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AI4S2S

A high-level python package integrating expert knowledge and artificial intelligence to boost (sub) seasonal forecasting

image credit: Photo by USGS on Unsplash (https://unsplash.com/photos/eFbxYl9M_lc)

Society is vulnerable to weather extremes like heatwaves and droughts, and current operational forecast systems are typically only accurate up to 10 days ahead. New, massive climate datasets from satellites and climate models, combined with novel machine learning techniques provide great promise to push that forecast horizon further, potentially to several weeks or months ahead. To achieve this, we want to develop code that enables reproducible analyses according to best practices, which also provides insights into where the predictability is coming from.

AI4S2S builds an open-source python package that can efficiently run across different Big Climate Data platforms and that will include the latest advances in machine learning. We will actively involve experts worldwide to generate a sustainable, community-driven coding effort, via dedicated workshops and online outreach. AI4S2S has the potential to make a huge impact on research (enabling scientific breakthroughs), education (lowering technical barriers) and society (forecast-based risk reduction).

Participating organisations

Colorado State University
Environment & Sustainability
Environment & Sustainability
Netherlands eScience Center
University of Amsterdam
University of Oxford
Vrije Universiteit Amsterdam

Output

Team

DC
Dim Coumou
Lead Applicant
Vrije Universiteit Amsterdam
AW
Antje Weisheimer
EB
Elizabeth A. Barnes
Advisor
Colorado State University
Yang Liu
Yang Liu
Lead RSE
Netherlands eScience Center
IM
Iris Manola
Co-Applicant
Vrije Universiteit Amsterdam
JvI
Jannes van Ingen
Vrije Universiteit Amsterdam
SV
Sem Vijverberg
Fakhereh (Sarah) Alidoost
Fakhereh (Sarah) Alidoost
Niels  Drost
Niels Drost
Programme Manager
Netherlands eScience Center

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