Spatiotemporal forecasting model based on hybrid convolution for local weather prediction post-processing

Xiang Li, Jie Xiang, Jiping Guan, Lifeng Zhang, Zenghui Cao, Jilu Xia

Frontiers in Earth Science · 27 September 2022

In one sentence. STCNet combines 2D and 1D convolutions to post-process NWP for station weather, outperforming a Seq2Seq RNN and yielding hourly forecasts from 3-hourly input.

Abstract

Future weather conditions can be obtained based on numerical weather prediction (NWP); however, NWP is unsatisfied with precise local weather prediction. In this study, we propose a spatiotemporal convolutional network (STCNet) based on spatiotemporal modeling for local weather prediction post-processing. To model the spatiotemporal information, we use a convolutional neural network and an interactive convolutional module, which use two-dimensional convolution for spatial feature extraction and one-dimensional convolution for time-series processing, respectively. We performed experiments at several stations, and the results show that our model considerably outperforms the traditional recurrent neural network–based Seq2Seq model while demonstrating the effectiveness of the fusion of observation and forecast data. By investigating the influences of seasonal changes and station differences, we conclude that the STCNet model has high prediction accuracy and stability. Finally, we completed the hour-by-hour local weather prediction using the 3-h forecast data and attained similar results to the 3-h local weather prediction that efficiently compensated for the temporal resolution of the forecast data. Thus, our model can enhance the spatial and temporal resolutions of forecast data and achieve remarkable local weather prediction.

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