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：高分辨率且可靠的气候与天气数据的可获得性，对于为气候适应与减缓的长期决策提供依据，以及指导对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的预测。统计降尺度方法，包括基于深度学习（如超分辨率技术），能够提供一种高效的低分辨率数据升采样方法。然而，尽管此类模型在某些情况下能产生视觉上令人信服的结果，但在预测物理变量时，它们常常违反守恒定律。为了守恒物理量，我们开发了能够保证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，并证明了它们在不同神经网络架构及多种气候与天气数据集上的适用性。除了能实现更快、更准确的气候预测外，我们还表明，我们的新颖方法可改进卫星数据及标准数据集的超分辨率效果。