ECMWF

New cloud radar data from the ESA-JAXA EarthCARE (Earth Clouds, Aerosols and Radiation Explorer) satellite mission is being assimilated into ECMWF’s global forecasting system – the first time any operational forecasting centre has assimilated this type of data.

The new observations provide an unprecedented view of the vertical structure of clouds and precipitation across the globe. Incorporating this information into the Integrated Forecasting System (IFS) will help to improve its forecasts.

ECMWF Director-General Florian Pappenberger said: “The new data from the Cloud Profiling Radar on the joint ESA-JAXA EarthCARE mission is a crucial step for reducing uncertainties in how clouds influence the weather.”

ESA - European Space Agency
JAXA (Japan Aerospace Exploration Agency)

More information
➡️ https://www.ecmwf.int/en/about/media-centre/news/2026/earthcare-cloud-radar-data-world-first

ECMWF has joined the Risk-informed Early Action Partnership (REAP), a global initiative that aims to strengthen early warning systems and improve anticipatory action in the face of climate- and weather-related hazards.

REAP brings together over 100 partners from across the climate, development, humanitarian and hydrometeorological communities.

Through its involvement in REAP, ECMWF will explore opportunities to strengthen the use of advanced forecasting capabilities, including machine learning-based approaches and supporting improved access to open meteorological and climate data.

Stijn Vermoote, Head of Partnerships and Engagement at ECMWF, said: “Joining REAP brings together ECMWF’s expertise with REAP’s action-focused network to strengthen early warning systems and early action financing, helping communities act sooner, reduce risk and save lives.”

More information
➡️ https://www.ecmwf.int/en/about/media-centre/news/2026/ecmwf-joins-reap

🌍🌐 Today is International Women in Engineering Day.

On this occasion, we are continuing our DestinE ML blog series, this time with a contribution from ECMWF scientist Rachel Furner from the Ocean Modelling team.

As part of the European Commission’s Destination Earth (DestinE) initiative, ECMWF and partners are developing machine learning (ML)-based components for next-generation Earth system models.

In this blog, we explore ECMWF’s ML-based ocean model, which aims to provide an accurate and computationally efficient representation of the ocean system.

✍️ By Rachel Furner, ECMWF scientist (Ocean Modelling Team)

📖 Read the blog: ➡️ https://destine.ecmwf.int/news/destine-blog-towards-an-ml-based-earth-system-model-oceans/

The era of artificial intelligence (AI) and machine learning (ML)-powered weather forecasting is here to stay.

But far from making traditional approaches such as reanalysis irrelevant, AI/ML weather models have instead brought reanalysis back to the spotlight, highlighting their importance as the “memory” of the Earth system and helping us understand both the present and the future.

But what exactly is reanalysis, and why does it matter for climate and weather science?

Find out in our Science Blog
➡️ https://www.ecmwf.int/en/about/media-centre/science-blog/2026/reanalysis-past-present-future-weather-climate