Assessment of radial basis function method with thin plate spline kernel for evaluable precipitable water vapor interpolation from GPS observations over state of California, USA

Precipitable water vapor (PWV) is one of the most important data in meteorological studies. This component has high spatial and temporal changes, today the use of global navigation satellite systems (GNSS) observations is one of the ways to improve the accuracy of water vapor parameter estimation. The waves sent from GNSS satellites are delayed when passing through the troposphere layer. The troposphere delay is divided into two parts, dry and wet, and the wet part depends on changes in water vapor. In this article, the interpolation methods based on radial basis functions with 3D thin plate spline kernel, artificial neural network of perceptron type, kriging and inverse distance weighted have been evaluated. A region located in North America including 26 Global Positioning System (GPS) stations has been studied and the amounts of precipitable water vapor on two days in winter and summer have been evaluated using these data in the aforementioned methods. The value of the root mean square error (RMSE) using the 3D thin plate spline method for two days in winter and summer has been obtained as 0.6 and 1.62 mm, respectively, which has the lowest RMSE value compared to other methods. And as a result, there is a higher accuracy in both days. Finally, by using 3D thin plate spline interpolation method, a dense map of water vapor changes in the troposphere layer in the study area has been prepared, which can have an impact on forecasting the weather and estimating the amount of precipitation