A Novel Framework to Harmonise Satellite Data Series for Climate Applications

• Main Authors: Ralf Giering, Ralf Quast, Jonathan P. D. Mittaz, Samuel E. Hunt, Peter M. Harris, Emma R. Woolliams, Christopher J. Merchant
• Format: Article
• Language: English
• Published: MDPI AG 2019-04-01
• Series: Remote Sensing
• Subjects: climate data record; fundamental climate data record; calibration; harmonisation; Advanced Along Track Scanning Radiometer (AATSR); Advanced Very High Resolution Radiometer (AVHRR); uncertainty propagation; metrology; errors-in-variables; algorithmic differentiation; Earth observation; remote sensing
• Online Access: https://www.mdpi.com/2072-4292/11/9/1002

Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since any satellite only operates over a relatively short period of time, creating a climate data record also requires the combination of space-borne measurements from a series of several (often similar) satellite sensors. Simply combining calibrated measurements from several sensors can, however, produce an inconsistent climate data record. This is particularly true of older, historic sensors whose behaviour in space was often different from their behaviour during pre-launch calibration and more scientific value can be derived from considering the series of historical and present satellites as a whole. Here, we consider harmonisation as a process that obtains new calibration coefficients for revised sensor calibration models by comparing calibrated measurements over appropriate satellite-to-satellite matchups, such as simultaneous nadir overpasses and which reconciles the calibration of different sensors given their estimated spectral response function differences. We present the concept of a framework that establishes calibration coefficients and their uncertainty and error covariance for an arbitrary number of sensors in a metrologically-rigorous manner. We describe harmonisation and its mathematical formulation as an inverse problem that is extremely challenging when some hundreds of millions of matchups are involved and the errors of fundamental sensor measurements are correlated. We solve the harmonisation problem as marginalised errors in variables regression. The algorithm involves computation of first and second-order partial derivatives using Algorithmic Differentiation. Finally, we present re-calibrated radiances from a series of nine Advanced Very High Resolution Radiometer sensors showing that the new time series has smaller matchup differences compared to the unharmonised case while being consistent with uncertainty statistics.