Neural Networks for Postprocessing Ensemble Weather Forecasts
Neural Networks for Postprocessing Ensemble Weather Forecasts
复制标题
DOI：
10.1175/mwr-d-18-0187.1
复制
发表时间：
2018-11-01
影响因子：
3.2
通讯作者：
Lerch, Sebastian
中科院分区：
文献类型：
--
作者：
Rasp, Stephan;Lerch, Sebastian
集合天气预报需要对系统误差进行统计后处理，以获得可靠和准确的概率预报。传统上，这是通过分布回归模型来实现的，在该模型中，预测分布的参数是从训练期间估计的。我们提出了一种灵活的基于神经网络的方法，它可以结合任意预测变量和预测分布参数之间的非线性关系，这些关系是以数据驱动的方式自动学习的，而不需要预先指定的链接函数。在对德国地面站2米温度预报的案例研究中，神经网络方法的性能明显优于基准后处理方法，同时在计算上更容易负担得起。这一改进的关键部分是在嵌入的帮助下使用辅助预报变量和特定于站点的信息。此外，经过训练的神经网络可以用来洞察气象变量的重要性，从而挑战神经网络是无法解释的黑匣子的概念。我们的方法可以很容易地扩展到其他统计后处理和预测问题。我们预计，深度学习方面的最新进展，加上不断增加的模式和观测数据，将在未来十年改变数值天气预报的后处理。
Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring prespecified link functions. In a case study of 2-m temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark postprocessing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables, thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical postprocessing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the postprocessing of numerical weather forecasts in the coming decade.