Predictability Limit of the 2021 Pacific Northwest Heatwave from Deep-Learning Sensitivity Analysis

TL;DR

This paper demonstrates that deep-learning sensitivity analysis can significantly improve long-range weather forecast accuracy for the 2021 Pacific Northwest heatwave, revealing the potential predictability limits of such extreme events.

Contribution

It introduces a deep-learning based approach to estimate weather forecast sensitivity, surpassing traditional adjoint methods, and extends predictability estimates to about 23 days.

Findings

Over 90% reduction in 10-day forecast errors.

Forecast improvements extend to about 23 days.

Model error is not a major factor in initial perturbations.

Abstract

The traditional method for estimating weather forecast sensitivity to initial conditions uses adjoint models, which are limited to short lead times due to linearization around a control forecast. The advent of deep-learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions to minimize forecast errors. We apply this approach to forecasts of the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10-day forecast errors over the Pacific Northwest. Similar improvements are found for Pangu-Weather model forecasts initialized with the GraphCast-derived optimal, suggesting that model error is not an important part of the initial perturbations. Eliminating small scales from the initial perturbations also yields similar forecast improvements. Extending the length of the optimization window, we find forecast improvement to about 23 days, suggesting atmospheric predictability at the upper end of recent estimates.