WP-MIP: An Artificial Intelligence, Hybrid and Physically Based Model Intercomparison Project for Weather Prediction
Abstract
Rapid progress in the field of machine-learning for weather prediction has led to the emergence of algorithms whose forecasting skill can exceed that of traditional physically based models. This development represents an opportunity to improve the quality of forecasting services provided by operational centers, particularly given the speed at which machine-learning based models generate predictions. Despite the clear promise of these systems, questions remain about the ability of the current generation of machine-learning models to generate physically consistent predictions of the full suite of required forecast fields under all conditions. Answering these questions will require careful comparisons between the well-understood physically based models, current state-of-the-art machine-learning models, and the hybrid models that combine elements of these two archetypes. The Weather Prediction Model Intercomparison Project (WP-MIP) is a World Meteorological Organization-supported initiative whose initial goal is to create a centralized database of physically based, machine-learning and hybrid model forecasts to enable a distributed assessment and evaluation effort. The first instance of WP-MIP focuses on global deterministic predictions using both center-specific and common initializations to facilitate sensitivity studies. Forecasts contributed by institutions across six continents will be used to develop AI-ready verification techniques that highlight the strengths and weaknesses of each class of prediction system, with the goal of establishing best-practice guidance to model developers and national weather centers. The broad engagement of the operational and forecast-evaluation communities in WP-MIP will ensure that the project results are highly relevant to the development and deployment of next-generation weather prediction systems.
Keywords
Cite
@article{arxiv.2604.16643,
title = {WP-MIP: An Artificial Intelligence, Hybrid and Physically Based Model Intercomparison Project for Weather Prediction},
author = {Ron McTaggart-Cowan and Linus Magnusson and Inna Polichtchouk and Duncan Ackerley and Martin Koehler and Barbara Casati and Jan-Huey Chen and Debra Hudson and Masashi Ujiie and Nurizana Amir Aziz and Massimo Bonavita and Zied Ben Bouallegue and Catherine de Burgh-Day and Stephane Chamberland and Kyounngmi Cho and Caio A. S. Coelho and Rostislav Fadeev and Manuel Fuentes and Jorge L. Garcia Franco and Claude Gilbert and Bruno S. Guimaraes and Chris Harris and Michelle Harrold and Syed Husain and Molly James and Alex Kaltenbaugh and Marta Koch and Paulo Y. Kubota and Eun-Hee Lee and Chen Li and Wei Li and Weiwei Li and Nicholas Loveday and Chrstian Lussana and Zubiar Maalick and Mohau J. Mateyisi and Amy McGovern and Koos van der Merwe and Joel Miller and Marion Mittermaier and Richard Mladek and Kathryn Newman and Andre L. O. Neves and John Pill and Roland Potthast and Maheswar Pradhan and Subhrajit Rath and David S. Richardson and Leo Separovic and Michelle Simoes Reboita and Gregor Skok and Ankur Srivastava and Mikhail Tolstykh and Zhuo Wang and Beth J. Woodham and Fanglin Yang and Radomir Zaripov and Gan Zhang and Hongyan Zhu},
journal= {arXiv preprint arXiv:2604.16643},
year = {2026}
}