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1
https://www-cdn.eumetsat.int/files/2021-03/05%20-%20LEINONEN%20-%20Evaluation%20MTG%20benefits%20in%20thunderstorm%20nowcasting%20based%20on%20machine%20learning.zip
https://www-cdn.eumetsat.int/files/2020-05/pdf_mtg_li_mag_9_tor.pdf
https://www-cdn.eumetsat.int/files/2023-10/01%20-%20LEINONEN%20-%20Multi-source%20thunderstorm%20nowcasting%20with%20AI%281375517%20V2%29.pdf
https://www-cdn.eumetsat.int/files/2022-03/05%20-%20LEINONEN%20-%20Multi-source%20nowcasting%20of%20thunderstorm%20hazards%20with%20convolutional-recurrent%20deep%20learning%20network.zip
Building scientific relationships and supporting young scientists.
Validating the MTG Lightning Imager (LI) with scenarios of Lightning Mapping Array (LMA) in-field campaigns in Africa.
https://www-cdn.eumetsat.int/files/2023-10/EUMETSAT_fellow_day_2023_Erdmann.pdf
https://www-cdn.eumetsat.int/files/2022-12/LI_MAG14_D110_Erdmann_LI_for_NWC.pdf
https://www-cdn.eumetsat.int/files/2021-03/06%20-%20ERDMANN%20-%20Towards%20an%20automated%20severe%20weather%20warning%20tool%20based%20on%20MTG-LI%20and%20FCI%20data.pdf
https://www-cdn.eumetsat.int/files/2021-03/IASI_study_final_report_omsz.pdf
https://www-cdn.eumetsat.int/files/2020-06/pdf_mtg_li_mag_9_asim.pdf
https://www-cdn.eumetsat.int/files/2020-04/pdf_vis_sci_rep_2014_1.pdf