用卫星数据训练神经气候模型，显著提升降水模拟精度。
Neural general circulation models optimized to predict satellite-based precipitation observations
- 基于可微分神经气候模型框架，直接以卫星降水观测为训练目标。
- 在2.8°分辨率下，降水分布、极端事件和日变化周期均优于现有模型。
- 适合气候模拟、极端天气研究及需要高精度降水预测的领域。
气候模型难以准确模拟降水，尤其在极端事件和日变化周期方面表现不佳。本文提出一种混合模型，直接基于卫星降水观测进行训练。该模型分辨率达2.8°，基于可微分神经气候模型（NeuralGCM）框架构建。与现有通用气候模型（GCM）、ERA5再分析数据以及全球云解析模型相比，该模型在降水模拟上表现出显著改进：偏差更小，降水分布更真实，极端事件表征更准确，日变化周期更精确。此外，其性能超越了欧洲中期天气预报中心（ECMWF）集合预报的中等水平降水预测。这一进展为当前气候的可靠模拟开辟了新路径，并展示了直接以观测数据训练可有效提升GCM性能。
原文摘要 · Abstract (English)
Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. Here, we present a hybrid model that is trained directly on satellite-based precipitation observations. Our model runs at 2.8$^\circ$ resolution and is built on the differentiable NeuralGCM framework. The model demonstrates significant improvements over existing general circulation models, the ERA5 reanalysis, and a global cloud-resolving model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle. Furthermore, it outperforms the mid-range precipitation forecast of the ECMWF ensemble. This advance paves the way for more reliable simulations of current climate and demonstrates how training on observations can be used to directly improve GCMs.
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