A Grid Model for Vertical Correction of Precipitable Water Vapor over the Chinese Mainland and Surrounding Areas Using Random Forest
Abstract. Various ground-based observing techniques provide precipitable water vapor (PWV) products with different spatial resolutions. To effectively integrate these products, especially in terms of vertical orientation, spatial interpolation is essential. In this context, we have developed a model to characterize PWV variation with altitude in the study area. Our model, known as RF-PWV (a PWV vertical correction grid model with a 1° x 1° resolution), is constructed using random forest based on the relationship between PWV differences from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) monthly average hourly data and height differences and time. When validated against 1-h ERA5 PWV profiles, RF-PWV exhibits a 99.84 % reduction in Bias and a 63.41 % decrease in RMSE compared to the most recent model, C-PWVC1. Furthermore, when validated against radiosonde data, RF-PWV shows a 96.36 % reduction in Bias and a 5 % decrease in RMSE compared to C-PWVC1. Additionally, RF-PWV outperforms C-PWVC1 in terms of resistance to seasonal and height differences interference. The model eliminates the need for meteorological parameters, allowing for high-precision PWV vertical correction by inputting only time and height differences. Consequently, RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
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