A Regression-Based Approach for Cool-Season Storm Surge Predictions along the New York–New Jersey Coast
A Regression-Based Approach for Cool-Season Storm Surge Predictions along the New York–New Jersey Coast
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基于回归的纽约-新泽西海岸冷季风暴潮预测方法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Munch
中科院分区：
文献类型：
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作者：
K. J. Roberts;B. Colle;N. Georgas;S. Munch
摘要利用1979 - 2012年冷季（10月1日-31年3月31日）纽约-新泽西沿岸沿着三个观测站（新泽西大西洋城、纽约市炮台和纽约蒙托克角）的两种不同的大气再分析数据集和水位观测资料，提出了一种多线性回归（MLR）方法，用于预测3小时风暴潮。MLR的预报因子被指定为代表每个站附近的方框区域的长期表面风应力和表面海平面压力最小值。回归低估了相对较大（≥ 95%）的风暴最大涌浪高度6.0%-38.0%。偏差校正技术减少了10%-15%的平均绝对误差，在不同的站风暴最大潮预测。在2010年10月至2014年3月期间，使用来自北美中尺度（NAM）模型的相同预测表面风和压力，将Battery的原始和偏差校正的浪涌预测与原始输出进行比较。
AbstractA multilinear regression (MLR) approach is developed to predict 3-hourly storm surge during the cool-season months (1 October–31 March 31) between 1979 and 2012 using two different atmospheric reanalysis datasets and water-level observations at three stations along the New York–New Jersey coast (Atlantic City, New Jersey; the Battery in New York City; and Montauk Point, New York). The predictors of the MLR are specified to represent prolonged surface wind stress and a surface sea level pressure minimum for a boxed region near each station. The regression underpredicts relatively large (≥95th percentile) storm maximum surge heights by 6.0%–38.0%. A bias-correction technique reduces the average mean absolute error by 10%–15% at the various stations for storm maximum surge predictions. Using the same forecast surface winds and pressures from the North American Mesoscale (NAM) model between October and March 2010–14, raw and bias-corrected surge predictions at the Battery are compared with raw output ...