Identifying Ocean Swell Generation Events from Ross Ice Shelf Seismic Data
Identifying Ocean Swell Generation Events from Ross Ice Shelf Seismic Data
复制标题
从罗斯冰架地震数据中识别海洋涌浪生成事件
DOI：
10.1175/jtech-d-19-0093.1
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发表时间：
2019
影响因子：
2.2
通讯作者：
Bromirski, Peter D.
中科院分区：
文献类型：
--
作者：
Hell, Momme C.;Cornelle, Bruce D.;Gille, Sarah T.;Miller, Arthur J.;Bromirski, Peter D.
温带气旋下的强烈地面风对海洋施加强烈的表面应力，导致海洋上层混合，热通量增强，并产生波浪，随着时间的推移，导致长距离传播的巨浪(超过10-S周期)。由于低频涌浪的传播速度快于高频涌浪，因此可以利用测量地点涌浪到达时间的频率相关性来推断波从其发生地点传播的距离和时间。这项研究提出了一种方法，利用罗斯冰架(RIS)上的点观测得到的海洋膨胀光谱图来验证南大洋上空高风速区的位置，从而验证温带气旋的位置。这里的重点是该方法的实施和稳健性，以便为今后广泛应用于从大气再分析数据核实南大洋风暴位置奠定基础。该方法将线性涌浪频散与参数波模型相结合，建立了RIS地震观测频谱图中离散涌浪到达的时频相关模型。梯度下降和蒙特卡罗抽样的两步优化程序(深度学习)允许对参数分布进行详细估计，并对膨胀起因进行稳健估计。涌浪源位置的中位数不确定度径向距离为110公里，时间为2小时。不确定性是由RIS观测和模型得出的，而不是假设的分布。该方法是由物理第一原理通知的有监督机器学习的例子，以便于在物理域中解释参数。
Strong surface winds under extratropical cyclones exert intense surface stresses on the ocean that lead to upper-ocean mixing, intensified heat fluxes, and the generation of waves, that, over time, lead to swell waves (longer than 10-s period) that travel long distances. Because low-frequency swell propagates faster than high-frequency swell, the frequency dependence of swell arrival times at a measurement site can be used to infer the distance and time that the wave has traveled from its generation site. This study presents a methodology that employs spectrograms of ocean swell from point observations on the Ross Ice Shelf (RIS) to verify the position of high wind speed areas over the Southern Ocean, and therefore of extratropical cyclones. The focus here is on the implementation and robustness of the methodology in order to lay the groundwork for future broad application to verify Southern Ocean storm positions from atmospheric reanalysis data. The method developed here combines linear swell dispersion with a parametric wave model to construct a time- and frequency-dependent model of the dispersed swell arrivals in spectrograms of seismic observations on the RIS. A two-step optimization procedure (deep learning) of gradient descent and Monte Carlo sampling allows detailed estimates of the parameter distributions, with robust estimates of swell origins. Median uncertainties of swell source locations are 110 km in radial distance and 2 h in time. The uncertainties are derived from RIS observations and the model, rather than an assumed distribution. This method is an example of supervised machine learning informed by physical first principles in order to facilitate parameter interpretation in the physical domain.
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影响因子：
3.7
作者：
J. Miles
通讯作者：
J. Miles
埃里伯斯冰川舌的诞生
影响因子：
64.8
作者：
W. Robinson;T. Haskell
通讯作者：
T. Haskell
影响因子：
5.2
作者：
P. Bromirski;A. Diez;P. Gerstoft;R. Stephen;S. Bolmer;D. Wiens;R. Aster;A. Nyblade
通讯作者：
P. Bromirski;A. Diez;P. Gerstoft;R. Stephen;S. Bolmer;D. Wiens;R. Aster;A. Nyblade
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影响因子：
3.4
作者：
V. Squire;W. Robinson;M. Meylan;T. Haskell
通讯作者：
T. Haskell
影响因子：
5.2
作者：
MacAyeal, Douglas R.;Okal, Emile A.;Thom, Jonathan E.
通讯作者：
Thom, Jonathan E.