Ensemble weather forecasts enable a measure of uncertainty to be attached to each forecast, by computing the ensemble's spread. However, generating an ensemble with a good spread‐error relationship is far from trivial, and a wide range of approaches to achieve this have been explored—chiefly in the context of numerical weather prediction models. Here, we aim to transform a deterministic neural network weather forecasting system into an ensemble forecasting system. We test four methods to generate the ensemble: random initial perturbations, retraining of the neural network, use of random dropout in the network, and the creation of initial perturbations with singular vector decomposition. The latter method is widely used in numerical weather prediction models, but is yet to be tested on neural networks. The ensemble mean forecasts obtained from these four approaches all beat the unperturbed neural network forecasts, with the retraining method yielding the highest improvement. However, the skill of the neural network forecasts is systematically lower than that of state‐of‐the‐art numerical weather prediction models.

中文翻译：

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基于神经网络的天气预报的集成方法

集合天气预报通过计算集合的传播范围，使不确定性的度量可以附加到每个天气预报中。但是，生成具有良好分布误差关系的集合并不是一件容易的事，并且已经探索了多种方法来实现这一目标，这主要是在数值天气预报模型的背景下进行的。在这里，我们旨在将确定性神经网络天气预报系统转变为整体预报系统。我们测试了四种生成集合的方法：随机初始扰动，神经网络的再训练，网络中随机缺失的使用以及奇异矢量分解的初始扰动的创建。后一种方法广泛用于数值天气预报模型中，但尚未在神经网络上进行测试。从这四种方法获得的整体均值预测都击败了不受干扰的神经网络预测，其中再训练方法产生了最大的改进。但是，神经网络预报的技巧在系统上低于最新的数值天气预报模型。