Enhanced Retrieval of Precipitable Water Vapor Over Land: An Approach for Algorithmic Improvement and Performance Assessment

Precipitable Water Vapor (PWV) is a critical variable for monitoring extreme weather events, particularly precipitation. Accurate retrieval of PWV is therefore essential in hydrology and meteorology. This study proposes an applied machine learning framework, termed PWV-ML, to enhance PWV estimation. Three machine learning methods, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Random Forest (RF), were evaluated using data from Radiosonde and GNSS observation stations between 2019 and 2023 across the United States and Canada. The models incorporated multiple auxiliary variables derived from AMSR2 brightness temperature data, MODIS products (MOD21A1N, MOD13A2), and seven ERA5 reanalysis variables. Results show that PWV-ML substantially outperforms traditional Multiple Linear Regression (MLR). Among the tested methods, RF achieved slightly superior accuracy. Variable selection proved critical, with dewpoint temperature (Td) and total column water vapor (TCWV) from ERA5 providing the strongest contributions. An optimized minimal input configuration, consisting of BT89V, cloud liquid water (CLW), the microwave atmospheric water vapor index (MAWVI), altitude, and Td, yielded strong agreement with ground observations (R = 0.92; RMSE = 4.77 mm; KGE = 0.87 for Radiosonde, and R = 0.91; RMSE = 2.82 mm; KGE = 0.76 for GNSS). Incorporating additional ERA5 variables further improved performance, achieving near-perfect correlations (R = 0.99). These findings demonstrate that the PWV-ML framework can retrieve PWV with high accuracy under diverse climatic conditions.