A novel method for vicarious re-characterisation of the MVIRI VIS spectral response to facilitate climate monitoring

<p>The Meteosat Visible and Infrared (MVIRI) sensors on board Meteosat First Generation (MFG) geostationary satellites (1982 - today) acquire radiance every 30 minutes in a broad spectral band, ranging approximately from 0.4 to 1.1 µm and referred to as the visible (VIS) band.</p><p>The original objective of the MFG programme was the acquisition of Earth images to provide the meteorological community with information on atmospheric circulation and weather. One of the objectives of the H2020 Fidelity and Uncertainty in Climate Data Records from Earth Observation (FIDUCEO) project is to re-evaluate the VIS acquisitions in a metrological rigorous way to generate new fundamental and thematic climate data records with traceable uncertainty and stability estimates. The clue to this re-evaluation is an accurate characterisation of the VIS spectral response functions, which were characterised poorly pre-launch and had apparently been degrading stronger in the blue than the near-infrared part of the VIS band.<br></p><p>In consequence, a novel practice for a vicarious re-characterisation of the MVIRI VIS spectral response function has been developed. The so termed reverse engineering method applies advanced algorithmic differentiation techniques to calculate daily maximum posterior probability estimates of the sensor spectral response and accurate and traceable measures of its uncertainty and spectral error covariance.</p><p>This presentation explains the key concepts of the method and imparts new findings and results of its application to the Meteosat Second Generation (MSG) Meteosat-10 and Meteosat-7 visible bands.</p><p><br></p><p><i>Oral presentation given at the EUMETSAT Meteorological Satellite Conference, Rome, 2-6 October 2017.</i></p

Creating Fidelitous Climate Data Records from Meteosat First Generation Observations

Essay on the reconstruction of the Meteosat VIS band spectral response function in the course of the FIDUCEO project. Conference paper contributed to the ESA Living Planet Symposium, Prague, May 2016:<div><br></div><div><p><a><i>Paper 1442</i></a><i> - Session title: Atmosphere & Climate Posters</i></p><p><strong>ATMO-178 - Creating Fidelitous Climate Data Records from Meteosat First Generation VIS Band Observations</strong></p></div

A novel framework to harmonise satellite data series for climate applications

<div> <div> <div> <div> <p>Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. </p> <p>Since any satellite operates over a limited period of time only, creating a climate data record requires the combination of space-born measurements from a series of several (often similar) satellites. </p> <p>Simple combination of measurements from several sensors, however, will produce an inconsistent climate data record because the behaviour of historical satellites in space was often different from their behaviour during pre-launch calibration in the laboratory. More scientific value can be derived from considering the series of historical and present satellites as a whole. </p> <p>Here we consider harmonisation as a process that obtains new calibration coefficients and a revised calibration model for each sensor by comparing the output of each satellite to radiometrically more accurate sensors using appropriate match-ups, such as simultaneous nadir overpasses. </p> </div> <div> <p>When we perform a comparison of two sensors using match- ups, we must take into account the fact that those sensors are not observing exactly the same Earth radiance. This is in part due to uncertainties in the collocation process itself, but also due to differences in the spectral response functions of the two instruments, even when nominally observing the same spectral band. </p> <p>We do not aim to correct for spectral response function differences, but to reconcile the calibration of different sensors given their estimated spectral response function differences. </p> <p>Here we present the concept of a framework that establishes calibration coefficients for several sensors simultaneously and rigorously with respect to their uncertainty and error covariance. </p> </div> <div> <p>We present the harmonisation and its mathematical formulation as a large-structured inverse problem. Solving this problem is a challenge because it involves some hundred million of match-ups and has significant error correlation in the measured data. </p> <p>We sketch different approaches to solve the harmonisation problem and present our first attempt to recalibrate AVHRR radiance from a series of nine NOAA and MetOp satellites. </p> </div> </div> </div></div><div><br></div><i>Presented at the EUMETSAT Meteorological Satellite Conference, Rome, October 2017.</i><br