A CLIMATOLOGY BASED FORECAST TOOL FOR COASTAL FLOODING IN THE LOWCOUNTRY Journal of Applied Meteorology and Climatology

Coastal nuisance flooding has increased by an order of magnitude over the past half century, but the National Weather Service has a limited suite of statistical tools to forecast them. Such a tool was developed using coastal flood events from 1996—2014 in Charleston, South Carolina, which were identified and classified by prevailing synoptic conditions based on composite mean sea level pressure anomalies. The synoptic climatology indicated low level northeasterly winds dominated the forcing in anticyclonic and cyclonic events, while a southeasterly surge was the main forcing component for frontal events. Tidal anomalies between flood events and previous low tides were used to create linear regression models for each composite classification studied for forecasting levels of coastal flood magnitude. Beta tests using data from 2018—2019 confirmed the effectiveness of the models with RMSE values less than 0.3 ft and MAE values less than 0.25 ft for each event type. The veracity of the methods was further verified by a multiple day case study from November 2018, where the model was tested against both statistically predicted heights and heights based on ETSS Model (v2.2). The RMSE and MAE for the statical model were 0.18 and 0.15 respectively, while the same values for the ETSS model were 0.28 and 0.23 respectively. Grant no. NA11NWS4670004

近半个世纪以来，轻微海岸洪涝（coastal nuisance flooding）的发生频次已增长一个数量级，但美国国家气象局（National Weather Service, NWS）可用于预报这类灾害的统计工具套件仍较为有限。本研究基于1996—2014年美国南卡罗来纳州查尔斯顿的海岸洪涝事件开发了一款此类工具，这些事件通过合成平均海平面气压异常值，结合主导天气形势完成识别与分类。天气气候学分析显示，反气旋型与气旋型洪涝事件的主导强迫因子为低层东北风，而锋面型事件的主要强迫因子则为东南向风暴增水。研究选取洪涝事件与此前低潮位之间的潮汐异常值，针对每一类合成天气型分类构建线性回归模型，以预测海岸洪涝的水位量级。利用2018—2019年数据开展的β测试（beta tests）验证了模型的有效性：各类事件的均方根误差（Root Mean Square Error, RMSE）均小于0.3英尺，平均绝对误差（Mean Absolute Error, MAE）均小于0.25英尺。2018年11月的多日案例研究进一步验证了该方法的可靠性：模型分别与统计预测水位以及基于ETSS模型（v2.2）的水位进行了对比测试。该统计模型的均方根误差与平均绝对误差分别为0.18英尺和0.15英尺，而ETSS模型的对应数值则分别为0.28英尺和0.23英尺。资助编号：NA11NWS4670004