用于总云量预测的机器学习
Machine learning for total cloud cover prediction
摘要：
准确可靠的总云量（TCC）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。大多数气象中心都发布TCC的集合预报，然而，这些预报通常是未经校准的，并且表现出比其他天气变量的集合预报更差的预报技巧。因此，强烈需要某种形式的后处理来提高预测性能。由于TCC观测通常在离散尺度上报告，仅采用称为oktas的九个不同值，TCC集合预报的统计校准可以被认为是一个分类问题，其输出由oktas的概率给出。这是应用机器学习方法的经典领域。我们使用多层感知器（MLP）神经网络，梯度增强机（GBM）和随机森林（RF）方法的后处理的性能进行了研究。基于欧洲中期天气预报中心2002-2014年的全球TCC集合预报，我们将这些方法与比例比值逻辑斯谛回归（POLR）和多类逻辑斯谛回归（MLR）模型以及原始TCC集合预报进行了比较。我们进一步评估是否可以通过将集合预报的降水作为额外的预测，以提高预测技巧。与原始集合相比，所有的校正方法都显著提高了预报能力。RF模型在预测性能方面的提高最小，而MLP、POLR和GBM方法的性能最好。降水预报资料的使用导致预报技巧的进一步提高，除了很短的前置时间外，扩展的MLP模式显示出最好的整体性能。
英文摘要：
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.
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