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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 11 | Copyright

Special issue: Ten years of Ozone Monitoring Instrument (OMI) observations...

Atmos. Meas. Tech., 10, 4079-4098, 2017
https://doi.org/10.5194/amt-10-4079-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 01 Nov 2017

Research article | 01 Nov 2017

Aerosol-type retrieval and uncertainty quantification from OMI data

Anu Kauppi1,2, Pekka Kolmonen1, Marko Laine1, and Johanna Tamminen1 Anu Kauppi et al.
  • 1Finnish Meteorological Institute, Helsinki, Finland
  • 2Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland

Abstract. We discuss uncertainty quantification for aerosol-type selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs) and top-of-atmosphere (TOA) spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval.

This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions.

The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model selection. The posterior probability distribution can provide a comprehensive characterisation of the uncertainty in this kind of problem for aerosol-type selection. As a result, the proposed method can account for the model error and also include the model selection uncertainty in the total uncertainty budget.

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The paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The proposed method is based on Bayesian inference approach and can account for the model error and also include the model selection uncertainty in the total uncertainty budget. The method is applied to OMI measurements but is also applicable to other instruments. The retrieval was evaluated by comparison with ground-based measurements.
The paper focuses on the aerosol microphysical model selection and characterisation of...
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