论文标题
用于空间信息的合奏后处理的卷积自动编码器
Convolutional autoencoders for spatially-informed ensemble post-processing
论文作者
论文摘要
合奏天气预测通常显示出必须通过后处理来纠正的系统错误。即使是基于神经网络的最先进的后处理方法，也通常仅依赖于特定于位置的预测指标，这些预测因子需要对物理天气模型的空间预测字段插值到目标位置。但是，在此插值步骤中，输入字段内的大规模空间结构中包含的潜在有用的可预测性信息可能会丢失。因此，我们建议使用卷积自动编码器学习空间输入字段的紧凑表示形式，然后可以将其用于增强特定于位置的信息作为后处理模型的附加输入。包括此空间信息的好处在德国水面站的2-M温度预测的案例研究中得到了证明。
Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks often solely rely on location-specific predictors that require an interpolation of the physical weather model's spatial forecast fields to the target locations. However, potentially useful predictability information contained in large-scale spatial structures within the input fields is potentially lost in this interpolation step. Therefore, we propose the use of convolutional autoencoders to learn compact representations of spatial input fields which can then be used to augment location-specific information as additional inputs to post-processing models. The benefits of including this spatial information is demonstrated in a case study of 2-m temperature forecasts at surface stations in Germany.
