Unlocking the Potential of MTG FCI Data for Severe Storm Nowcasting and NWP Applications

The Meteosat Third Generation (MTG) Flexible Combined Imager (FCI) was declared operational in December 2024 and offers new opportunities for enhancing the observation and prediction of severe convective storms. This presentation will explore how novel retrieval and enhancement techniques using FCI data—also when combined with artificial intelligence and machine learning (AI/ML)—can produce new, valuable inputs for severe storm nowcasting and numerical weather prediction (NWP) assimilation. 

First, we present the available FCI Level-2 products from the EUMETSAT Central Facility useful for severe storm studies. We also describe the development of a new total column water vapour (TCWV) retrieval based on the 0.9 µm band of FCI, as part of a multi-mission framework for optical imagers. This approach aims to improve the quantitative characterization of atmospheric low-level moisture—an essential parameter for storm development. 

Secondly, we report on efforts to derive pseudo-radar reflectivity fields from FCI L1C imagery and Lightning Imager (LI) L2 products using AI/ML models trained on coincident radar and satellite datasets. This method has shown promising results in the U.S. using GOES data for identifying convective cores in real time, offering radar-like information where no ground-based systems exist. 

A third study focuses on enhancing the temporal resolution of FCI visible imagery by leveraging the high-frequency (60s) Lightning Imager (LI) background data. AI/ML super-resolution methods are being evaluated to combine the image detail cloud-top structure information from FCI, with the temporal resolution of the lower resolution LI background data. 

Finally, we investigate sharpening FCI’s normal-resolution (FDHSI) channels by using information from the four high-resolution (HRFI) channels. Deep learning techniques are applied to reconstruct finer spatial detail, with the aim to enhance storm top feature detection, analysis, tracking and nowcasting. 

Together, these developments aim to enrich the suite of observational products available from MTG, offering improved data for forecasters and potentially beneficial for NWP data assimilation. Examples from test cases and prototype products will be presented to illustrate the relevance of these enhancements.