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, here we introduce methods that guarantee statistical 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 data sets. Besides enabling faster and more accurate climate predictions through downscaling, we also show that our novel methodologies can improve super-resolution for satellite data and natural images data sets.

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。由于计算成本的限制，预测模型通常只能生成低分辨率的预测结果。统计降尺度方法（包括基于深度学习的超分辨率技术）可提供一种高效的上采样低分辨率数据的手段。然而，尽管这些模型在某些情况下能生成视觉上令人信服的结果，但在预测物理变量时常常违反守恒定律。为了确保物理量的守恒，本文引入了一系列方法，通过深度学习降尺度模型保证统计约束得到满足，同时提升其在传统指标上的性能。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与天气数据集上的适用性。通过降尺度，本文提出的新方法不仅能实现更快、更准确的气候预测，还能改善卫星数据及自然图像数据集的超分辨率效果。