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Model Selection in Atmospheric Remote Sensing with an Application to Aerosol Retrieval from DSCOVR/EPIC, Part 1: Theory

Sasi, Sruthy and Natraj, V. and Molina García, Víctor and Efremenko, Dmitry and Loyola, Diego and Doicu, Adrian (2020) Model Selection in Atmospheric Remote Sensing with an Application to Aerosol Retrieval from DSCOVR/EPIC, Part 1: Theory. Remote Sensing, 12 (22), 3724_1-3724_29. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs12223724. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/12/22/3724


The retrieval of aerosol and cloud properties such as their optical thickness and/or layer/top height requires the selection of a model that describes their microphysical properties. We demonstrate that, if there is not enough information for an appropriate microphysical model selection, the solution’s accuracy can be improved if the model uncertainty is taken into account and appropriately quantified. For this purpose, we design a retrieval algorithm accounting for the uncertainty in model selection. The algorithm is based on (i) the computation of each model solution using the iteratively regularized Gauss–Newton method, (ii) the linearization of the forward model around the solution, and (iii) the maximum marginal likelihood estimation and the generalized cross-validation to estimate the optimal model. The algorithm is applied to the retrieval of aerosol optical thickness and aerosol layer height from synthetic measurements corresponding to the Earth Polychromatic Imaging Camera (EPIC) instrument onboard the Deep Space Climate Observatory (DSCOVR) satellite. Our numerical simulations show that the heuristic approach based on the thesolution minimizing the residual, which is frequently used in literature, is completely unrealistic when both the aerosol model and surface albedo are unknown.

Item URL in elib:https://elib.dlr.de/146090/
Document Type:Article
Title:Model Selection in Atmospheric Remote Sensing with an Application to Aerosol Retrieval from DSCOVR/EPIC, Part 1: Theory
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Sasi, SruthySruthy.Sasi (at) dlr.dehttps://orcid.org/0000-0002-5477-9879
Molina García, VíctorVictor.MolinaGarcia (at) dlr.deUNSPECIFIED
Efremenko, DmitryDmitry.Efremenko (at) dlr.deUNSPECIFIED
Loyola, DiegoDiego.Loyola (at) dlr.dehttps://orcid.org/0000-0002-8547-9350
Doicu, AdrianAdrian.Doicu (at) dlr.deUNSPECIFIED
Date:12 November 2020
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs12223724
Page Range:3724_1-3724_29
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:EPIC, aerosol
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research, R - Spectroscopic methods of the atmosphere
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Loyola, Dr.-Ing. Diego
Deposited On:25 Nov 2021 10:36
Last Modified:25 Nov 2021 10:36

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