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A Dynamic Inversion Model of the Beijing Local Climate

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  • 1 College of Geography Science, Nanjing Normal University, Nanjing, and College of Sciences, Northwest Agriculture and Forestry University, Yangling, China
  • | 2 College of Geography Science, Nanjing Normal University, and Jiangsu Key Laboratory of Environmental Change and Ecological Construction, Nanjing, China
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Abstract

A nonautonomous dynamic inverse model based on the autonomous dynamic inverse model is presented in this paper. By introducing the extrinsic cycle driving force and the surrounding environment driving force, the Beijing local climatic model is established via the nonautonomous dynamic inverse method. The model not only has a certain ability to predict but can relatively accurately reveal the inherent dynamic mechanisms among the monthly average atmospheric pressure, temperature, and precipitation. The model also applies to describing the dynamic characteristics of the departure from the annual cycle. The nonautonomous inversion dynamic model has broad application potential, which provides a new method for understanding and predicting local climate.

Corresponding author address: Dr. Zhenshan Lin, College of Geography Science, Nanjing Normal University, Wenyuan Road, Yadongxincheng District, Nanjing 210097, China. E-mail: linzhenshan@njnu.edu.cn

Abstract

A nonautonomous dynamic inverse model based on the autonomous dynamic inverse model is presented in this paper. By introducing the extrinsic cycle driving force and the surrounding environment driving force, the Beijing local climatic model is established via the nonautonomous dynamic inverse method. The model not only has a certain ability to predict but can relatively accurately reveal the inherent dynamic mechanisms among the monthly average atmospheric pressure, temperature, and precipitation. The model also applies to describing the dynamic characteristics of the departure from the annual cycle. The nonautonomous inversion dynamic model has broad application potential, which provides a new method for understanding and predicting local climate.

Corresponding author address: Dr. Zhenshan Lin, College of Geography Science, Nanjing Normal University, Wenyuan Road, Yadongxincheng District, Nanjing 210097, China. E-mail: linzhenshan@njnu.edu.cn
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