Spatial Simulation and Prediction of Air Temperature Based on CNN-LSTM

Predicting the air temperature based on spatially accurate simulations is helpful to agricultural production, commercial activities, air transportation, water transportation, power supply, and national defense. Traditional prediction is not generally based on the time series obtained from multiple meteorological stations and from a spatial perspective. In this study, a deep convolution neural network with long short-term memory (CNN-LSTM) is constructed to extract the spatiotemporal features of temperature and the correlation between meteorological elements. The accuracies of the simulated and the predicted temperatures are spatially visualized by using the Kriging interpolation method. The accuracy of air temperature obtained from the CNN-LSTM was compared with those obtained from the CNN and the LSTM to verify its performance. The results show that there were prominent spatial variations in the temperature, with a latitudinal zonal structure in the southern Ningxia and a radial zonal structure in the northern Ningxia, China. The accuracies of the simulated and predicted temperature were high in areas with a small range of the annual temperature. The accuracies of the mean monthly temperature simulated by the CNN-LSTM in spring and autumn were higher than that in the summer. The predicted annual average temperature increased each year from 2020 to 2025. The CNN-LSTM had a higher accuracy of simulating and predicting the temperature as well as a better generalization ability than the CNN and LSTM.