基于GRU 神经网络的雷州半岛近海岸能见度 短临预报研究.

Atmospheric visibility changes in areas near the coast have complex, non-linear and local characteristics. With few near-coastal meteorological observation stations, it has been difficult for fine forecasting operations. In this paper, a multi-station GRU model, a single-station GRU model and a stepwise regression forecast model for 1 h valid, short-impending forecast of near-coastal visibility for the Leizhou Peninsula were constructed, tested and evaluated using a GRU neural network with the national basic meteorological station of Zhanjiang and its surrounding upstream and downstream observations. The results show that compared with the traditional stepwise regression method, the GRU neural network can better identify the spatiotemporal meteorological characteristics of upstream and downstream visibility changes, and the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) scores of the multi-station GRU model are significantly better than those of the multiple stepwise regression model. Model structure is crucial to the effectiveness of short-range visibility forecasting, and the introduction of upstream and downstream meteorological features into the visibility short-impending forecasting model can significantly improve the forecasting effectiveness. The multistation GRU model decreased MAE and RMSE by 36% and 29%, respectively, and improved R2 by 30%, compared with the single-station GRU model in a typical case, indicating that the multi-station GRU neural network model has obvious advantages for visibility forecasting and provides a new idea for refining short-impending forecast of near-coastal visibility. [ABSTRACT FROM AUTHOR]