Downscaling sea surface height and currents in coastal regions using convolutional neural network

Abstract

Downscaling is the process to obtain high-resolution data from low-resolution data. Recently statistical models using convolutional neural networks have gained popularity for fast downscaling of environmental fields, while their application to the coastal sea surface height and currents is lacking. This research aims to downscale sea surface height and depth-averaged current to a resolution of hundreds of meters in coastal regions with dynamic shorelines using convolutional neural networks. Hourly outputs over one year from a physical numerical model for a coastal region in the German Bight are used as the low-resolution input and high-resolution ground truth for the network. The results show that the network effectively reconstructs sea surface height and current in the region to a resolution of hundreds of meters with a scale factor of 16 or even 64, and accurately traces the moving sea surface and shorelines. The global mean absolute error and root mean square error for the sea surface height are found to be less than 0.03 m and 0.07 m, respectively, and for the current less than 0.03 m/s and 0.05 m/s, respectively. These values are around ten times smaller than those obtained from interpolation methods including nearest neighbor, bilinear and bicubic. The network also effectively replicates the distribution of high-resolution data. The errors in the reconstructed time average, 1st percentile and 99th percentile are significantly smaller than those from interpolation methods, especially for the current. These results highlight the ability of the network to downscale sea surface height and currents in regions with complex shorelines, and have implications for downscaling other coastal fields and shoreline tracking.