Responses of GNSS ZTD Variations to ENSO Events and Prediction Model Based on FFT-LSTME

The El Nino-Southern Oscillation (ENSO) event often causes natural disasters in mainland China. Existing quantitative analysis of ENSO event's 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 (TPMZ), (-1.12, 1.61) for the subtropical monsoon zone (SMZ), (-1.19, 1.62) for the temperate monsoon zone (TMZ), (-1.26, 1.64) for the temperate continental zone (TCZ), and (-1.22, 1.72) for the mountain plateau zone (MPZ). The ENSO event causes the amplitude of the nine-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 by 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.

Automated summary

By quantitatively analyzing the response patterns of GNSS ZTD to ENSO events, the prediction accuracy of ENSO events can be improved. A forecasting model is proposed that can predict ENSO events in the next 24 months.