Collaborative Research: Stochastic Transport Models for the Coastal Ocean

合作研究：沿海海洋随机输运模型

• 批准号: 0352187
• 负责人: Carter Ohlmann
• 金额: --
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-03-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0352187&HistoricalAwards=false
• 关键词: Collaborative; Research; Stochastic; Transport; Models

ABSTRACTOCE-0352187The PI's seek to determine optimal stochastic particle transport (i.e. Lagrangian) models for use in the coastal ocean which will provide a necessary link between the copious Eulerian coastal circulation data from high frequency (HF) radar systems, and transport information required by coastal resource managers tasked with identifying the fate of pollutants, larvae, and objects lost-at-sea. Specific research objectives are to (i) observe surface flow fields in two coastal regions using HF radar and high-resolution drifters, (ii) to develop accurate Lagrangian transport models to predict trajectories from the HF radar fields, (iii) to model trajectories and quantify their skill through comparisons with in-situ drifter tracks, and (iv) to compute redistribution kernel functions (RKFs), or connectivity matrices, and demonstrate their utility as simple probabilistic near-shore transport models. Existing trajectory models will be modified for consideration of flow patterns characteristic to the coastal ocean. A model with a large-scale mean component (U), a periodic component representing tidal and (near-) inertial motions (Up), and a Lagrangian stochastic component (model; LSM) for subgrid-scale motions (u) will be developed. The LSM will initially be based on high resolution drifter data. An LSM parameterization based on large-scale velocity information (e.g. integral time scales, eddy variance.) and coastal geomorphology will then be developed. This enables the trajectory models to be applied in all coastal regions with sufficient large-scale flow information. Model skill over a wide range of dynamic parameters (e.g. velocity, vorticity, eddy energy) will be quantified in the Santa Barbara (CA) and Miami (FL) coastal regions through comparisons with in-situ drifter tracks. RKFs, giving the probability that a particle released at some location x0 at time t0 will reach location x1 at t1, will be computed with modeled trajectories and evaluated as tools for predicting transport, or connectivity in the coastal ocean. An improved understanding of coastal circulation will result from subsequent identification of how and why RKFs vary spatially and temporally.

PI寻求确定用于沿海海洋的最优随机颗粒传输(即拉格朗日)模型，该模型将提供来自高频(HF)雷达系统的丰富的欧拉海岸环流数据与沿海资源管理者所需的运输信息之间的必要联系，这些资源管理者的任务是确定污染物、幼虫和海上丢失物体的命运。具体的研究目标是：(1)使用高频雷达和高分辨率漂移器观测两个沿海地区的地表流场；(2)发展精确的拉格朗日输运模式，从高频雷达场预测轨迹；(3)通过与现场漂流路径的比较，模拟轨迹并量化其技能；以及(4)计算再分布核函数(RKF)，或连通性矩阵，并展示其作为简单概率近岸输运模型的实用性。现有的轨迹模型将进行修改，以考虑沿海海洋特有的流动模式。将发展一个具有大尺度平均分量(U)、代表潮汐和(近)惯性运动(UP)的周期分量以及次网格尺度运动(U)的拉格朗日随机分量(MODEL；LSM)的模式。LSM最初将基于高分辨率漂流数据。基于大尺度速度信息(例如，积分时间尺度、涡度变化)的LSM参数化。然后发展海岸地貌学。这使得轨迹模型可以应用于所有具有足够大尺度水流信息的沿海地区。在圣巴巴拉(CA)和迈阿密(FL)沿海地区，将通过与现场漂流路径的比较，对各种动态参数(如速度、涡度、涡旋能量)的模型技能进行量化。RKF给出了在时间t0在某个位置x0释放的粒子将在t1到达位置x1的概率，将通过模拟的轨迹进行计算，并作为预测沿海海洋中的运输或连通性的工具进行评估。随后查明RKF在空间和时间上的变化方式和原因，将有助于更好地了解沿海环流。