Testing the Limit of Atmospheric Predictability with a Machine Learning Weather Model

• URL: http://arxiv.org/abs/2504.20238v1
• Date: Mon, 28 Apr 2025 20:18:57 GMT
• Title: Testing the Limit of Atmospheric Predictability with a Machine Learning Weather Model
• Authors: P. Trent Vonich, Gregory J. Hakim,
• Abstract summary: We optimize forecast initial conditions using gradient-based techniques for twice-daily forecasts spanning 2020.<n>This approach yields an average error reduction of 86% at 10 days, with skill lasting beyond 30 days.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Atmospheric predictability research has long held that the limit of skillful deterministic weather forecasts is about 14 days. We challenge this limit using GraphCast, a machine-learning weather model, by optimizing forecast initial conditions using gradient-based techniques for twice-daily forecasts spanning 2020. This approach yields an average error reduction of 86% at 10 days, with skill lasting beyond 30 days. Mean optimal initial-condition perturbations reveal large-scale, spatially coherent corrections to ERA5, primarily reflecting an intensification of the Hadley circulation. Forecasts using GraphCast-optimal initial conditions in the Pangu-Weather model achieve a 21% error reduction, peaking at 4 days, indicating that analysis corrections reflect a combination of both model bias and a reduction in analysis error. These results demonstrate that, given accurate initial conditions, skillful deterministic forecasts are consistently achievable far beyond two weeks, challenging long-standing assumptions about the limits of atmospheric predictability.

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