The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and, therefore, often generate coarse-resolution predictions. Statistical downscaling, including super-resolution methods from deep learning, can provide an efficient method of upsampling low-resolution data. However, despite achieving visually compelling results in some cases, such models frequently violate conservation laws when predicting physical variables. In order to conserve physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep learning downscaling model while also improving their performance according to traditional metrics. We compare different constraining approaches and demonstrate their applicability across different neural architectures as well as a variety of climate and weather datasets. Besides enabling faster and more accurate climate predictions, we also show that our novel methodologies can improve super-resolution for satellite data and standard datasets.

翻译：可靠的高分辨率气候和天气数据的可用性对于制定长期气候适应与减缓决策以及指导极端事件的快速响应至关重要。预报模型受限于计算成本，因此通常生成粗分辨率预测。统计降尺度方法，包括深度学习中的超分辨率技术，可以提供一种高效的低分辨率数据上采样方法。然而，尽管在某些情况下能够取得视觉上令人信服的结果，这类模型在预测物理变量时常常违反守恒定律。为了守恒物理量，我们开发了能够确保深度学习降尺度模型满足物理约束的方法，同时根据传统指标提升其性能。我们比较了不同的约束方法，并展示了它们在不同神经架构以及多种气候和天气数据集上的适用性。除了能够实现更快、更准确的气候预测外，我们的新方法还能改善卫星数据和标准数据集的超分辨率效果。