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Modern weather forecast models perform uncertainty quantification usingensemble prediction systems, which collect nonparametric statistics based onmultiple perturbed simulations. To provide accurate estimation, dozens of suchcomputationally intensive simulations must be run. We show that deep neuralnetworks can be used on a small set of numerical weather simulations toestimate the spread of a weather forecast, significantly reducing computationalcost. To train the system, we both modify the 3D U-Net architecture and exploremodels that incorporate temporal data. Our models serve as a starting point toimprove uncertainty quantification in current real-time weather forecastingsystems, which is vital for predicting extreme events.

Predicting Weather Uncertainty with Deep Convnets