20.500.12556/DiRROS-25416 Predictions of failed satellite retrieval of air quality using machine learning 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. trace gases failure prediction plini v sledovih zaznavanje napak napovedovanje napak true false false Copernicus Publications Nemčija Angleški jezik Slovenski jezik © Author(s) 2025. Neznano 2026-01-20 09:46:10 2026-01-20 09:46:11 2026-01-21 03:49:59 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Soavtorji iz Slovenije: Jure Brence, Jovan Tanevski, Sašo Džeroski; Opis vira z dne 19. 1. 2026; str. 1689-1715 iss. 7 Vol. 18 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-07-27 2025-02-10 2025-04-16 004.8 1867-8548 10.5194/amt-18-1689-2025 265157123 https://amt.copernicus.org/articles/18/1689/2025/ 1 7b3ffb68-f5dc-11f0-a15d-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=38489 RAZ_Malina_Edward_2025.pdf RAZ_Malina_Edward_2025.pdf 1 EBD4DF5E7ED6AAB26E3F811A93CF8AC7 0622c964dd52be193480377bef4ca6a0ccd85d84ab1d959e0d98ea0d09475e08 db04f4bc-f5dc-11f0-a15d-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=38490 Institut Jožef Stefan 0 0 0