论文标题
基于神经网络的天气预报的合奏方法
Ensemble methods for neural network-based weather forecasts
论文作者
论文摘要
合奏天气预报通过计算合奏的扩展，可以将不确定性衡量。但是，生成具有良好散布关系的合奏并远非微不足道，并且已经探索了广泛的方法 - 主要是在数值天气预测模型的背景下。在这里，我们旨在将确定性的神经网络天气预测系统转变为合奏预测系统。我们测试了生成集合的四种方法：随机初始扰动，神经网络的重新训练，网络中随机辍学的使用以及创建具有单数矢量分解的初始扰动。后一种方法广泛用于数值天气预测模型中，但尚待在神经网络上进行测试。从这四种方法获得的合奏平均预测都超过了未受扰动的神经网络预测，而再培训方法得出最高的改善。但是，神经网络预测的技能在系统上低于最先进的数值天气预测模型。
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.
