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Abstract
The objective of the study was to put forth an interpolation method (the LZ method) for refining the GNSS-derived precipitable water vapor (PWV) map. We established a regional weighted mean temperature (Tm) model for this experiment, which introduced a minor difference into the resultant GNSS-derived PWV compared to the previous Tm models. The kernel of the LZ method consists of increasing the sample density via the virtual sample points. These virtual sample points originated from the digital elevation model (DEM) were constructed on the basis of the statistically significant correlation between PWV and geographical location (i.e., geographical coordinates and elevation). The LZ method was validated and compared to the conventional interpolation approach only accounting for the original sample points. The results reflect that the PWV maps generated by the LZ method showed more details than through conventional one. In addition, the prediction performance of the LZ method was better than that of the conventional method by using cross-validation.
Highlights
Water vapor is a key greenhouse gas and an indispensable component of the water cycle. it accounts for only 0.1% to 3% of the atmosphere, it is one of the most active atmospheric components [1]
In this work, we developed a multifactorial regional Tm model using the datasets of radiosonde and surface meteorological data in Hong Kong, which was to meet the needs of the process of obtaining GNSS-derived Precipitable water vapor (PWV) in Hong Kong
We have proposed a new interpolation method for refining the GNSS-derived PWV distribution map
Summary
Water vapor is a key greenhouse gas and an indispensable component of the water cycle. It accounts for only 0.1% to 3% of the atmosphere, it is one of the most active atmospheric components [1]. It directly affects the vertical stability of the atmosphere and the formation or evolution of weather systems and contributes to radiation balance and a series of weather phenomena, such as cloud formation, rainfall, or snowfall events, by absorbing or releasing massive amounts of latent heat during phase transition [2]. The accurate detection of the distribution and variation of atmospheric water vapor content can provide the data necessary to understand weather processes for weather forecasting and meteorology research [3]. Precipitable water vapor (PWV), the water vapor content of a vertically integrated column per unit area, is a direct indicator of atmospheric water vapor content and is expressed as the height of the corresponding equivalent liquid water column in centimeters [4]
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