论文标题
使用卷积神经网络对风速预测的统计后处理
Statistical post-processing of wind speed forecasts using convolutional neural networks
论文作者
论文摘要
当前用于概率天气预测的后期统计后处理方法无法使用数值天气预测（NWP）模型中的完整空间模式。在本文中，我们使用卷积神经网络（CNN）结合了空间风速信息，并根据KNMI的确定性Harmonie-Arome NWP模型，在荷兰持续48小时的概率风速预测。与完全连接的神经网络和瓦解回归森林相比，来自CNN的概率预测显示为中等风速和更高风速的Brier技能得分，并且具有更好的连续排名概率评分（CRP）和对数得分。作为次要结果，我们使用了3种不同的密度估计方法（量化软键（QS），内核混合网络，并拟合截断的正态分布）进行了比较CNN，并发现基于QS方法的概率预测是最好的。
Current statistical post-processing methods for probabilistic weather forecasting are not capable of using full spatial patterns from the numerical weather prediction (NWP) model. In this paper we incorporate spatial wind speed information by using convolutional neural networks (CNNs) and obtain probabilistic wind speed forecasts in the Netherlands for 48 hours ahead, based on KNMI's deterministic Harmonie-Arome NWP model. The probabilistic forecasts from the CNNs are shown to have higher Brier skill scores for medium to higher wind speeds, as well as a better continuous ranked probability score (CRPS) and logarithmic score, than the forecasts from fully connected neural networks and quantile regression forests. As a secondary result, we have compared the CNNs using 3 different density estimation methods (quantized softmax (QS), kernel mixture networks, and fitting a truncated normal distribution), and found the probabilistic forecasts based on the QS method to be best.
