Introducing Anemoi: a new collaborative framework for ML weather forecasting
Dramsch, Jesper ; Raoult, Baudouin ; Chantry, Matthew ; García, Teresa ; Denby, Leif ; Prill, Florian ; Sokka, Niko ; Vocino, Antonio ; Wijnands, Jasper ; Nipen, Thomas ; Osuna, Carlos ; Akodad, Sara ; Van Ginderachter, Michiel ; Van den Bleeken, Dieter
A range of national meteorological services across Europe and ECMWF are pleased to announce the launch of Anemoi, a Python-based framework for creating machine learning (ML) weather forecasting systems. Named after the Greek gods of the winds, Anemoi is a collaborative, open-source initiative involving the Spanish State Meteorological Agency (AEMET), the Danish Meteorological Institute (DMI), the German National Meteorological Service (DWD), the Finnish Meteorological Institute (FMI), the Italian Air Force Meteorological Service (ITAF Met Service), the Royal Netherlands Meteorological Institute (KNMI), MET Norway, Météo-France, MeteoSwiss, Belgium's Royal Meteorological Institute (RMI) and ECMWF. It has the potential to democratise access to and further develop data-driven weather forecasts.<br />The goal of Anemoi is to provide the key building blocks to train state?of-the?art data-driven models and run them in an operational context. As a framework it seeks to handle many of the complexities that meteorological organisations will share, allowing them to easily train models from existing recipes but with their own data.</p>
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