Assessing NOAA Rip-Current Hazard Likelihood Predictions: Comparison with Lifeguard Observations and Parameterizations of Bathymetric and Transient Rip-Current Types

电流(流体) 裂流 水深测量 瞬态(计算机编程) 危害 气象学 环境科学 气候学 计算机科学 地质学 地理 海洋学 化学 有机化学 海岸 操作系统
作者
Audrey Casper,Emma Nuss,Christine M. Baker,Melissa Moulton,Gregory Dusek
出处
期刊:Weather and Forecasting [American Meteorological Society]
标识
DOI:10.1175/waf-d-23-0181.1
摘要

Abstract Rip currents, fast offshore-directed flows, are the leading cause of death and rescues on surf beaches worldwide. The National Oceanographic and Atmospheric Administration (NOAA) seeks to minimize this threat by providing rip current hazard likelihood forecasts based on environmental conditions from the Nearshore Wave Prediction System. Rip currents come in several types, including bathymetric rip currents that form when waves break on sandbars interspersed with channels, and transient rip currents that form when there are breaking waves coming from multiple directions. The NOAA model was developed and tested in an area where bathymetric rip currents may be the most prevalent type of rip current. Therefore, model performance in regions where other types of rip currents (e.g., transient rip currents) may be more ubiquitous remains unknown. To investigate the efficacy of the NOAA model guidance in the context of different rip-current types, we compared modeled rip-current probabilities with physical-based parameterizations of bathymetric and transient rip-current speeds. We also compared these probabilities to lifeguard observations of bathymetric and transient rip currents from Salt Creek Beach, CA in Summer-Fall 2021. We found that the NOAA model skillfully predicts a wide range of hazardous parameterized bathymetric speeds but generally underpredicts hazardous transient rip-current speeds and the hazardous rip currents observed at Salt Creek Beach. Our results demonstrate how wave parameters, including directional spread, may serve as environmental indicators of rip-current hazard. By evaluating factors that influence the skill of modeled rip-current predictions, we strive towards improved rip-current hazard forecasting.
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