This study describes a framework that provides qualitative weather information on winter precipitation types using a data-driven approach. The framework incorporates the data retrieved from weather radars and the numerical weather prediction (NWP) model to account for relevant precipitation microphysics. To enable multimodel-based ensemble classification, we selected six supervised machine learning models: k-nearest neighbors, logistic regression, support vector machine, decision tree, random forest, and multi-layer perceptron. Our model training and cross-validation results based on Monte Carlo Simulation (MCS) showed that all the models performed better than our baseline method, which applies two thresholds (surface temperature and atmospheric layer thickness) for binary classification (i.e., rain/snow). Among all six models, random forest presented the best classification results for the basic classes (rain, freezing rain, and snow) and the further refinement of the snow classes (light, moderate, and heavy). Our model evaluation, which uses an independent dataset not associated with model development and learning, led to classification performance consistent with that from the MCS analysis. Based on the visual inspection of the classification maps generated for an individual radar domain, we confirmed the improved classification capability of the developed models (e.g., random forest) compared to the baseline one in representing both spatial variability and continuity.
Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies
