Science documents Apply Refine results Type Files & Media Document Categories Science Data Meteosat Metop Tags Presentation ASCAT IRS MSG SEVIRI MTG Research Fellows IASI Date 2022 2021 2020 2019 2018 2017 2016 2015 2014 SHOW ALL 48 results EUMETSAT 1 Improving the Use of Atmospheric Motion Vectors in NWP 2022 https://www-cdn.eumetsat.int/files/2022-03/10%20-%20WARRICK%20-%20Improving%20the%20Use%20of%20Atmospheric%20Motion%20Vectors%20in%20NWP.zip Improving the Use of Atmospheric Motion Vectors in NWP 2022 EUMETSAT 1 Maximising the Exploitation of Window Channel Radiance Observations from Geostationary Spacecraft 2022 https://www-cdn.eumetsat.int/files/2022-03/09%20-%20QUESADA-RUIZ%20-%20Maximising%20the%20Exploitation%20of%20Window%20Channel%20Radiance%20Observations%20from%20Geostationary%20Spacecraft.zip Maximising the Exploitation of Window Channel Radiance Observations from Geostationary Spacecraft 2022 EUMETSAT 1 Towards a data-driven nowcasting of severe weather with Meteosat Third Generation 2022 https://www-cdn.eumetsat.int/files/2022-03/07%20-%20KUCUK%20-%20Towards%20a%20data-driven%20nowcasting%20of%20severe%20weather%20with%20Meteosat%20Third%20Generation.pdf Towards a data-driven nowcasting of severe weather with Meteosat Third Generation 2022 EUMETSAT 1 Developments in the all-sky assimilation of microwave imagers at ECMWF 2022 https://www-cdn.eumetsat.int/files/2022-03/11%20-%20SCANLON%20-%20Developments%20in%20the%20all-sky%20assimilation%20of%20microwave%20imagers%20at%20ECMWF.pdf Developments in the all-sky assimilation of microwave imagers at ECMWF 2022 EUMETSAT 1 Multi-source nowcasting of thunderstorm hazards with convolutional-recurrent deep learning network 2022 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 Multi-source nowcasting of thunderstorm hazards with convolutional-recurrent deep learning network 2022 EUMETSAT 1 Expanding All-sky AMSU-A Assimilation to Its Window Channels 2022 https://www-cdn.eumetsat.int/files/2022-03/08%20-%20DUNCAN%20-%20Expanding%20All-sky%20AMSU-A%20Assimilation%20to%20Its%20Window%20Channels.pdf Expanding All-sky AMSU-A Assimilation to Its Window Channels 2022 EUMETSAT 1 Analyzing automatically detected lightning jumps from optical Geostationary Lightning Mapper (GLM) lightning observations 2022 https://www-cdn.eumetsat.int/files/2022-03/06%20-%20ERDMANN%20-%20Analyzing%20automatically%20detected%20lightning%20jumps%20from%20optical%20Geostationary%20Lightning%20Mapper%20%28GLM%29%20lightning%20observations.pdf Analyzing automatically detected lightning jumps from optical Geostationary Lightning Mapper (GLM) lightning observations 2022 EUMETSAT 1 Preparation for the assimilation of the future IRS sounder in NWP models 2022 https://www-cdn.eumetsat.int/files/2022-03/04%20-%20COOPMAAN%20-%20Preparation%20for%20the%20assimilation%20of%20the%20future%20IRS%20sounder%20in%20NWP%20models_0.zip Preparation for the assimilation of the future IRS sounder in NWP models 2022 EUMETSAT 1 Transformed retrievals at the Met Office - Assimilation results and a strategy for bias correction 2022 https://www-cdn.eumetsat.int/files/2022-03/03%20-%20LEVENS%20-%20Transformed%20retrievals%20at%20the%20Met%20Office%20-%20Assimilation%20results%20and%20a%20strategy%20for%20bias%20correction.zip Transformed retrievals at the Met Office - Assimilation results and a strategy for bias correction 2022 EUMETSAT 1 Towards triple collocation analysis of 4D wind observations 2022 https://www-cdn.eumetsat.int/files/2022-03/02%20-%20COSSU%20-%20Triple%20collocation%20analysis%20of%204D%20wind%20observations%202022.zip Towards triple collocation analysis of 4D wind observations 2022 EUMETSAT 1 CREATE – Characterising and REducing uncertainties in All-sky microwave radiative TransfEr 2022 https://www-cdn.eumetsat.int/files/2022-03/01%20-%20BARLAKAS%20-%20CREATE%20%E2%80%93%20Characterising%20and%20REducing%20uncertainties%20in%20All-sky%20microwave%20radiative%20TransfEr.pdf CREATE – Characterising and REducing uncertainties in All-sky microwave radiative TransfEr 2022 EUMETSAT 1 Towards an automated severe weather warning tool based on MTG-LI and FCI data 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 Towards an automated severe weather warning tool based on MTG-LI and FCI data Load More