Correlation Analysis between El Niño and Regional Water Vapor Characteristics Based on Intelligent Sensor and Walktrap Algorithm

Abstract

Water vapor is an important part of the atmospheric system and plays a key role in the global water cycle, energy balance, extreme weather, and long-term climate research. It is of great significance to fully grasp the spatial and temporal distribution characteristics of water vapor for the study of various climate models. The traditional water vapor detection technology mainly has the problems of low spatial-temporal resolution and poor capture of fine changes. In recent decades, the rapid development of intelligent sensor technology and Walktrap algorithm has made it a reliable means to obtain and analyze the characteristics of water vapor. However, the acquisition of atmospheric water vapor requires two key parameters: station pressure (PS) and weighted average temperature (TM). Since most traditional stations are mainly used for geodetic research, few are equipped with meteorological intelligent sensors. Therefore, based on the traditional meteorological stations, this paper improves the equipment of the traditional stations and endows the traditional stations with precise and sensitive intelligent sensor equipment to obtain the regional water vapor characteristic data of El Niño and a province in the east of China, combined with the advanced Walktrap algorithm, in order to use the above equipment and technology to realize the correlation analysis of El Niño and regional water vapor characteristics. The results show that through the water vapor information calculated by 12 coastal stations, the relationship between monthly average PWV and seawater surface temperature is studied, and the correlation degree between the relevant characteristics is as high as 94.6%.