News

ECMWF and the NWP SAF brought together almost 200 experts in a virtual workshop from 2 to 5 November to discuss how the latest research into the treatment of random and systematic errors in satellite data assimilation can help to improve weather forecasts.

The autumn 2020 issue of the ECMWF Newsletter is now available. As well as news about ECMWF staff and events, it features articles about new developments and the use that can be made of ECMWF products.

ECMWF’s 2021 events programme is set to include both virtual and physical events. It features workshops on cloud technologies, ocean data assimilation and hydrological prediction alongside established events such as our user meeting, Annual Seminar and workshop on HPC in meteorology.

The global forecasting system of the Copernicus Atmosphere Monitoring Service (CAMS) was successfully upgraded on 6 October, leading to improved forecasts of sea salt and dust aerosol, particulate matter and stratospheric ozone. CAMS is implemented by ECMWF on behalf of the European Commission.

New Python packages and training with Jupyter notebooks are making it simpler to access and use vast weather and climate datasets from ECMWF and Copernicus.

The first ECMWF–ESA Workshop on Machine Learning for Earth System Observation and Prediction drew a large virtual attendance of international experts in machine learning and Earth system sciences.

Floods impact billions of people worldwide. Learn about major new upgrades to the European Flood Awareness System (EFAS) and explore how the Global Flood Awareness System (GloFAS) helped communities prepare for, and recover from, this summer’s monsoon floods in Bangladesh.

Hundreds of ECMWF forecast charts are now free and accessible to all.

ECMWF and Atos are opening a Center of Excellence in HPC, AI, and Quantum Computing for Weather & Climate to support ECMWF scientists in their work on medium- and long-range weather prediction, and global climate modelling.

An ECMWF/ESA workshop from 5 to 8 October 2020 will explore the application of machine and deep learning techniques in Earth system observation and prediction. Marcin Chrust considers what scope there may be for such techniques in ECMWF’s Object-Oriented Prediction System.