@Article{CiCP-31-1,
author = {Wenjia, Kong,  and Li, Haochen,  and Yu, Chen and Xia, Jiangjiang,  and Yanyan, Kang,  and Zhang, Pingwen},
title = {A Deep Spatio-Temporal Forecasting Model for Multi-Site Weather Prediction Post-Processing},
journal = {Communications in Computational Physics},
year = {2022},
volume = {31},
number = {1},
pages = {131--153},
abstract = {<p style="text-align: justify;">In this paper, we propose a deep spatio-temporal forecasting model (DeepSTF) for multi-site weather prediction post-processing by using both temporal and
spatial information. In our proposed framework, the spatio-temporal information is
modeled by a CNN (convolutional neural network) module and an encoder-decoder
structure with the attention mechanism. The novelty of our work lies in that our model
takes full account of temporal and spatial characteristics and obtain forecasts of multiple meteorological stations simultaneously by using the same framework. We apply
the DeepSTF model to short-term weather prediction at 226 meteorological stations in
Beijing. It significantly improves the short-term forecasts compared to other widely-used benchmark models including the Model Output Statistics method. In order to
evaluate the uncertainty of the model parameters, we estimate the confidence intervals by bootstrapping. The results show that the prediction accuracy of the DeepSTF
model has strong stability. Finally, we evaluate the impact of seasonal changes and topographical differences on the accuracy of the model predictions. The results indicate
that our proposed model has high prediction accuracy.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2020-0158},
url = {https://global-sci.com/article/79508/a-deep-spatio-temporal-forecasting-model-for-multi-site-weather-prediction-post-processing}
}