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Remote Sensing
Volume 14
Issue 9
10.3390/rs14092197
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Technical Note

Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework

by
Arno Keppens
*,
Steven Compernolle
,
Daan Hubert
,
Tijl Verhoelst
,
José Granville
and
Jean-Christopher Lambert
Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Avenue Circulaire 3, 1180 Brussels, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(9), 2197; https://doi.org/10.3390/rs14092197
Submission received: 25 March 2022 / Revised: 29 April 2022 / Accepted: 2 May 2022 / Published: 4 May 2022
(This article belongs to the Special Issue Multi Sensor Data Integration for Atmospheric Composition Analysis)
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Abstract

A method is developed that removes a priori information from remotely sensed atmospheric state profiles. This consists of a Wiener deconvolution, whereby the required cost function is obtained from the complete data fusion framework. Asserting that the deconvoluted averaging kernel matrix has to equal the unit matrix, results in an iterative process for determining a profile-specific deconvolution matrix. In contrast with previous deconvolution approaches, only the dimensions of this matrix have to be fixed beforehand, while the iteration process optimizes the vertical grid. This method is applied to ozone profile retrievals from simulated and real measurements co-located with the Izaña ground station. Individual profile deconvolutions yield strong outliers, including negative ozone concentration values, but their spatiotemporal averaging results in prior-free atmospheric state representations that correspond to the initial retrievals within their uncertainty. Averaging deconvoluted profiles thus looks like a viable alternative in the creation of harmonized Level-3 data, avoiding vertical smoothing difference errors and the difficulties that arise with averaged averaging kernels.
Keywords: atmospheric retrieval; prior information; Wiener deconvolution; complete data fusion atmospheric retrieval; prior information; Wiener deconvolution; complete data fusion

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MDPI and ACS Style

Keppens, A.; Compernolle, S.; Hubert, D.; Verhoelst, T.; Granville, J.; Lambert, J.-C. Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework. Remote Sens. 2022, 14, 2197. https://doi.org/10.3390/rs14092197

AMA Style

Keppens A, Compernolle S, Hubert D, Verhoelst T, Granville J, Lambert J-C. Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework. Remote Sensing. 2022; 14(9):2197. https://doi.org/10.3390/rs14092197

Chicago/Turabian Style

Keppens, Arno, Steven Compernolle, Daan Hubert, Tijl Verhoelst, José Granville, and Jean-Christopher Lambert. 2022. "Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework" Remote Sensing 14, no. 9: 2197. https://doi.org/10.3390/rs14092197

APA Style

Keppens, A., Compernolle, S., Hubert, D., Verhoelst, T., Granville, J., & Lambert, J.-C. (2022). Removing Prior Information from Remotely Sensed Atmospheric Profiles by Wiener Deconvolution Based on the Complete Data Fusion Framework. Remote Sensing, 14(9), 2197. https://doi.org/10.3390/rs14092197

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