气候学
全球导航卫星系统应用
环境科学
异常(物理)
多元ENSO指数
对流层
降水
季风
天顶
气象学
拉尼娜现象
地质学
卫星
厄尔尼诺南方涛动
大地测量学
地理
物理
凝聚态物理
工程类
航空航天工程
作者
Tengli Yu,Ershen Wang,Shuanggen Jin,Yong Wang,Jing Huang,Xiao Liu,Zehong Wei
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-17
被引量:1
标识
DOI:10.1109/tgrs.2023.3251375
摘要
The El Niño-Southern Oscillation (ENSO) event often causes natural disasters in mainland China. Existing quantitative analysis of ENSO events effects on climate change in mainland China is insufficient. The monthly scale prediction effectiveness of ENSO events is still low. Global Navigation Satellite System (GNSS) can estimate zenith tropospheric delay (ZTD) with high accuracy, which can study ZTD responses to ENSO and improve the prediction accuracy of ENSO events. This study quantitatively analyzed the response patterns of GNSS ZTD time-frequency variation to ENSO events in mainland China. The monthly multivariate ENSO index (MEI) thresholds for GNSS ZTD anomaly response to ENSO events are (-1.12,1.92) for the tropical monsoon zone, (-1.12,1.61) for the subtropical monsoon zone, (-1.19,1.62) for the temperate monsoon zone, (-1.26,1.64) for the temperate continental zone, and (-1.22,1.72) for the mountain plateau zone. The ENSO event causes the amplitude of the 9-month variation period to decrease and the amplitude of the 0.8–3-month period to increase for the GNSS ZTD in mainland China. Furthermore, a forecasting model is proposed with integrating fast Fourier transform and long short-term memory extended (FFT-LSTME). The model uses monthly MEI as the primary input and the GNSS ZTD reconstruction sequence that responds to ENSO as the auxiliary input. It can predict ENSO events in the next 24 months with an index of agreement (IA) of 91.56% and a root mean square error (RMSE) of 0.25. The RMSE is optimized by 70.48%, 43.95%, and 11.6% when compared with radial basis function (RBF), LSTM, and FFT-LSTM.
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