We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Model Enforced Post-Process Correction of Satellite Aerosol Retrievals.
- Authors
Lipponen, Antti; Kolehmainen, Ville; Kolmonen, Pekka; Kukkurainen, Antti; Mielonen, Tero; Sabater, Neus; Sogacheva, Larisa; Virtanen, Timo H.; Arola, Antti
- 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.
- Subjects
AEROSOLS; ATMOSPHERIC aerosols; TROPOSPHERIC aerosols; ARTIFICIAL satellites; WATER vapor; AIR quality; APPROXIMATION error
- Publication
Atmospheric Measurement Techniques Discussions, 2020, p1
- ISSN
1867-8610
- Publication type
Article
- DOI
10.5194/amt-2020-229