A Forecast Cycle–Based Evaluation for Tropical Cyclone Rapid Intensification Forecasts by the Operational HWRF Model

Abstract

Abstract An evaluation framework for tropical cyclone rapid intensification (RI) forecasts is introduced and applied to evaluate the performance of RI forecasts by the operational Hurricane Weather Research and Forecasting (HWRF) Model. The framework is based on the performance of each 5-day forecast cycle, while the conventional RI evaluation is based on the statistics of successful or false RI forecasts at individual lead times. The framework can be used to compare RI forecasts of different cycles, which helps model developers and forecasters to characterize RI forecasts under different scenarios. It also can provide the evaluation of statistical performance in the context of 5-day forecast cycles. The RI forecast of each cycle is assessed using a modified probability-based approach that takes the absolute errors in intensity changes into account. The overall performance of RI forecasts during a given period is assessed based on the fractions of the individual forecast cycles during which RI events are successfully or falsely predicted. The framework is applied to evaluate the performance of RI forecasts by the HWRF Model for the whole life cycle of a single hurricane, as well as for each of the hurricane seasons from 2009 to 2021. The metric based on the probabilities of detection and false alarm rate of RI is compared with that based on the absolute errors in the intensity and intensity change during RI events. Significance Statement An evaluation framework for tropical cyclone rapid intensification (RI) forecasts is introduced, focusing on the performance of RI forecasts in each 5-day forecast cycle. The cycle-based approach can help to characterize RI forecasts under different conditions such as certain synoptic scenarios, initial conditions, or vortex structures. It also can be used to assess the overall performance of RI forecasts in terms of the percentages of individual forecast cycles that successfully or falsely predict RI events.