In this paper, we propose a deep spatio-temporal forecasting model (DeepSTF) for multi-site weather prediction post-processing by using both temporal andspatial information. In our proposed framework, the spatio-temp...In this paper, we propose a deep spatio-temporal forecasting model (DeepSTF) for multi-site weather prediction post-processing by using both temporal andspatial information. In our proposed framework, the spatio-temporal information ismodeled by a CNN (convolutional neural network) module and an encoder-decoderstructure with the attention mechanism. The novelty of our work lies in that our modeltakes full account of temporal and spatial characteristics and obtain forecasts of multiple meteorological stations simultaneously by using the same framework. We applythe DeepSTF model to short-term weather prediction at 226 meteorological stations inBeijing. It significantly improves the short-term forecasts compared to other widelyused benchmark models including the Model Output Statistics method. In order toevaluate the uncertainty of the model parameters, we estimate the confidence intervals by bootstrapping. The results show that the prediction accuracy of the DeepSTFmodel has strong stability. Finally, we evaluate the impact of seasonal changes and topographical differences on the accuracy of the model predictions. The results indicatethat our proposed model has high prediction accuracy.展开更多
基金This work is supported by the National Key Research and Development Program of China(Grant Nos.2017YFC0209804 and 2018YFF0300104)Beijing Academy of Artificial Intelligence(BAAI)+1 种基金the National Natural Science Foundation of China(Grant No.11421101)the Open Research Fund of Shenzhen Research Institute of Big Data(Grant No.2019ORF01001).
文摘In this paper, we propose a deep spatio-temporal forecasting model (DeepSTF) for multi-site weather prediction post-processing by using both temporal andspatial information. In our proposed framework, the spatio-temporal information ismodeled by a CNN (convolutional neural network) module and an encoder-decoderstructure with the attention mechanism. The novelty of our work lies in that our modeltakes full account of temporal and spatial characteristics and obtain forecasts of multiple meteorological stations simultaneously by using the same framework. We applythe DeepSTF model to short-term weather prediction at 226 meteorological stations inBeijing. It significantly improves the short-term forecasts compared to other widelyused benchmark models including the Model Output Statistics method. In order toevaluate the uncertainty of the model parameters, we estimate the confidence intervals by bootstrapping. The results show that the prediction accuracy of the DeepSTFmodel has strong stability. Finally, we evaluate the impact of seasonal changes and topographical differences on the accuracy of the model predictions. The results indicatethat our proposed model has high prediction accuracy.
