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David Ahijevych, James O. Pinto, John K. Williams, and Matthias Steiner


A data mining and statistical learning method known as a random forest (RF) is employed to generate 2-h forecasts of the likelihood for initiation of mesoscale convective systems (MCS-I). The RF technique uses an ensemble of decision trees to relate a set of predictors [in this case radar reflectivity, satellite imagery, and numerical weather prediction (NWP) model diagnostics] to a predictand (in this case MCS-I). The RF showed a remarkable ability to detect MCS-I events. Over 99% of the 550 observed MCS-I events were detected to within 50 km. However, this high detection rate came with a tendency to issue false alarms either because of premature warning of an MCS-I event or in the continued elevation of RF forecast likelihoods well after an MCS-I event occurred. The skill of the RF forecasts was found to increase with the number of trees and the fraction of positive events used in the training set. The skill of the RF was also highly dependent on the types of predictor fields included in the training set and was notably better when a more recent training period was used. The RF offers advantages over high-resolution NWP because it can be run in a fraction of the time and can account for nonlinearly varying biases in the model data. In addition, as part of the training process, the RF ranks the importance of each predictor, which can be used to assess the utility of new datasets in the prediction of MCS-I.

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David John Gagne II, Amy McGovern, Sue Ellen Haupt, Ryan A. Sobash, John K. Williams, and Ming Xue


Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical weather prediction models, but they make many simplifying assumptions, are sensitive to differences in numerical model configuration, and are often not calibrated to observations. In this paper a storm-based probabilistic machine learning hail forecasting method is developed to overcome the deficiencies of existing methods. An object identification and tracking algorithm locates potential hailstorms in convection-allowing model output and gridded radar data. Forecast storms are matched with observed storms to determine hail occurrence and the parameters of the radar-estimated hail size distribution. The database of forecast storms contains information about storm properties and the conditions of the prestorm environment. Machine learning models are used to synthesize that information to predict the probability of a storm producing hail and the radar-estimated hail size distribution parameters for each forecast storm. Forecasts from the machine learning models are produced using two convection-allowing ensemble systems and the results are compared to other hail forecasting methods. The machine learning forecasts have a higher critical success index (CSI) at most probability thresholds and greater reliability for predicting both severe and significant hail.

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