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Found 7 papers, 3 papers with code
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling.
Deep Learning for Day Forecasts from Sparse Observations
The ability of neural models to make a prediction in less than a second once the data is available and to do so with very high temporal and spatial resolution, and the ability to learn directly from atmospheric observations, are just some of these models' unique advantages.
A Machine Learning Outlook: Post-processing of Global Medium-range Forecasts
Post-processing typically takes the outputs of a Numerical Weather Prediction (NWP) model and applies linear statistical techniques to produce improve localized forecasts, by including additional observations, or determining systematic errors at a finer scale.
Global Extreme Heat Forecasting Using Neural Weather Models
We find that training models to minimize custom losses tailored to emphasize extremes leads to significant skill improvements in the heat wave prediction task, compared to NWMs trained on the mean squared error loss.
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks
An emerging class of weather models based on neural networks represents a paradigm shift in weather forecasting: the models learn the required transformations from data instead of relying on hand-coded physics and are computationally efficient.
MetNet: A Neural Weather Model for Precipitation Forecasting
Weather forecasting is a long standing scientific challenge with direct social and economic impact.
Machine Learning for Precipitation Nowcasting from Radar Images
High-resolution nowcasting is an essential tool needed for effective adaptation to climate change, particularly for extreme weather.
