SEVIRI Solar Channel Calibration system Sébastien Wagner Tim Hewison, Marianne Koenig, Yves Govaerts

Objectives and methodology SSCC methodology and algorithm Outline Context Objectives and methodology SSCC methodology and algorithm SEVIRI calibration: current status Impact of the calibration uncertainties Future developments

Context: Meteosat observations... Archived data since 1982 Keeps growing... Meteosat imagers solar channel calibration device? No on-board calibration system (MVIRI / SEVIRI) On-board solar diffusers (FCI) Various system set-ups: MFG: 30 min repeat cycle / 6 bits (Met 1 to 3) or 8 bits (Met 4 to 7) coding / Sampling distance at SSP = 2.5 km / 1 solar channel MSG: 15 min repeat cycle / 10 bits coding / Sampling distance at SSP = 3 km for VIS06 / VIS08 / NIR16 and 1km for HRVIS / 4 solar channels MTG: 10 min repeat cycle / Sampling distance at SSP = 0.5 and 1.0 km/ 8 channels (2 non-window channels) Various specifications for the solar channel calibration: MVIRI = no spec SEVIRI = 10% + 5% long-term stability FCI = 5% + 2% long-term stability

Context... The particular case of SEVIRI HRVIS VIS06 VIS08 NIR16

Objectives and strategy Near real time / real time applications  operational evaluation of the calibration coefficients Long time series analysis / climate applications  re-analysis with consistent calibration for the various missions (31 years of archived data) Strategy: Vicarious calibration using reference RTM simulations and comparing with TOA radiances 2 target types (potentially 3): Desert bright targets (18 targets) Dark sea targets (10 targets) Deep convective clouds

SEVIRI Solar Channel Calibration methodology Initially for MSG/SEVIRI  Extended to Meteosat missions Definition of the “reference”: simulated Top-Of-Atmosphere radiances Evaluation of the “reference”: against well-calibrated polar-orbiting instruments (SeaWiFs, ATSR2, AATSR, VEGETATION, MERIS) Advantages of using bright desert targets: If well chosen  very stable Many potential targets in the Meteosat FOV Disadvantages of using bright desert targets If not well chosen  seasonal variations Difficulty to characterize the surface reflectance Aerosol characterization

The SEVIRI Solar Channel Calibration algorithm Meteosat Images (Lev. 1.5) Data accumulation (5 up to 10 days) Radiances Target identification Pixel extraction RTM calculation Cloud mask + Cloud analysis K + K0 + target properties (surf + atm) Cloud free scenes / admissible geometry and surface conditions Calibration coefficients + associated errors ECMWF (Analysis – 6h) H2O total column content Surface pressure 10m U/V wind Quality control Update Lev.1.5 headers TOMS + AERONET climatology LUTs O3 total column content Aerosol AOD Final calibration coefficients + associated errors + Quality indicator Estimate of the sensor temporal drift + associated error

Example of results – MSG1 Impact of the nature of the signal on the use of some target types September 2007 Need for quality check and error estimation in order to remove outliers and poor quality retrievals Large differences in the amount of retrievals

SSCC references Govaerts, Y. M. and M. Clerici (2004). "Evaluation of radiative transfer simulations over bright desert calibration sites." IEEE Transactions on Geoscience and Remote Sensing 42(1): 176-187. Govaerts, Y. M., M. Clerici, et al. (2004). "Operational Calibration of the Meteosat Radiometer VIS Band." IEEE Transactions on Geoscience and Remote Sensing 42(9): 1900-1914. Govaerts, Y. M., and Clerici, M. (2004). MSG-1/SEVIRI Solar Channels Calibration Commissioning Activity Report (EUMETSAT).

Current status – Meteosat 8 – Level 1.5 Headers

Current status – Meteosat 9 – Level 1.5 Headers

Current status SEVIRI / MSG 1 Difference Desert / Sea targets: VIS06 = 8.16 % VIS08 = 6.26% NIR16= not quantifiable HRVIS = 1.54% SEVIRI / MSG 2 VIS06 = 7.74 % VIS08 = 3.95 % HRVIS = 1.38%

Impact of calibration uncertainties... Retrieval of the aerosol optical depth using MSG/SEVIRI data from VIS06 / VIS08 / NIR16  Comparisons with AERONET (Wagner, Govaerts, and Lattanzio, Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 2. Implementation and evaluation, JGR, 2010) Other sources of uncertainties in the retrieval scheme could also explain the differences Non corrected data Correction: VIS06 = 9% VIS08 = 6% NIR16 = none Corrected data Mean RMSE RMSE std dev Mean Bias Bias std dev No correction 0.145 0.063 0.032 0.0480 Correction 0.055 0.015 0.0476

Future work and developments -1 In order to : Meet the requirements on MTG/FCI: 5% accuracy (half the current requirement on MSG/SEVIRI) Improve SEVIRI current calibration system Re-visit the Meteosat archive Need for reducing uncertainties and improving calibration accuracy What is foreseen with SSCC ? Re-assessment of the current system uncertainties Use of MODIS / MISR data in the current system Definition of more stable desert targets + characterization of the associated BRF Improvement of the RTM Improvement of the aerosol climate data sets, and use of an dust-aerosol mask to avoid dust events to be processed if not well detected Re-visit the consistency analysis, taking the new BRF errors into account Re-evaluation of the reference against reference instruments (MODIS, MISR, MERIS-like, ATSR-like, VEGETATION, PARASOL...) For non-window channels: use of DCCs + homogeneous water clouds as targets

Future work and developments - 2 Sphericity: Spherical 6S Non-spherical LDA_NSP_MEDRAD Aerosol load: Aerosol Optical Thickness = 0.2 Aerosol Optical Thickness = 0.4

Future work and developments - 3 What about lunar calibration ? With SEVIRI, information available in Lev 1.0 + Lev 1.5 images Very stable surface properties Reference = entire Moon radiance Limited number of observations (up to 30min in the FOV, 3 up to 5 times / month)  Useful for long-term drift but no use for operational calibration Rectification needed to avoid double counting of the pixels due to lunar motion Difficult to estimate accurately the surface reflectance (at pixel level and total) Forward model: need to predict lunar reflectance according to the illumination/observation geometry SEVIRI Level 1.0 image (forward and backward scan)

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