Weather type reconstruction using machine learning approaches

Abstract

Weather types are used to characterise large-scale synoptic weather patterns over a region. Long-standing records of weather types hold important information about day-to-day variability and changes in atmospheric circulation and the associated effects on the surface. However, most weather type reconstructions are restricted in their temporal extent and suffer from methodological limitations. In our study, we assess various machine learning approaches for station-based weather type reconstruction over Europe based on the nine-class cluster analysis of principal components (CAP9) weather type classification. With a common feedforward neural network performing best in this model comparison, we reconstruct a daily CAP9 weather type series back to 1728. This new reconstruction constitutes the longest daily weather type series available. Detailed validation shows considerably better performance compared to previous statistical approaches and good agreement with the reference series for various climatological analyses. Our approach may serve as a guide for other weather type classifications.