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.
Highlights
Water vapor is a major driving force of weather change and atmospheric circulation, and its dynamic trend is an important factor affecting climate and weather prediction [1,2,3]
To further analyze whether the anomalous changes of precipitable water vapor (PWV)/T were affected by El Niño–Southern Oscillation (ENSO) event in Southeast China, Figure 3 shows the spatiotemporal pattern of PWV and T first mode compared with that of sea surface temperature anomaly (SSTA) in Southeast China over the period of 1979–2017
We firstly proposed a novel index standard PWV and T index (SPTI) for monitoring the impact of ENSO over Southeast China, and its performance was validated in the entire area of Southeast China and at Global Navigation Satellite System (GNSS) and RS stations, respectively
Summary
Water vapor is a major driving force of weather change and atmospheric circulation, and its dynamic trend is an important factor affecting climate and weather prediction [1,2,3]. An increase of precipitable water vapor (PWV) will cause slight climate change in China, which is the focus area of this paper [6]. Better knowledge on the accurate distribution and change in atmospheric water vapor is important for climate research and weather prediction Techniques, such as radio sounding and water vapor radiometer, have been used to retrieve precipitable water vapor (PWV) for reflecting the variation of atmospheric water vapor. [29] found PWV data can effectively estimate surface wet and dry changes These studies have shown that PWV can be used to monitor meteorological drought/flood. The response thresholds of anomalous change of PWV and T in Southeast China were determined by moving window correlation analysis (MWCA), and the thresholds were credible based on the experiment data period (1979–2017) and the percentile method. The proposed SPTI is validated using ECMWF, GNSS, and RS data over the different period, and the results show a good performance of the proposed index
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