提出统一框架，让模型同时完成气象预报与数据融合，长期稳定且覆盖从日到十年的气候变化。
Generative assimilation and prediction for weather and climate
- 用生成式模型统一处理气象数据同化与预测，联合学习大气状态的概率分布。
- 在季节预测和千年气候模拟中表现稳定，能再现日到十年尺度的气候变率。
- 适合需要长期气候模拟或高精度天气预报的研究者，尤其关注系统性误差控制。
机器学习模型在两周内天气预测上已超越传统物理模型，但现有方法多仅关注预测，缺乏必要数据同化，且长时滚动预测存在误差累积，难以用于季节或气候预测。本文提出生成式同化与预测（GAP）框架，通过学习在观测、预报及外部强迫约束下大气状态的概率分布，实现气象与气候相关任务的统一建模，包括数据同化、无缝预测与气候模拟。GAP在多种任务中表现优异：可媲美最先进集合同化与概率性天气预报，实现稳定的千年级气候模拟，并复现从每日到十年尺度的气候变率。
原文摘要 · Abstract (English)
Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions or climate projections. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP is competitive with state-of-the-art ensemble assimilation, probabilistic weather forecast and seasonal prediction, yields stable millennial simulations, and reproduces climate variability from daily to decadal time scales.
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