Preprints
https://doi.org/10.5194/amt-2020-229
https://doi.org/10.5194/amt-2020-229

  29 Sep 2020

29 Sep 2020

Review status: a revised version of this preprint was accepted for the journal AMT and is expected to appear here in due course.

Model Enforced Post-Process Correction of Satellite Aerosol Retrievals

Antti Lipponen1, Ville Kolehmainen2, Pekka Kolmonen1, Antti Kukkurainen1, Tero Mielonen1, Neus Sabater1, Larisa Sogacheva1, Timo H. Virtanen1, and Antti Arola1 Antti Lipponen et al.
  • 1Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
  • 2Department of Applied Physics, University of Eastern Finland, Kuopio, Finland

Abstract. Satellite-based aerosol retrievals provide a timely global view of atmospheric aerosol properties for air quality, atmospheric characterization, and correction of satellite data products and climate applications. Current aerosol data products based on satellite data, however, often have relatively large biases relative to accurate ground-based measurements and distinct levels of uncertainty associated with them. These biases and uncertainties are often caused by oversimplified assumptions and approximations used in the retrieval algorithms due to unknown surface reflectance or fixed aerosol models. Moreover, the retrieval algorithms do not usually take advantage of all the possible observational data collected by the satellite instruments and may, for example, leave some spectral bands unused. The improvement and the re-processing of the past and current operational satellite data retrieval algorithms would become a tedious and computationally expensive task. To overcome this burden, we have developed a model enforced post-process correction approach that can be used to correct the existing and operational satellite aerosol data products. Our approach combines the existing satellite aerosol retrievals and a post-processing step carried out with a machine learning based correction model for the approximation error in the retrieval. The developed approach allows for the utilization of auxiliary data sources, such as meteorological information, or additional observations such as spectral bands unused by the original retrieval algorithm. The post-process correction model can learn to correct for the biases and uncertainties in the original retrieval algorithms. As the correction is carried out as a post-processing step, it allows for computationally efficient re-processing of existing satellite aerosol datasets with no need to fully reprocess the much larger original radiance data. We demonstrate with over land aerosol optical depth (AOD) and Angstrom exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. We also give recommendations for the validation of satellite data products that are constructed using machine learning based models.

Antti Lipponen et al.

 
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Status: closed
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Antti Lipponen et al.

Antti Lipponen et al.

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Short summary
We have developed a new computational method to post-process correct the satellite aerosol retrievals. The proposed method combines the conventional satellite aerosol retrievals relying on physics-based models and machine learning. The results show significantly improved accuracy in the aerosol data over the operational satellite data products. The correction can be applied to the existing satellite aerosol datasets with no need to fully re-process the much larger original radiance data.