Statistical downscaling of the 2m temperature with a generative adversarial network (GAN)

Abstract

<p>Despite continuous improvements in numerical atmospheric models and the ever-growing computational resources, global weather forecast and climate models still operate with grid spacings between 10 and 50 km. While a further increase in spatial resolution is computationally expensive and introduces new challenges in the design of these models (e.g. gray-zone parameterizations), statistical downscaling models can overcome the mentioned issues. Inspired by the success of super-resolution applications in computer vision, deep neural networks have recently been recognized as an appealing approach for statistical downscaling of meteorological fields.</p><p>In this study, we apply a generative adversarial network (GAN) to downscale the 2m temperature over Central Europe where complex terrain introduces a high degree of spatial variability. GANs are considered superior to purely convolutional networks since the model is encouraged to generate data whose statistical properties are similar to real data. Here, the generator consists of an u-shaped encoder-decoder network which is capable of extracting features on various spatial scales. Instead of pursuing a pure super-resolution approach where coarsened data is mapped back to high resolution data, we choose a more realistic, but also more challenging test suite. Coarsened ERA5 reanalysis data, originally provided on a 0.3°-grid, is used to downscale the 2m temperature onto a 0.1°-grid with the help of short-range forecasts from the operational Integrated Forecasting System (IFS) model. Thus, our GAN model does not only have to reconstruct finer spatial features, but must also correct for model biases.  <br>The downscaled 2m temperature is evaluated in terms of several evaluation metrics measuring the error on grid point-level as well as the quality in terms of spatial variability and the produced probability function. We also investigate the importance of static and dynamic predictors such as the surface elevation and the temperature on different pressure levels, respectively. Our results motivate further development of deep neural networks for statistical downscaling of meteorological fields. This includes downscaling of other, inherently uncertain variables such as precipitation, operations on spatial resolutions at kilometer-scale and ultimately targets an operational application on output data from global NWP models.</p>