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Promotional graphic showing a globe overlaid with a grid, alongside ECMWF branding and text announcing the IFS Cycle 50r1 and AIFS v2 upgrade.

Significant update to ECMWF’s key forecasting systems IFS and AIFS goes live

IFS Cycle 50r1 and AIFS v2 go live, bringing improved forecasting of severe rainfall, fully coupled atmosphere–ocean–sea-ice assimilation, and new AI-driven wave and snow forecasting capabilities.
Stylised graphic of rising forecast skill, with multiple coloured trend lines increasing across a grid overlaid on clouds and a circular digital interface.

2025 forecast evaluation met with positive feedback

ECMWF’s 2025 forecast evaluation highlights major gains in forecast skill, including the largest improvement in over a decade following upgrades to the Integrated Forecasting System.
Sun and snowflake icons on wooden blocks against red and blue backgrounds.

Register now for UEF2026

There is still time to register for the annual Using ECMWF’s Forecasts forum, bringing together users worldwide to explore extreme temperature forecasting and help shape ECMWF’s products.
ECMWF at EGU General Assembly 2026 banner with blue EGU flags, showing dates 3–8 May 2026.

ECMWF at the EGU General Assembly 2026

ECMWF staff are contributing a wide range of sessions at the European Geosciences Union (EGU) General Assembly 2026.
Cover graphic titled “European State of the Climate – Summary 2025,” showing stylised orange and green circles along a rising line on a teal background, with EU, Copernicus, ECMWF and WMO logos.

European State of the Climate 2025 report published

Rapid warming is driving record heat, glacier and snow loss, marine heatwaves, drought and wildfires.

Offshore wind farm at dusk with clouds above

A digital twin for offshore wind energy – the first of its kind

The DTWO project builds a first-of-its-kind digital twin for offshore wind, integrating weather, turbine and grid models to optimise siting, performance, resilience and grid integration.
Looking up at a forest canopy

The machine learning method advising on tree planting for water sustainability

A new study shows how machine learning can identify optimal tree-planting locations, reducing flood risk and water loss.
A promotional banner for ECMWF’s “Code for Earth” initiative. It reads: “Phase 1: 24.02.2026 – 09.04.2026. Call for participation.” Below, text says: “Browse through the 2026 challenges on GitHub. Submit your proposal by 09 April 2026.” The design features a dark blue background with green and teal digital-style graphics, including an oval highlight around the word “participation.” Logos for ECMWF, Copernicus, the European Union, Destination Earth, and the European Weather Cloud appear along the bottom.

Application deadline approaches for Code for Earth 2026

Applications are closing soon for Code for Earth 2026, offering innovators the chance to develop open‑source solutions that advance earth science, data visualisation, machine learning and climate resilience.
Upward view of large balloon with supporting cables and metal framework against a blue sky.

ECMWF and Météo-France celebrate collaboration on World Meteorological Day with the Ballon Generali de Paris

On World Meteorological Day, ECMWF, Météo-France and the Ballon Generali de Paris are joining forces to show how international collaboration helps protect lives from weather and climate extremes.
A digital graphic showing a glowing blue Earth with circuit‑like lines extending across the surface, symbolizing artificial intelligence. A circular icon with the letters “AI” appears over the globe. To the right, white text reads: “Artificial Intelligence and Earth Observation: from Innovation to Services.” Along the bottom are logos for the European Commission, the EU Programme, Copernicus, Destination Earth, and the hashtag #EUSpace. Background is a dark star‑filled space scene.

Workshop on AI and Earth observation highlights rapid progress in AI for weather and climate

Experts from across Europe gathered in Brussels and online to explore how artificial intelligence and machine learning are transforming Earth observation, weather prediction and environmental services.
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