← 研踪 PaperTrailarXiv:2611.05592 · v2
Distinguishing Initial-Condition and Model Error Using Machine-Learning Weather Models
首次提交 2026-11-04 04:26
版本更新 2026-11-05 15:06
雷达观测 2026-11-06 12:09
摘要
Differentiable weather models allow atmospheric initial conditions to be directly optimized to shadow a known trajectory, which we take from a reanalysis dataset. Here, we extend this optimization to include the forecast model, GraphCast, using low-rank adaptation (LoRA). We compare model optimization with initial-condition (IC) optimization, and then joint optimization, where both are tuned simultaneously. When compared against a control forecast initialized with reanalysis data, LoRA and IC optimization individually produce similar improvements. We find similar results when IC and LoRA are jointly optimized across a spectrum of solutions where a tuning coefficient smoothly shifts the weight of the joint optimization from all-LoRA to all-IC; forecast improvement is not unique to the relative contributions of model and IC corrections. To better discriminate the source of error, we introduce a second model, Pangu-Weather, as a supervisor that gates IC updates during joint optimization. This gated optimization is the same as before, except that IC updates at each epoch are accepted only when they improve the forecasts of both models; otherwise, only the LoRA update is accepted. Results show that this cross-model constraint substantially improves agreement between the two models in their response to the same optimized ICs, narrowing the gap in mean forecast error reduction from 64% to 10%. Moreover, when used together, the LoRA and IC corrections from the gated optimization yield GraphCast forecast improvements similar to those from the ungated optimization. These results imply that model error that is encoded in the ungated ICs is shifted to the LoRA corrections via gating, yielding IC corrections that may provide better analysis estimates.
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v2 · 2026-11-05 15:06 · 最新观测版本
Distinguishing Initial-Condition and Model Error Using Machine-Learning Weather Models
Differentiable weather models allow atmospheric initial conditions to be directly optimized to shadow a known trajectory, which we take from a reanalysis dataset. Here, we extend this optimization to include the forecast model, GraphCast, using low-rank adaptation (LoRA). We compare model optimization with initial-condition (IC) optimization, and then joint optimization, where both are tuned simultaneously. When compared against a control forecast initialized with reanalysis data, LoRA and IC optimization individually produce similar improvements. We find similar results when IC and LoRA are jointly optimized across a spectrum of solutions where a tuning coefficient smoothly shifts the weight of the joint optimization from all-LoRA to all-IC; forecast improvement is not unique to the relative contributions of model and IC corrections. To better discriminate the source of error, we introduce a second model, Pangu-Weather, as a supervisor that gates IC updates during joint optimization. This gated optimization is the same as before, except that IC updates at each epoch are accepted only when they improve the forecasts of both models; otherwise, only the LoRA update is accepted. Results show that this cross-model constraint substantially improves agreement between the two models in their response to the same optimized ICs, narrowing the gap in mean forecast error reduction from 64% to 10%. Moreover, when used together, the LoRA and IC corrections from the gated optimization yield GraphCast forecast improvements similar to those from the ungated optimization. These results imply that model error that is encoded in the ungated ICs is shifted to the LoRA corrections via gating, yielding IC corrections that may provide better analysis estimates.
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Distinguishing Initial-Condition and Model Error Using Machine-Learning Weather Models
Gated and ungated optimizations evaluated in GraphCast and Pangu-Weather. The first panel (upper-left) shows the mean over the 36 dates; remaining panels show first-of-month 12 UTC optimization descents from January-November 2017. The y-axis metric is the summed day 1-10 Z500 RMSE verified against ERA5, expressed as percent change from each model's date-matched control. The gated arm optimizes the initial conditions jointly with a LoRA weight correction; its GraphCast curve uses the initial conditions alone (LoRA removed), the same state Pangu receives. Stars and diamonds mark the lowest Pangu errors in the 5- and 10-day windows, respectively. Percentages above each panel show the best Pangu improvement relative to control for gated (green) and ungated (red) optimization. Ungated outperforms gated on half of dates when stopped early. Gating does not guarantee a better Pangu performance, but it does produce more comparable GraphCast and Pangu-Weather improvements on all 36 cases.
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