Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea
Lagrangian tracking in stochastic fields with application to an ensemble of velocity fields in the Red Sea
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DOI：
10.1016/j.ocemod.2018.08.008
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发表时间：
2018-11
期刊：
影响因子：
3.2
通讯作者：
Samah El Mohtar;I. Hoteit;O. Knio;Leila Issa;I. Lakkis
中科院分区：
文献类型：
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作者：
Samah El Mohtar;I. Hoteit;O. Knio;Leila Issa;I. Lakkis
拉格朗日跟踪被动示踪剂在随机速度场内的顺序集合数据同化框架是具有挑战性的，由于粒子数量的指数增长。这种增长源于将速度随时间的行为描述为一组不同实现的可能组合，在每个同化周期之前和之后。本文解决了问题，有效地平流粒子在随机流场，其统计规定的一个潜在的合奏，在一个并行计算框架（openMP）。为此，提出了一种有效的被动粒子在随机流场中的前向和后向跟踪算法。该算法采用高阶粒子平流方案，提出了一种控制粒子数量增长的机制。该机制使用自适应分箱过程，同时保留概率的零阶、一阶和二阶矩（总概率、平均位置和方差）。自适应分箱过程通过将粒子数量限制为所需的最大值，在速度和精度之间进行权衡。为了验证我们的方法，我们进行了各种向前和向后的粒子跟踪实验中的一个现实的高分辨率集合同化设置的红海，专注于粒子的最大数量的效果，时间步长，集合的方差，旅行时间，源的位置，和历史的运输。
Lagrangian tracking of passive tracers in a stochastic velocity field within a sequential ensemble data assimilation framework is challenging due to the exponential growth in the number of particles. This growth arises from describing the behavior of velocity over time as a set of possible combinations of the different realizations, before and after each assimilation cycle. This paper addresses the problem of efficiently advecting particles in stochastic flow fields, whose statistics are prescribed by an underlying ensemble, in a parallel computational framework (openMP). To this end, an efficient algorithm for forward and backward tracking of passive particles in stochastic flow-fields is presented. The algorithm, which employs higher order particle advection schemes, presents a mechanism for controlling the growth in the number of particles. The mechanism uses an adaptive binning procedure, while conserving the zeroth, first and second moments of probability (total probability, mean position, and variance). The adaptive binning process offers a tradeoff between speed and accuracy by limiting the number of particles to a desired maximum. To validate our method, we conducted various forward and backward particles tracking experiments within a realistic high-resolution ensemble assimilation setting of the Red Sea, focusing on the effect of the maximum number of particles, the time step, the variance of the ensemble, the travel time, the source location, and history of transport.