Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

Journal metrics

  • IF value: 2.989 IF 2.989
  • IF 5-year<br/> value: 3.489 IF 5-year
    3.489
  • CiteScore<br/> value: 3.37 CiteScore
    3.37
  • SNIP value: 1.273 SNIP 1.273
  • SJR value: 2.026 SJR 2.026
  • IPP value: 3.082 IPP 3.082
  • h5-index value: 45 h5-index 45
doi:10.5194/amt-2017-47
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
22 Mar 2017
Review status
This discussion paper is under review for the journal Atmospheric Measurement Techniques (AMT).
Aerosol type retrieval and uncertainty quantification from OMI data
Anu Kauppi1,2, Pekka Kolmonen1, Marko Laine1, and Johanna Tamminen1 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 pre-calculated aerosol microphysical models stored in look-up tables (LUTs) and top of atmosphere spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modeled 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 characterization 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 where 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 each main aerosol type is in representing the underlying atmospheric aerosol conditions.

The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using multi-wavelength 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 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 characterization 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.


Citation: Kauppi, A., Kolmonen, P., Laine, M., and Tamminen, J.: Aerosol type retrieval and uncertainty quantification from OMI data, Atmos. Meas. Tech. Discuss., doi:10.5194/amt-2017-47, in review, 2017.
Anu Kauppi et al.
Anu Kauppi et al.
Anu Kauppi et al.

Viewed

Total article views: 306 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
199 93 14 306 9 16

Views and downloads (calculated since 22 Mar 2017)

Cumulative views and downloads (calculated since 22 Mar 2017)

Viewed (geographical distribution)

Total article views: 306 (including HTML, PDF, and XML)

Thereof 305 with geography defined and 1 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 23 Apr 2017
Publications Copernicus
Download
Short summary
The paper focuses on the aerosol microphysical model selection and characterization 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 applicable to other instruments also. The retrieval was evaluated by comparison with ground-based measurements.
The paper focuses on the aerosol microphysical model selection and characterization of...
Share