  The knowledge of type of precipitating cloud is crucial for radar based
quantitative estimates of precipitation. We propose a novel model called
CloudSense which uses machine learning to accurately identify the type of
precipitating clouds over the complex terrain locations in the Western Ghats
(WGs) of India. CloudSense uses vertical reflectivity profiles collected during
July-August 2018 from an X-band radar to classify clouds into four categories
namely stratiform,mixed stratiform-convective,convective and shallow clouds.
The machine learning(ML) model used in CloudSense was trained using a dataset
balanced by Synthetic Minority Oversampling Technique (SMOTE), with features
selected based on physical characteristics relevant to different cloud types.
Among various ML models evaluated Light Gradient Boosting Machine (LightGBM)
demonstrate superior performance in classifying cloud types with a BAC of 0.8
and F1-Score of 0.82. CloudSense generated results are also compared against
conventional radar algorithms and we find that CloudSense performs better than
radar algorithms. For 200 samples tested, the radar algorithm achieved a BAC of
0.69 and F1-Score of 0.68, whereas CloudSense achieved a BAC and F1-Score of
0.77. Our results show that ML based approach can provide more accurate cloud
detection and classification which would be useful to improve precipitation
estimates over the complex terrain of the WG.
