Interpretation of clearness day-ahead forecast errors using novel cloud classification

The formation and dissipation of clouds are one of the longest studied and yet least understood phenomenon in nature. This is crucial in atmospheric and climate science as clouds have a significant impact on radiative forcing. In numerical weather prediction, solar radiation forecasts have lower skill than other parameters as temperature forecasts despite recent progresses. This study aims at better understanding cloud situations over Europe and how solar radiation forecast errors are related to these situations. Therefore, an enhanced cloud class algorithm based on unsupervised Deep Learning and hierarchical clustering is introduced. By using the MODIS optical cloud thickness product, the algorithm is able to classify 14 different daily cloud situations which are applied on defined tile regions (approximately 70,000 km²) of Europe. These different classes differ in both optical cloud phase and the overall structure of the cloud shape. The usefulness of the cloud classes is illustrated by showing regional differences of cloud type frequencies over the last 20 years. To better understand solar radiation forecast errors, the cloud classes are assigned to ECMWF IFS clearness day-ahead forecast errors. We show that high-water content and mixed-cloud phase situations lead to highest absolute forecast errors for single sites. Summed up over an area, we observe an accumulation of forecast errors for mixed-cloud phase situations whereas for other cloud situations forecast errors are more likely to cancel each other out (e.g. broken high-water content clouds). This study is useful for researchers and practitioners to better understand situations of high solar radiation errors by using the developed cloud product.