Predicting Weather Uncertainty with Deep Convnets

TL;DR

This paper demonstrates that deep convolutional neural networks, including modified 3D U-Net architectures, can effectively estimate weather forecast uncertainty from limited simulation data, reducing computational costs and aiding real-time predictions.

Contribution

It introduces a neural network approach for weather uncertainty estimation using fewer simulations, enhancing efficiency over traditional ensemble methods.

Findings

Neural networks can predict forecast spread with less computational effort.

Modified 3D U-Net architectures improve uncertainty estimation accuracy.

Incorporating temporal data enhances model performance.

Abstract

Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such computationally intensive simulations must be run. We show that deep neural networks can be used on a small set of numerical weather simulations to estimate the spread of a weather forecast, significantly reducing computational cost. To train the system, we both modify the 3D U-Net architecture and explore models that incorporate temporal data. Our models serve as a starting point to improve uncertainty quantification in current real-time weather forecasting systems, which is vital for predicting extreme events.