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S El Mohtar, I Hoteit, O Knio, L Issa, I Lakkis
S El Mohtar, I Hoteit, O Knio, L Issa, I Lakkis
Ocean Modelling 131, 1-14
Ocean Modelling 131, 1-14
Highlights
Highlights
We present a new method for forward/backward Lagrangian tracking in stochastic velocity fields.
This is done within a sequential (50 members) ensemble data assimilation framework.
Growth in number of particles is capped using an adaptive binning algorithm, which conserves 0,1, 2 moments of probability.
The variance in particles positions due to binning is adaptively controlled; high probability regions have low variance.
Using the parallel algorithm, source recovery in a forward/backward experiment is within 40 km using only 50 M. elements
Abstract
Abstract
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.
I Hoteit, Y Abualnaja, S Afzal, B Ait-El-Fquih, T Akylas, C Antony, ...
I Hoteit, Y Abualnaja, S Afzal, B Ait-El-Fquih, T Akylas, C Antony, ...
Bulletin of the American Meteorological Society 102 (1), E99-E122
Bulletin of the American Meteorological Society 102 (1), E99-E122
Abstract
The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.
MAER Hammoud, I Lakkis, O Knio, I Hoteit
MAER Hammoud, I Lakkis, O Knio, I Hoteit
Ocean Engineering 220, 108435
Ocean Engineering 220, 108435
Highlights
Highlights
Tracking particles continuously released from moving sources in an uncertain flow.
Propose a novel method for source identification of pollutants in an uncertain flow.
Successful identification of polluting ships in the Mediterranean Sea.
Abstract
Identifying marine pollutant sources is essential to assess, contain and minimize their risk. We propose a Lagrangian Particle Tracking algorithm (LPT) to study the transport of passive tracers advected by an uncertain flow field described by an ensemble of realizations of the ocean currents, and to identify the source parameters of the release in backward mode. Starting from a probability map describing the distribution of a pollutant, reverse tracking is used to generate probabilistic inverse maps by integrating it with the ensemble of flow fields backward in time. An objective function based on the probability-weighted distance between the resulting inverse maps and the source trajectory is then minimized to identify the likely source of pollution. We conduct numerical experiments to demonstrate the efficiency of the proposed algorithm in the Mediterranean Sea. Passive tracers are released along the path of a ship and propagated with an ensemble of realistic flow fields to generate a probability map, which is then used for the inverse problem of source identification. Our results suggest that the proposed algorithm captures the release time and source of pollution, successfully pinpointing to the release parameters up to two weeks back in time in certain case studies.
S El Mohtar, B Ait-El-Fquih, O Knio, I Lakkis, I Hoteit
S El Mohtar, B Ait-El-Fquih, O Knio, I Lakkis, I Hoteit
Marine pollution bulletin 169, 112514
Marine pollution bulletin 169, 112514
Highlights
Highlights
A Bayesian approach to oil spill source identification from oil contours is presented.
Full and partial images of oil slicks are used to identify the source of a spill.
Good estimates of the test spills' location, time, duration and quantity are obtained.
Confidence in the estimated source parameters is assessed through the posterior.
Abstract
Oil spills at sea pose a serious threat to coastal environments. Identifying oil pollution sources could help to investigate unreported spills, and satellite imagery can be an effective tool for this purpose. We present a Bayesian approach to estimate the source parameters of a spill from contours of oil slicks detected by remotely sensed images. Five parameters of interest are estimated: the 2D coordinates of the source of release, the time and duration of the spill, and the quantity of oil released. Two synthetic experiments of a spill released from a fixed point source are investigated, where a contour is fully observed in the first case, while two contours are partially observed at two different times in the second. In both experiments, the proposed method is able to provide good estimates of the parameters along with a level of confidence reflected by the uncertainties within.
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