| description abstract | This paper presents an automated approach for classifying storm type from weather radar reflectivity using decision trees. Recent research indicates a strong relationship between storm type (morphology) and severe weather, and such information can aid in the warning process. Furthermore, new adaptive sensing tools, such as the Center for Collaborative Adaptive Sensing of the Atmosphere?s (CASA?s) weather radar, can make use of storm-type information in real time. Given the volume of weather radar data from those tools, manual classification of storms is not possible when dealing with real-time data streams. An automated system can more quickly and efficiently sort through real-time data streams and return value-added output in a form that can be more easily manipulated and understood. The method of storm classification in this paper combines two machine learning techniques: K-means clustering and decision trees. K-means segments the reflectivity data into clusters, and decision trees classify each cluster. The K means was used to separate isolated cells from linear systems. Each cell received labels such as ?isolated pulse,? ?isolated strong,? or ?multicellular.? Linear systems were labeled as ?trailing stratiform,? ?leading stratiform,? and ?parallel stratiform.? The classification scheme was tested using both simulated and observed storms. The simulated training and test datasets came from the Advanced Regional Prediction System (ARPS) simulated reflectivity data, and observed data were collected from composite reflectivity mosaics from the CASA Integrative Project One (IP1) network. The observations from the CASA network showed that the classification scheme is now ready for operational use. | |
