Principal Component Analysis of IASI Measurements for the Detection of Extreme Atmospheric Composition Events: Methodology and Applications

Le résumé fourni par la source

Extreme atmospheric events are rare phenomena characterized by unusual intensity or chemical composition compared to the atmospheric background state. Many such events, in the context of global warming, pose threats to human health, thereby making their early detection and monitoring essential. The Infrared Atmospheric Sounding Interferometer (IASI), able to measure more than 30 atmospheric chemical species on a global scale, offers strong potential in this context. However, the large volume of current and upcoming satellite observations makes intelligent data screening more and more challenging. This work aims to overcome this limitation by developing a dedicated algorithm based on Principal Component Analysis (PCA) of Level 1C IASI spectra. Named IASI-PCA, it is designed for the systematic detection of fires, volcanic eruptions, dust storms, pollution plumes, and other extreme atmospheric events that remain unclassified. The detected events are defined as spectral outliers relative to the representative global variability of IASI observations under normal or unperturbed conditions. The detection of an individual spectrum is driven by its anomalous behavior in selected spectral domains, where a set of optimized indicators corresponding to more than 10 species has been defined to discriminate events according to their chemical signatures. This methodology has been implemented in a near-real-time operational system providing detection and classification products within one hour and one day, respectively. The IASI-PCA approach has been applied to multiple case studies demonstrating that it is a powerful tool for: (1) efficiently detecting and monitoring fires, whether isolated sources or extended plumes; (2) handling both clear and cloudy conditions; (3) detecting dust plumes predominantly associated with calcite; (4) identifying pollution sources; and (5) detecting volcanic events. The consistency of our results with those obtained with the EUMETSAT Principal Component Compression (PCC) is also illustrated through the processing of a representative study period.

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