CloudSense: A model for cloud type identification using machine learning from radar data

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 (WG) 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 (Balanced Accuracy) of 0.79 and F1-Score of 0.8. 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 of 0.8 and F1-Score of 0.79. 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. • A novel model called CloudSense is proposed which uses machine learning (ML) for identifying precipitating cloud types over the WG. • Using CloudSense, the clouds are classified into shallow, stratiform, convective and mixed stratiform-convective. • Among various ML models, Light GBM, a tree-based learning algorithm, showed the best test results with a BAC of 0.79 and F1-Score of 0.8. • CloudSense performed better than conventional radar algorithm, achieving a BAC of 0.8 and F1-Score of 0.79 while the latter attained a BAC of 0.69 and F1-Score of 0.68.