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