A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements
2024
Kauppi, Anu | Kukkurainen, Antti | Lipponen, Antti | Laine, Marko | Arola, Antti | Lindqvist, Hannakaisa | Tamminen, Johanna | Ilmatieteen laitos | Finnish Meteorological Institute | 0009-0008-1000-0350 | 0000-0002-3371-7337 | 0000-0002-6902-9974 | 0000-0002-5914-6747 | 0000-0002-9220-0194 | 0000-0001-9202-906X | 0000-0003-3095-0069
This article presents a method within a Bayesian framework for quantifying uncertainty in satellite aerosol remote sensing when retrieving aerosol optical depth (AOD). By using a Bayesian model averaging technique, we take into account uncertainty in aerosol optical model selection and also obtain a shared inference about AOD based on the best-fitting optical models. In particular, uncertainty caused by forward-model approximations has been taken into account in the AOD retrieval process to obtain a more realistic uncertainty estimate. We evaluated a model discrepancy, i.e., forward-model uncertainty, empirically by exploiting the residuals of model fits and using a Gaussian process to characterise the discrepancy. We illustrate the method with examples using observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite. We evaluated the results against ground-based remote sensing aerosol data from the Aerosol Robotic Network (AERONET).
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