Abstract
Spaceborne precipitation radar (PR) observations have strongly promoted the understanding of global precipitation. Nevertheless, only two spaceborne PRs are currently in routine operation, which limits the applications of PR data in precipitation monitoring and research. In this work, a machine learning (ML) algorithm is applied to derive radar-based convection intensity parameters with multiple-channel passive microwave observations from the GPM Microwave Imager (GMI). The algorithm first determines whether the precipitation features (PFs) reach a certain intensity, for example, a maximum radar reflectivity exceeding 20 dBZ. The classification process achieves a high score, with an accuracy rate of 97% and a recall rate of 100%. Then the intensity parameters of the PFs meeting a certain intensity, such as the maximum 20 dBZ echo top height (MAXHT20) and the temperature at MAXHT20 (T-MAXHT20), are fitted with the optimal ML algorithm, a random forest (RF) model. The RF model is applied to derive MAXHT20 with global PFs, and the root mean square error and the coefficient of determination are 1.07 km and 0.79, respectively. Nevertheless, there are significant regional discrepancies in biases between the ML model results and the observations. Therefore, investigations are conducted in different regions and for different PF intensities. The results imply that the applying the algorithm in a specific region can improve model performance and that algorithm performs best in cases with considerable amounts of ice particles. These results confirm the feasibility of applying passive microwave observations to derive radar-based parameters in the assessment of precipitation systems.
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