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.
翻译:对云层总覆盖率(TCC)的准确和可靠预测对于天文学、能源需求和生产或农业等许多领域至关重要,但大多数气象中心对TCC的全套预测大多没有校准,预测技能也比其他天气变数的全套预测要差,因此,非常需要某种形式的后处理来改进预测性能。由于TCC观测通常在离散规模上报告,仅使用9种不同的数值,称为Aktas,TCC的整流预测的统计校准可被视为一个分类问题,其产出由天花板的概率提供。这是一个经典领域,采用机器学习方法。我们利用多层透视网络、梯度加速机器和随机森林方法来调查后处理的绩效。在欧洲中期天气预报中心全球总总和多层预测中,我们将这些方法与准确的准确度准确度调整和多级后回归(MLRR)方法相比较。在模型中,我们用最精确的预测方法进一步显示最佳的准确性能,同时通过原始的预测,在原始的预测中进一步的预测中,我们用最精确的预测显示前的预变的预测,以进一步的精度。