A Novel ENSO Monitoring Method using Precipitable Water Vapor and Temperature in Southeast China

Abstract

Southeast China, a non-core region influenced by the El Niño–Southern Oscillation (ENSO), has been seldom investigated before. However, the occurrence of ENSO will affect the redistribution of precipitation and the temperature (T) spatial pattern on a global scale. This condition will further lead to flood or drought disasters in Southeast China. Therefore, the method of monitoring the occurrence of ENSO is important and is the focus of this paper. The spatiotemporal characteristics of precipitable water vapor (PWV) and T are first analyzed during ENSO using the empirical orthogonal function (EOF). The results showed that a high correlation spatiotemporal consistency exist between PWV and T. The response thresholds of PWV and T to ENSO are determined by moving the window correlation analysis (MWCA). If the sea surface temperature anomaly (SSTA) at the Niño 3.4 region exceeded the ranges of (−1.17°C, 1.04°C) and (−1.15°C, 1.09°C), it could cause the anomalous change of PWV and T in Southeast China. Multichannel singular spectral analysis (MSSA) is introduced to analyze the multi-type signals (tendency, period, and anomaly) of PWV and T over the period of 1979–2017. The results showed that the annual abnormal signal and envelope line fluctuation of PWV and T agreed well in most cases with the change in SSTA. Therefore, a standard PWV and T index (SPTI) is proposed on the basis of the results to monitor ENSO events. The PWV and T data derived from the grid-based European Center for Medium-Range Weather Forecasting (ECMWF) reanalysis products and GNSS/RS stations in Southeast China were used to validate the performance of the proposed SPTI. Experimental results revealed that the time series of average SPTI calculated in Southeast China corresponded well to that of SSTA with a correlation coefficient of 0.66 over the period of 1979–2017. The PWV values derived from the Global Navigation Satellite System (GNSS) and radiosonde data at two specific stations (WUHN and 45004) were also used to calculate the SPTI. The results showed that the correlation coefficients between SPTI and SSTA were 0.73 and 0.71, respectively. Such results indicate the capacity of the proposed SPTI to monitor the ENSO in Southeast China.

GNSS Precipitable Water Vapor Retrieval With the Aid of NWM Data for China

“…Zhao, Yao, Yao, and Zhang (2018) used the layered products from the ECMWF ERA‐Interim reanalysis data to calculate ZHD and T m in China for GNSS PWV retrieval. The pressure layer profile for GNSS PWV retrieval has been widely used to analyze or monitor extreme weather events, such as rainstorm, typhoon, ENSO, and drought (W. Jiang et al., 2017; Suparta & Rahman, 2016; Zhao, Liu, Yao, et al., 2020; Zhao, Ma, Yao, & Yao, 2019; Zhao, Ma, Yao, Liu, et al., 2019). Meanwhile, extensive studies have been conducted on the development of global T m empirical models to meet the requirements for real‐time GNSS PWV estimation (Huang et al., 2018; Sun et al., 2019a; Yao et al., 2014), which have largely accelerated the development of GNSS meteorology.…”

A Refined Tomographic Window for GNSS-Derived Water Vapor Tomography

“…Atmospheric water vapor (WV) drives numerous weather events and climate change [1,2]. Hence, accurately sensing the dynamic variation of WV is beneficial to weather forecasting and warnings for severe weather [3][4][5][6]. However, the traditional WV detection technique exhibits several inevitable shortcomings.…”