The impacts of climate change are felt by most critical systems, such as
infrastructure, ecological systems, and power-plants. However, contemporary
Earth System Models (ESM) are run at spatial resolutions too coarse for
assessing effects this localized. Local scale projections can be obtained using
statistical downscaling, a technique which uses historical climate observations
to learn a low-resolution to high-resolution mapping. Depending on statistical
modeling choices, downscaled projections have been shown to vary significantly
terms of accuracy and reliability. The spatio-temporal nature of the climate
system motivates the adaptation of super-resolution image processing techniques
to statistical downscaling. In our work, we present DeepSD, a generalized
stacked super resolution convolutional neural network (SRCNN) framework for
statistical downscaling of climate variables. DeepSD augments SRCNN with
multi-scale input channels to maximize predictability in statistical
downscaling. We provide a comparison with Bias Correction Spatial
Disaggregation as well as three Automated-Statistical Downscaling approaches in
downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km)
over the Continental United States. Furthermore, a framework using the NASA
Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM
models with multiple emission scenarios.

本文介绍 DeepSD 框架，一种利用超级分辨率图像处理技术进行气候统计降尺度的卷积神经网络模型，可以将原本粗糙的模拟数据提高预测的准确性和可靠性。作者使用 NASA 地球交换平台，将其应用于对超过 20 种不同情境下的全球气候变化进行计算。