Abstract
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.
Highlights
Precipitation is one of the most important variables in atmospheric and environmental sciences, including weather- and hydrology-related research
The conventional way of monitoring winter weather types has often relied on the dual-polarization capability of weather radars, which allows us to define hydrometeor types (e.g., [6]). These precipitation types obtained at the radar sampling locations aloft occasionally do not coincide with what is observed at the surface because of the possible phase transition along its falling path
The six models selected for this study are k-nearest neighbors, logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and multi-layer perceptron (MLP)
Summary
Precipitation is one of the most important variables in atmospheric and environmental sciences, including weather- and hydrology-related research. On the other hand, quantifying amounts of solid and mixed precipitation remains challenging, as does identifying the many different types of precipitation (e.g., [3,4]) because of the difficulty of their reliable measurement The information on this cold type of precipitation is important for infrastructure and facility management (e.g., air/ground traffic control and road closure) during the winter season in many regions (e.g., [5]). The conventional way of monitoring winter weather types (e.g., snow and freezing rain) has often relied on the dual-polarization capability of weather radars, which allows us to define hydrometeor types (e.g., [6]) These precipitation types obtained at the radar sampling locations aloft occasionally do not coincide with what is observed at the surface because of the possible phase transition along its falling path
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