spectral Corrections Comparisons with hyperspectral references instruments Allow retrieval of SRFs (for non-window channnels) Possible for IR channels – e.g. IASI comparisons Possible for VIS channels – e.g. GOME-2 comparisons? Could define Spectral Corrections To complement radiometric corrections? How to decide/partition radiometric/spectral? Implications for users? Implementing different bias correction schemes Re-tuning RTMs ...