RT @MercatorOcean: Join #OP24 Session 5.1 on #MachineLearning in #OceanScience
🔹 Biogeochemical forecasting
🔹 Neural networks for seasonal predictions
🔹 Deep learning for ocean currents
🔹 Data assimilation techniques
To advance ocean modeling & prediction with us :
👉https://t.co/wxLxu8xwsS https://t.co/e1vQkbkzTn

Copernicus Marine

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New #AIFS blog: Forecast performance is multifaceted. Zied Ben Bouallègue and the AIFS team discuss how accuracy and activity can be ‘competing’ attributes for #MachineLearning-based deterministic forecasts.
➡️ https://t.co/rdFGxsRQNH https://t.co/tSkFsb8Ce2

ECMWF

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New #AIFS blog: we highlight that the first AIFS model weights are now available and explain how you can use them yourself ➡️ https://t.co/ECMn50kFSY
#MachineLearning https://t.co/BKvwtZlBQT

ECMWF

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Our news highlights from 2024 include a major upgrade of the #IntegratedForecastingSystem; initiatives to drive forward weather science, including #MachineLearning; and news from the EU-funded services implemented by ECMWF.

For details ➡️ https://t.co/0RDq6ah6Xv https://t.co/OcmIeq21jO

ECMWF

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In a new Data-in-Action story from NASA #PODAAC, “Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics”, read how #machinelearning has been applied to create a new higher resolution dataset for examining ocean mesoscale eddies.
https://t.co/4kFr7kZcR2 https://t.co/csxcX0QOum

NASAEarthdata

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New #AIFS blog: #MachineLearning (ML) for #weather and climate has continued to evolve at breathtaking pace.
Matthew Chantry takes us on a tour of the year past and gives some previews of the year to come.
➡️ https://t.co/BP9s1NTdQv https://t.co/Zzba0zc8gF

ECMWF

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If you are at #AMS2025, make sure to attend today’s presentation by our Director-General, @FlorenceRabier.

At 17:00 CST, she will talk about the future of ECMWF and our use of #MachineLearning.

More information
➡️https://t.co/YfTNjAPuku https://t.co/0u0pTJbP1I

ECMWF

Climate Change Science on Telegram by @ClimateChangeScience
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Our new 10-year Strategy sets out anticipated progress and direction for the whole of ECMWF. Find out about its main points, including an emphasis on collaboration and the growing role of #MachineLearning in weather prediction ➡️ https://t.co/1aSFKVy1mC https://t.co/sbHdJnXa3k

ECMWF

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🆕 PIK-led research work in @ClimateActionSN generates the map of research on #ClimatePolicy from 85,000 individual studies. It informs existing gaps in knowledge and facilitates the summarisation of the state of knowledge for governments. #MachineLearning
https://t.co/io7sqADo6A https://t.co/KzffkGdOOX

Potsdam Institute for Climate Impact Research PIK

Climate Change Science on Telegram by @ClimateChangeScience
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This week we hosted @environmentca's Cécile Siewe & Heather Aucoin to discuss recent updates & plans in research, high-performance computing and #MachineLearning. Our thanks also to Charlie Hébert-Pinard for her contribution to ECMWF-ECCC collaboration while working on Anemoi. https://t.co/HJgyNwO3bM

ECMWF

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Interested in next-gen prediction, data-driven modelling, and integrating #machinelearning with physical #science? Join us for the 8th WMO #Symposium on #DataAssimilation in Nanjing, China (31 Aug–4 Sep 2026) and submit your #abstract by 28 Feb. Read more:
https://t.co/n4JFhggzdF https://t.co/iKzSnpIrTr

World Meteorological Organization

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