News

Scientists from the EU’s Copernicus Climate Change Service (C3S) implemented by ECMWF have observed major changes in Arctic precipitation, air temperature and sea-ice coverage over the most recent three decades. Reanalysis enables a new focus on the Arctic climate.

An upgrade of the Integrated Forecasting System (IFS) to Cycle 48r1 is at the centre of our plans for 2023, ECMWF Director-General Florence Rabier has said in an interview that previews the Centre’s activities this year.

Numerical weather predictions rely on observations to help establish the initial conditions of forecasts as accurately as possible. Mohamed Dahoui monitors weather observations at ECMWF to ensure they are of good quality.

ECMWF has published a new Software Strategy, outlining its plans for the development of software outside the weather prediction process until 2027. One of the guiding ideas is for software to be developed openly.

The ECMWF-coordinated CoCO2 project to build a prototype system to monitor anthropogenic CO2 emissions worldwide has moved into its final year. This EU-funded initiative is on track to feed into a permanent anthropogenic greenhouse gas emissions Monitoring and Verification Support Capacity.

The winter 2022/23 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.

Machine learning is entering many aspects of weather forecasting. At ECMWF, a team of three people is supporting these activities. They expect machine learning to continue to play a growing role in weather forecasting.

The year 2022 was the second warmest on record in Europe, and the summer of 2022 was the hottest in Europe, data released by the European Union’s Copernicus Climate Change Service (C3S) run by ECMWF show.

ECMWF news highlights of 2022 include forecast data updates; projects and workshops to drive forward weather science; and news from the EU-funded services implemented by ECMWF.

Twenty-five years ago, ECMWF introduced ‘4D-Var’ to the assimilation of observational data into its weather model. Changes worked on over the last few years include reducing the effects of model biases using machine learning techniques.