A Machine-Learning Approach to Mitigate Ground Clutter Effects in the GPM Combined Radar-Radiometer Algorithm (CORRA) Precipitation Estimates

In this study, a machine learning-based methodology is developed to mitigate the effects of ground clutter on precipitation estimates from the Global Precipitation Measurement Combined Radar-Radiometer Algorithm. Ground clutter can corrupt and obscure the precipitation echo in radar observations, leading to inaccuracies in precipitation estimates. To improve upon previous work, this study introduces a general machine learning (ML) approach that enables a systematic investigation and a better understanding of uncertainties in clutter mitigation. To allow for a less restrictive exploration of conditional relations between precipitation above the lowest clutter-free bin and surface precipitation, reflec- tivity observations above the clutter are included in a fi xed-size set of predictors along with the precipitation type, surface type, and freezing level to estimate surface precipitation rates, and several ML-based estimation methods are investigated. A neural network (NN) model is ultimately identified as the best candidate for systematic evaluations, as it is computationally fast to apply while effective in applications. The NN provides unbiased estimates; however, it does not significantly outperform a simple bias correction approach in reducing random errors in the estimates. The similar performance of other ML approaches suggests that the NN's limited improvement beyond bias removal is due to indeterminacies in the data rather than limitations in the ML approach itself.