Deep learning has been utilized for the statistical downscaling of climate data. Specifically, a two-dimensional (2D) convolutional neural network (CNN) has been successfully applied to precipitation estimation. This study implements a three-dimensional (3D) CNN to estimate watershed-scale daily precipitation from 3D atmospheric data and compares the results with those for a 2D CNN. The 2D CNN is extended along the time direction (3D-CNN-Time) and the vertical direction (3D-CNN-Vert). The precipitation estimates of these extended CNNs are compared with those of the 2D CNN in terms of the root-mean-square error (RMSE), Nash-Sutcliffe efficiency (NSE), and 99th percentile RMSE. It is found that both 3D-CNN-Time and 3D-CNN-Vert improve the model accuracy for precipitation estimation compared to the 2D CNN. 3D-CNN-Vert provided the best estimates during the training and test periods in terms of RMSE and NSE.
@article{arxiv.2112.06571,
title = {Extension of Convolutional Neural Network along Temporal and Vertical Directions for Precipitation Downscaling},
author = {Takeyoshi Nagasato and Kei Ishida and Ali Ercan and Tongbi Tu and Masato Kiyama and Motoki Amagasaki and Kazuki Yokoo},
journal= {arXiv preprint arXiv:2112.06571},
year = {2021}
}