  Accurate and reliable forecasting of total cloud cover (TCC) is vital for
many areas such as astronomy, energy demand and production, or agriculture.
Most meteorological centres issue ensemble forecasts of TCC, however, these
forecasts are often uncalibrated and exhibit worse forecast skill than ensemble
forecasts of other weather variables. Hence, some form of post-processing is
strongly required to improve predictive performance. As TCC observations are
usually reported on a discrete scale taking just nine different values called
oktas, statistical calibration of TCC ensemble forecasts can be considered a
classification problem with outputs given by the probabilities of the oktas.
This is a classical area where machine learning methods are applied. We
investigate the performance of post-processing using multilayer perceptron
(MLP) neural networks, gradient boosting machines (GBM) and random forest (RF)
methods. Based on the European Centre for Medium-Range Weather Forecasts global
TCC ensemble forecasts for 2002-2014 we compare these approaches with the
proportional odds logistic regression (POLR) and multiclass logistic regression
(MLR) models, as well as the raw TCC ensemble forecasts. We further assess
whether improvements in forecast skill can be obtained by incorporating
ensemble forecasts of precipitation as additional predictor. Compared to the
raw ensemble, all calibration methods result in a significant improvement in
forecast skill. RF models provide the smallest increase in predictive
performance, while MLP, POLR and GBM approaches perform best. The use of
precipitation forecast data leads to further improvements in forecast skill and
except for very short lead times the extended MLP model shows the best overall
performance.
