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与天气数据的获取，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。受计算成本限制，预测模型通常仅能生成粗分辨率预测结果。统计降尺度方法（包括基于深度学习的超分辨率技术）可提供高效的低分辨率数据升采样手段。然而，此类模型虽能在某些情况下生成视觉上令人满意的结果，但在预测物理变量时常常违背守恒定律。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，同时提升其在传统指标下的性能。我们比较了不同约束方法，并论证了它们在多种神经架构及各类气候与天气数据集上的适用性。除实现更快、更准确的气候预测外，我们还展示了新方法可改进卫星数据及标准数据集的超分辨率效果。