Comparison of AI Downscaling Methods on C3S Seasonal Forecasts for Early Warning System Development

Abstract

<p>The seasonal climate forecasts at the Copernicus Climate Change Service (C3S) [1] provide longer-term predictions with several variables and forecast systems, serving as an outlook of weather statistics several weeks to months ahead. Hydrological models can assess natural hazards at regional scales when given high-resolution inputs of kilometer scales. Thus, to use the seasonal forecast ensembles for regional damage estimation, it is essential to increase the spatial resolution of these large sets of climate variables. Furthermore, the high-resolution data are adjusted to be physically consistent with the driving model in weather processes. Thanks to the latest advances in artificial intelligence, downscaling to high-resolution local data has become feasible. This study focuses on the comparison of three AI downscaling methods: multiple linear regression [2], artificial neural networks [3], and support vector machines [4]. In AI downscaling, the gridded data is used for model training and hyperparameter tuning. First, climate variables, including temperature, wind speed, precipitation, and solar radiation, are downscaled from 1° to 1 km. The downscaling results are then evaluated using statistical indicators compared with the historical daily station observations. Finally, the best-performing AI downscaling method is implemented to develop an early warning system to detect future climate extreme risks and their impacts on diverse economic activities in Germany.</p><p>[1] Seasonal forecast daily and subdaily data on single levels, Copernicus Climate Change Services, (2018). DOI: 10.24381/cds.181d637e.<br>[2] J. Bedia, J. Baño-Medina, M. N. Legasa, M. Iturbide, R. Manzanas, S. Herrera, A. Casanueva, D. San-Martín, A. S. Cofiño, J. M. Gutiérrez, Statistical downscaling with the downscaleR package (v3.1.0): Contribution to the VALUE intercomparison experiment, Geosci. Model Dev. <strong>13</strong> (3), 1711-1735 (2020). DOI: 10.5194/gmd-13-1711-2020.<br>[3] K. Ahmed, S. Shahid, S. B. Haroon, X. J. Wang, Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan, J. Earth Syst. Sci. <strong>124</strong> (6), 1325-1341 (2015). DOI: 10.1007/s12040-015-0602-9.<br>[4] A. Goly, R. S. V. Teegavarapu, A. Mondal, Development and evaluation of statistical downscaling models for monthly precipitation <strong>18</strong> (18), 1-28 (2014). DOI: 10.1175/EI-D-14-0024.1.</p>