1. 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

2. 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

3. 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

4. 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).

5. 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 μm 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

6. 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 r.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.

7. 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

8. 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
...