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：高质量、高分辨率的气候与天气数据对于制定长期气候适应与缓解决策，以及指导对极端事件的快速响应至关重要。然而，预报模型受限于计算成本，通常只能生成粗分辨率预测。统计降尺度方法（包括深度学习中的超分辨率技术）可提供一种高效的低分辨率数据上采样途径。然而，即便在某些情况下能获得视觉上令人满意的结果，此类模型在预测物理变量时常违反守恒定律。为守恒物理量，我们开发了能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模型性能。我们比较了不同约束方法，并展示了它们在多种神经架构及气候变化与天气数据集上的适用性。除实现更快速、更准确的气候预测外，我们亦证明这些新方法可提升卫星数据与标准数据集的超分辨率效果。