Data-Driven Modeling of Global Storm Surges
Data-Driven Modeling of Global Storm Surges
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DOI：
10.3389/fmars.2020.00260
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发表时间：
2020-04-24
影响因子：
3.7
通讯作者：
Cid, A.
中科院分区：
文献类型：
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作者：
Tadesse, M.;Wahl, T.;Cid, A.
在许多地区，热带或温带气旋引起的风暴潮是关键极端海平面事件的主要原因。可以使用基于基本物理过程的数值模型来模拟风暴潮，也可以使用数据驱动的模型来模拟风暴潮，这些模型量化了风暴潮(风暴潮)和相关预报因子(风速、平均海平面气压等)之间的关系。这项研究探索了数据驱动模型在全球模拟风暴潮的潜力。利用大量预报器(从遥感和气候再分析获得)以及预报量(来自验潮仪观测和风暴潮再分析)来训练和验证数据驱动模型，以模拟全球海岸线的日最大涌浪。数据驱动模式更好地模拟了热带和亚热带地区的日最大涌浪[平均相关系数和均方根误差(RMSE)分别为0.79和7.5厘米]，而热带地区(平均相关系数和均方根误差分别为0.45和5.3厘米)。对于极端事件，温带(热带)地区的平均相关系数降低到0.54(0.33)，RMSE增加到14.5(13.1)cm。强制使用遥感预报器的模型的性能(平均相关性为0.69)略好于强制使用从再分析产品获得的预报器的模型(平均相关性为0.68)。结果还表明，与全球潮汐和涌浪再分析(GTSR)相比，全球潮汐和涌浪再分析(GTSR)有了显著改进(即平均相关性从0.54增加到0.68；RMSE从11厘米减少到7厘米)，GTSR来自唯一的全球水动力模型。对于大约70%的验潮仪来说，平均海平面压力是模拟日最大潮汐的最重要的预报因子。我们的结果突出了数据驱动模型在全球范围内模拟风暴潮的附加价值，以及现有的水动力数值模型。
In many areas, storm surges caused by tropical or extratropical cyclones are the main contributors to critical extreme sea level events. Storm surges can be simulated using numerical models that are based on the underlying physical processes, or by using data-driven models that quantify the relationship between the predictand (storm surge) and relevant predictors (wind speed, mean sea-level pressure, etc.). This study explores the potential of data-driven models to simulate storm surges globally. A multitude of predictors (obtained from remote sensing and climate reanalysis) along with predictands (from tide gage observations and storm surge reanalysis) are utilized to train and validate data-driven models to simulate daily maximum surge for the global coastline. Data-driven models simulate daily maximum surge better in extratropical and sub-tropical regions [average correlation and root-mean-square error (RMSE) of 0.79 and 7.5 cm, respectively], than in the tropics (average correlation and RMSE of 0.45 and 5.3 cm, respectively). For extreme events, the average correlation decreases to 0.54 (0.33) and RMSE increases to 14.5 (13.1) cm for extratropical (tropical) regions. Models forced with remotely sensed predictors showed a slightly better performance (average correlation of 0.69) than models forced with predictors obtained from reanalysis products (average correlation of 0.68). Results also highlight a significant improvement (i.e., average correlation increases from 0.54 to 0.68; RMSE reduces from 11 to 7 cm) over the Global Tide and Surge Reanalysis (GTSR), derived from the only global hydrodynamic model. For approximately 70% of tide gages, mean sea-level pressure is the most important predictor to model daily maximum surge. Our results highlight the added value of data-driven models in the context of simulating storm surges at the global scale, in addition to existing hydrodynamic numerical models.