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Global warming is inevitably the cause of local climate change, which will have a profound impact on regional ecology, especially in the desertified steppe and steppefied desert transition zones with fragile ecological environments. In order to investigate the change trends of precipitation, temperature and wind speed for effectively realizing the restoration and protection of desert ecosystems, a combination forecasting strategy including the data pre-processing technique, sub-models selection and parameter optimization was proposed and three numerical simulation experiments based on the combination model with the weights optimized by the particle swarm optimization algorithm were designed to forecast the precipitation, temperature and wind speed in the southeastern margin of the Tengger Desert in China. Numerical results showed that the proposed combination prediction method has higher forecasting accuracy and better robustness than single neural network models and hybrid models. The proposed method is beneficial to analyze climate change in arid regions.

A Combination Forecasting Strategy for Precipitation, Temperature and Wind Speed in the Southeastern Margin of the Tengger Desert