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N. Levi

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Preprint Nov 2026

PARDIG: Global Precipitation Estimates Learned from Sparse Satellite Observations

Estimating precipitation at the global scale is vital for Earth system science and disaster response, yet observational coverage and measurement precision remain constrained. Existing solutions provide options that trade off desirable traits such as skill, latency, geographical coverage, temporal coverage, and spatial...

Guy Shalev, S. Rasp, B. Babenko et al. · 0 citations
#machine learning Preprint Sep 2026

WeatherNext 3: Increasing resolution and performance of global weather models with raw observations

Together, WeatherNext 3's capabilities move operational AI-based weather forecasting beyond emulating the traditionally distinct stages of data assimilation, forecasting and post-processing, which helps to further push the frontier of performance and granularity for global weather prediction.

S. Rasp, B. Babenko, Dominic Masters et al. · 3 citations

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