Abstract
With the development of Global Navigation Satellite System (GNSS) reference station networks that provide rich data sources containing atmospheric information, the precipitable water vapor (PWV) retrieved from GNSS remote sensing has become one of the most important bodies of data in many meteorological departments. GNSS stations are distributed in the form of scatters, generally, these separations range from a few kilometers to tens of kilometers. Therefore, the spatial resolution of GNSS-PWV can restrict some applications such as interferometric synthetic aperture radar (InSAR) atmospheric calibration and regional atmospheric water vapor analysis, which inevitably require the spatial interpolation of GNSS-PWV. This paper explored a PWV interpolation scheme based on the GPT2w model, which requires no meteorological data at an interpolation station and no regression analysis of the observation data. The PWV interpolation experiment was conducted in Hong Kong by different interpolation schemes, which differed in whether the impact of elevation was considered and whether the GPT2w model was added. In this paper, we adopted three skill scores, i.e., compound relative error (CRE), mean absolute error (MAE), and root mean square error (RMSE), and two approaches, i.e., station cross-validation and grid data validation, for our comparison. Numerical results showed that the interpolation schemes adding the GPT2w model could greatly improve the PWV interpolation accuracy when compared to the traditional schemes, especially at interpolation points away from the elevation range of reference stations. Moreover, this paper analyzed the PWV interpolation results under different weather conditions, at different locations, and on different days.
Highlights
Water vapor comprises only a small percentage of the atmosphere, but it plays a key role in a series of atmospheric processes that act over a wide range of spatial and temporal scales, from global climate to micrometeorology [1]
The inverse distance weighted (IDW), Kriging, and Thin plate splines (TPS) algorithm without the global pressure and temperature 2 wet (GPT2w) model were used directly regardless of the elevation effect; the difference between the second set of schemes and the first set was the addition of the GPT2w model; for the third set, the three-dimensional (3D) Kriging and TPS algorithm that take into account the impact of elevation were the precipitable water vapor (PWV) in Global Navigation Satellite System (GNSS) reference stations, each weighted, and with the sum of weights equal to one
Some other useful conclusions can be drawn, i.e., TPS and Kriging are extremely ineffective when the impact of terrain elevation is not taken into account, especially the TPS, instead the IDW should be used in this situation; once the terrain elevation is considered, 3DKriging and 3DTPS would be effective in improving the PWV interpolation results, especially the 3DTPS, once again indicating the importance of elevation information for PWV interpolation
Summary
Water vapor comprises only a small percentage of the atmosphere, but it plays a key role in a series of atmospheric processes that act over a wide range of spatial and temporal scales, from global climate to micrometeorology [1]. Li et al proposed an elevation-dependent PWV interpolation method that employed an elevation-dependent covariance model to determine the best linear unbiased estimator weights, which needs a large number of measurements to achieve a reliable covariance function [16]. Another method based on the estimator of simple Kriging with varying local means and the Baby model was proposed by Li et al [17], which needs ground meteorological data. It was found that the proposed method had a good performance for the interpolation points that were far from the elevation range of the reference stations, which is a significant advantage over the traditional methods above-mentioned
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