Nonstationary Tropospheric Processes in Geodetic Precipitable Water Vapor Time Series

Abstract

Precipitable Water Vapor (PWV) time series acquired using space geodetic techniques such as GPS (Global Positioning System) and VLBI (Very Long Baseline Interferometry) have statistical properties which exhibit spatial-temporal variations. These variations could be seen as manifestation of atmospheric structure and dynamics. Statistically, second-order moments may carry spatial-temporal information. The measures of spatial-temporal information in the PWV time series such Self-Similar (SS) and Long-Range Dependence (LRD) parameters could be utilized for both geodetic applications and meteorology. If the time series is segmented into small windows and local statistical parameters calculated, the second-order quantities derived thereof could be used to describe global and local (non-)stationary processes in the atmosphere. Results from our study show that, the PWV time series reconstructed by Singular Spectrum Analysis (SSA) has features that describe (non-)stationary. The trends, spikes and excursions seen in the power spectra of a non-decimated discrete Haar wavelet transform of the PWV time series is further evidence to (non-)stationarity.Further, a wavelet based joint estimator of SS and LRD shows that the PWV time series has memory and the mean, variance, and the scaling exponents show fluctuations. These fluctuations depict nonstationarityKeywordsPrecipitable water vaporparameterSelf-similarWavelet transform