A Novel Framework to Harmonise Satellite Data Series for Climate Applications
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 meas...
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doaj-d88a57a8e3a74c15a5dd1e95da560bc62020-11-25T02:01:07ZengMDPI AGRemote Sensing2072-42922019-04-01119100210.3390/rs11091002rs11091002A Novel Framework to Harmonise Satellite Data Series for Climate ApplicationsRalf Giering0Ralf Quast1Jonathan P. D. Mittaz2Samuel E. Hunt3Peter M. Harris4Emma R. Woolliams5Christopher J. Merchant6FastOpt GmbH, 22767 Hamburg, GermanyFastOpt GmbH, 22767 Hamburg, GermanyDepartment of Meteorology, University of Reading, Reading RG6 6AL, UKNational Physical Laboratory, Teddington TW11 0LW, UKNational Physical Laboratory, Teddington TW11 0LW, UKNational Physical Laboratory, Teddington TW11 0LW, UKDepartment of Meteorology, University of Reading, Reading RG6 6AL, UKFundamental 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.https://www.mdpi.com/2072-4292/11/9/1002climate data recordfundamental climate data recordcalibrationharmonisationAdvanced Along Track Scanning Radiometer (AATSR)Advanced Very High Resolution Radiometer (AVHRR)uncertainty propagationmetrologyerrors-in-variablesalgorithmic differentiationEarth observationremote sensing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ralf Giering Ralf Quast Jonathan P. D. Mittaz Samuel E. Hunt Peter M. Harris Emma R. Woolliams Christopher J. Merchant |
spellingShingle |
Ralf Giering Ralf Quast Jonathan P. D. Mittaz Samuel E. Hunt Peter M. Harris Emma R. Woolliams Christopher J. Merchant A Novel Framework to Harmonise Satellite Data Series for Climate Applications Remote Sensing 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 |
author_facet |
Ralf Giering Ralf Quast Jonathan P. D. Mittaz Samuel E. Hunt Peter M. Harris Emma R. Woolliams Christopher J. Merchant |
author_sort |
Ralf Giering |
title |
A Novel Framework to Harmonise Satellite Data Series for Climate Applications |
title_short |
A Novel Framework to Harmonise Satellite Data Series for Climate Applications |
title_full |
A Novel Framework to Harmonise Satellite Data Series for Climate Applications |
title_fullStr |
A Novel Framework to Harmonise Satellite Data Series for Climate Applications |
title_full_unstemmed |
A Novel Framework to Harmonise Satellite Data Series for Climate Applications |
title_sort |
novel framework to harmonise satellite data series for climate applications |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-04-01 |
description |
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. |
topic |
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 |
url |
https://www.mdpi.com/2072-4292/11/9/1002 |
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