Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

Abstract

This paper addresses the subject of inter-annual variability of the tropical precipitable water vapor (PWV) derived from 18 years of global navigation satellite system (GNSS) observations. Non-linear trends of retrieved GNSS PWV were investigated using the singular spectrum analysis (SSA) along with various climate indices. For most of the analyzed stations (~49%) the GNSS PWV anomaly was related to the El Niño Southern Oscillation (ENSO), although its influence on the PWV variability was not homogeneous. The cross-correlations coefficient values estimated between the Multivariate ENSO Index (MEI) and PWV were up to 0.78. A strong cross-correlation was also found for regional climate pattern expressed through CAR, DMI, HAW, NPGO, TNA and TSA indices. A distinct agreement was also found when instead of climate indices, the local sea surface temperature was examined (average correlation 0.60). The SSA method made it also possible to distinguish small-scale phenomena that affect PWV, such as local droughts or wetter rainy seasons. The overall nature of the investigated changes was also verified through linear trend analysis. In general, not a single station was characterized by a negative trend and its weighted mean value, calculated for all stations was equal to 0.08 ± 0.01 mm/year.

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“…The data have very high temporal resolution, while long term records are able to well represent variability associated with low frequency modes such as ENSO. Thus, this method provides a consistent framework for investigation of interaction between local high frequency variability (e.g., diurnal cycle), subseasonal weather modes and low-frequency, climate scale phenomena such as ENSO (Baldysz et al 2021). Pb, Rb, Tl, Li, Co, In, Cr, Mn, Zn, Ni, and Cd), Fe and Na of the second (B, V, Na, Ti, Fe, and Ce), and Al and P of the third (Mo, P, Mg, Th, Sr, Al, Ba, and As).…”

Analysis of GNSS-based PWV inversion accuracy and its temporal-spatial variation patterns under different geographic conditions

“…In recent years, scholars have done intensive research on GNSS-based PWV inversion [11][12][13][14][15] . Wu et al [16] increased the PWV inversion accuracy by improving the weighted mean temperature model based on the geodetic datum modernization in China and the data from 377 GNSS stations in the CMONC.…”

Monitoring the Water Vapor Content at High Spatio-Temporal Resolution Using a Network of Low-Cost Multi-GNSS Receivers

“…Water vapor is the subject of the climate change debate [10], [11] and GNSS reprocessed data provide homogenized decadal time series of IWV [12], [13]. Therefore variations of IWV are continuously the subject of analysis ranging from global studies [14], [15], through regional scale [12], [16], [17], to individual sites [18], [19]. IWV allows for reconnaissance and evolution monitoring of atmospheric rivers, which are responsible for heavy precipitation [20], thus they need to be represented in ensemble forecast systems [21], [22].…”