Estimating the storm surge magnitude and annual exceedance probability is a key element in the siting and design of coastal nuclear power plants in both the U.S. and France. However, differences in storm climatology, specifically the relative importance of tropical cyclones (TCs) versus extratropical storms (XTCs), have driven differences in estimation method development. This work compares purely statistical modeling with combined statistical and numerical simulation modeling approaches for extreme storm surge applied to the U.S. North Atlantic coast which is subject to both tropical and extratropical storms. Two frequency analysis methods are applied to observed water levels and compared to a copula-based joint probability analysis of TCs and automated frequency analysis of XTCs that is enriched with numerically simulated storms. One frequency analysis method is applied using (1) hourly at-site data and (2) hourly at-site data enriched with additional data from a homogeneous region. The other frequency analysis method is applied using (1) hourly at-site data and (2) hourly at-site data enriched with monthly water level maxima. Variables of interest used in the comparison are skew storm surge, maximum instantaneous storm surge, non-tidal residual and maximum seal level. The performance of the methods (mean surge and water level estimates and confidence intervals) depend on the variable of interest and, to some extent, on return period. Inclusion of additional information (e.g., regional water levels, and monthly maxima) in the frequency analysis methods does not have a large impact on estimated mean surge and water levels, but significantly reduces resulting confidence intervals (over 40% reduction in some cases). However, the confidence intervals still grow with increasing return period. Inclusion of simulated storms in the joint probability analysis results in significantly different mean surge and water level estimates (up to 25% higher than the frequency analysis in some cases). The joint probability analysis confidence intervals are wider than those for the frequency analysis methods lower return periods (e.g., 60%–80% wider at 100 years), but they grow much more slowly and are significantly narrower for higher return periods (e.g., 40%–60% narrower at 1 000 years). Although there are appreciable differences between the results documented in this paper, these are reasonable due to differences in the data and methods used in this comparison.

估计风暴潮强度和年度超标概率是美国和法国沿海核电站选址和设计的关键要素。然而，风暴气候学的差异，特别是热带气旋（TC）与温带风暴（XTC）的相对重要性，导致了估算方法开发的差异。这项工作将纯粹的统计模型与统计和数值模拟相结合的建模方法进行了比较，适用于美国北大西洋沿岸遭受热带和温带风暴影响的极端风暴潮。将两种频率分析方法应用于观测的水位，并与基于联结的 TC 联合概率分析和通过数值模拟风暴丰富的 XTC 自动频率分析进行比较。应用一种频率分析方法，使用（1）每小时现场数据和（2）富含来自同质区域的附加数据的每小时现场数据。另一种频率分析方法采用 (1) 每小时现场数据和 (2) 富含每月水位最大值的每小时现场数据。比较中使用的感兴趣的变量是斜风暴潮、最大瞬时风暴潮、非潮汐残余和最大海豹水位。这些方法的性能（平均浪涌和水位估计以及置信区间）取决于感兴趣的变量，并且在某种程度上取决于重现期。在频率分析方法中包含附加信息（例如，区域水位和每月最高水位）不会对估计的平均浪涌和水位产生很大影响，但会显着降低所得的置信区间（在某些情况下降低超过 40%）。然而，置信区间仍然随着回报期的增加而增加。将模拟风暴纳入联合概率分析会导致平均浪涌和水位估计值显着不同（在某些情况下比频率分析高出 25%）。联合概率分析置信区间比频率分析方法的低回报期的置信区间更宽（例如，100 年时宽 60%–80%），但它们的增长速度要慢得多，并且对于较高回报期（例如，40 年）显着更窄。 1000 年时缩小 %–60%）。尽管本文记录的结果之间存在明显差异，但由于比较中使用的数据和方法不同，这些差异是合理的。