Predictions of failed satellite retrieval of air quality using machine learning

Abstract

The growing fleet of Earth observation (EO) satellites is capturing unprecedented quantities of information about the concentration and distribution of trace gases in the Earth's atmosphere. Depending on the instrument and algorithm, the yield of good remote soundings can be a few percent owing to interferences such as clouds, non-linearities in the retrieval algorithm, and systematic errors in the radiative transfer algorithm, leading to inefficient use of computational resources. In this study, we investigate machine learning (ML) techniques to predict failures in the trace gas retrieval process based upon the input satellite radiances alone, allowing for efficient production of good-quality data. We apply this technique to ozone and other retrievals using measurements from multiple satellites: the Suomi National Polar-orbiting Partnership Cross-Track Infrared Sounder (Suomi NPP CrIS) and joint retrievals from the Atmospheric Infrared Sounder (AIRS) Ozone Monitoring Instrument (OMI). Retrievals are performed using the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) algorithm. With this tool, we can identify 80 % of ozone retrieval failures using the MUSES algorithm at a cost of 20 % false positives from CrIS. For AIRS-OMI, 98 % of ozone retrieval failures are identified at a cost of 2 % false positives. The ML tool is simple to generate and takes <0.1 s to assess each measured spectrum. The results suggest that this tool can be applied to data from many EO satellites and can reduce the processing load for current and future instruments.