Comparison of Al-Based Approaches for Statistical Downscaling of Surface Wind Fields in the North Atlantic

In this paper, we present the novel approach for the downscaling of of near-surface winds in the North Atlantic.

Surface wind is one of the most important physical fields in climate research. Accurate prediction of high-resolution near-surface winds has a wide variety of applications. Statistical downscaling methods obtain high-resolution information about the physical quantity distribution using available low-resolution data. They avoid high-resolution hydrodynamic simulations that are computationally expensive. Deep learning methods are one of the typical examples of the machine learning approaches to complex nonlinear functions approximating. In this work, we consider statistical downscaling of near-surface wind in the North Atlantic. For this, cubic interpolation, various architectures of convolutional networks, and generative adversarial network are applied. Based on the results obtained, the quality of these statistical downscaling methods is compared, and their advantages and disadvantages are identified.