Abstract
Although there has been substantial improvement to numerical weather prediction models, accurate predictions of tropical cyclone rapid intensification (RI) remain elusive. The processes that govern RI, such as convection, may be inherently less predictable; therefore a probabilistic approach should be adopted. Although there have been numerous studies that have evaluated probabilistic intensity (i.e., maximum wind speed) forecasts from high resolution models, or statistical RI predictions, there has not been a comprehensive analysis of high-resolution ensemble predictions of various intensity change thresholds. Here, ensemble-based probabilities of various intensity changes are computed from experimental Hurricane Weather Research and Forecasting (HWRF) and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic (HMON) models that were run for select cases during the 2017–2019 seasons and verified against best track data. Both the HWRF and HMON ensemble systems simulate intensity changes consistent with RI (30 knots 24 h−1; 15.4 m s−1 24 h−1) less frequent than observed, do not provide reliable probabilistic predictions, and are less skillful probabilistic forecasts relative to the Statistical Hurricane Intensity Prediction System Rapid Intensification Index (SHIPS-RII) and Deterministic to Probabilistic Statistical (DTOPS) statistical-dynamical systems. This issue is partly alleviated by applying a quantile-based bias correction scheme that preferentially adjusts the model-based intensity change at the upper-end of intensity changes. While such an approach works well for high-resolution models, this bias correction strategy does not substantially improve ECMWF ensemble-based probabilistic predictions. By contrast, both the HWRF and HMON systems provide generally reliable predictions of intensity changes for cases where RI does not take place. Combining the members from the HWRF and HMON ensemble systems into a large multi-model ensemble does not improve upon HMON probablistic forecasts.
Highlights
Despite numerous advances in numerical weather prediction and physical understanding, the prediction of tropical cyclone (TC) intensity change remains a challenging problem, e.g., [1]
The goal of this study is to validate a large number of probabilistic TC intensity change forecasts, which are derived from the quasi-operational Hurricane Weather Research and Forecasting (HWRF) and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic (HMON) ensemble prediction systems, which were run as part of the Hurricane Forecast Improvement Project (HFIP)
In order to determine each model’s ability to replicate observed intensification rates, the cumulative distribution function (CDF) of 24 h intensity changes was computed from retrospective forecasts of each modeling system during each 24 h period (i.e., 0–24 h, 6–30 h, 12–36 h), as well as the corresponding best track intensity changes
Summary
Despite numerous advances in numerical weather prediction and physical understanding, the prediction of tropical cyclone (TC) intensity change remains a challenging problem, e.g., [1]. This issue is acute for rapid intensification (RI), which is often defined as the 95th percentile of the climatological intensity change [typically defined as 30 kt 24 h−1 or 15.4 m s−1 24 h−1] [2]. There are numerous potential reasons why RI might be especially difficult to predict, which is likely at least partially related to the interaction of multiple scales of motion that may be responsible for RI, e.g., [6]. Since RI is at least partially tied to convection, a probabilistic approach is likely more appropriate since convection is generally considered less predictable, deterministic forecasts are made by operational centers
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