GNSS ZTD Spatial-temporal Variation Analysis And Its Application In ENSO Events Monitoring

Frequent meteorological disasters such as floods,droughts,snowstorms,and heavy haze have caused serious hazards to human production and life.The monitoring and early warning accuracy of meteorological disasters need to be further improved.Precipitable Water Vapor,one of the most active components of the atmosphere,plays a crucial role in indicating extreme weather events.The El Ni(?)o-Southern Oscillation(ENSO)event indirectly causes droughts and floods in mainland China by affecting the movement of Western Pacific Subtropical High and the PWV transmission of the East Asian monsoon.It has important meaning to monitor the impact mechanism of ENSO events on China’s climate by using the temporal-spatial variation characteristics of PWV.This study applies GNSS meteorology to the field of extreme climate event monitoring and early warning and provides technical references for relevant meteorological monitoring departments and disaster prevention and control governance departments.This study explored the spatial distribution characteristics of GNSS ZTD in mainland China by the empirical orthogonal function analysis method.It was found that its regional regularity was more consistent with the distribution of the five major climate types.The author analyzed the time-frequency variation characteristics of GNSS ZTD time series by fast Fourier transform and wavelet transform.It was found that the time-series variation of GNSS ZTD is mainly driven by annual cycle,semi-annual cycle,seasonal cycle,many obvious variation cycles within 1～9 months,and daily and semi-diurnal waves.The mainland of China is divided into five regions based on the five climate types.The response of the GNSS ZTD time series anomaly and GNSS ZTD frequency domain oscillation characteristics in different regions to different types of ENSO events are explored.And the response thresholds of the GNSS ZTD anomaly in different regions to ENSO events are quantified.Moreover,this study proposed an optimized LSTME model,which applies the GNSS ZTD time-frequency reconstruction term that responds to ENSO events to the prediction of ENSO events.The above method overcomes the problems of existing studies such as restricted area and insufficient quantitative analysis and achieves higher accuracy and longer timeliness prediction of ENSO events.The main conclusions of this study are as follows.(1)The safety thresholds of the GNSS ZTD anomaly response to ENSO events in each region are: tropical monsoon region(-1.12,1.92);subtropical monsoon region(-1.12,1.61);temperate monsoon region(-1.19,1.62);temperate continental region(-1.26,1.64);plateau mountain region(-1.22,1.72).When the multivariate ENSO index(MEI)exceeds this safety threshold,the GNSS ZTD anomaly in China is mainly affected by ENSO events.(2)The occurrence of ENSO events has an impact on the period amplitude of GNSS ZTD in China,which will lead to a decrease in the amplitude of the 9-month significant variation cycle of GNSS ZTD and an increase in the amplitude of the significant variation cycle within 0.8～3 months.It also has a certain effect on the small-scale GNSS ZTD periods(diurnal and semi-diurnal waves),but the amplitude change has no obvious regularity.(3)The LSTME model is constructed by applying the GNSS ZTD in mainland China as an auxiliary variable for the prediction of ENSO events.It effectively improves the prediction accuracy compared with the traditional LSTM with RBF models and achieves prediction timeliness of 12 to 24 months.The model has high accuracy and strong practicability.