Particle tracking model results suggest little lateral transport bias in inorganic and organic SST proxies in the Mediterranean Sea

2021 ◽  
Author(s):  
Addison Rice ◽  
Peter Nooteboom ◽  
Erik van Sebille ◽  
Francien Peterse ◽  
Martin Ziegler ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Particle Tracking ◽  
Ocean Surface ◽  
Lateral Transport ◽  
Lagrangian Particle ◽  
Burial Site ◽  
Particle Tracking Model ◽  
Tracking Model ◽  
The Mediterranean ◽  
Paleoclimate Records

<p>Ocean currents can transport sinking particles hundreds of kilometers from their origin at the ocean surface to their burial location, resulting in an offset between sea surface temperatures (SSTs) above the burial site and the particle’s origin. Quantifying this offset in particles carrying molecules used in SST proxies can reduce uncertainty in paleoclimate reconstructions. In the Mediterranean Sea, where δ<sup>18</sup>O<sub>foraminifera</sub>, U<sup>K’</sup><sub>37</sub>- and TEX<sub>86</sub>-based SSTs can exhibit large offsets from surface conditions, understanding the possible contribution of lateral transport to proxy bias can provide additional insight when interpreting paleoclimate records.</p><p>In this study, Lagrangian particle tracking experiments are performed using the NEMO flow field to simulate transport and allow for a quantitative estimate of transport bias. The model determines the ocean surface origin locations of foraminifera and sedimentary particles that carry alkenones or GDGTs to compare with surface sediment datasets for δ<sup>18</sup>O<sub>foraminifera</sub>, U<sup>K’</sup><sub>37</sub> and TEX<sub>86</sub>, respectively. A range of sinking speeds appropriate for the export of organic matter (6, 12, 25, 50, 100, 250, and 500 m/d) is used in the model to represent different export modes (i.e., individual coccoliths, coccospheres, aggregates), where the three fastest sinking speeds can also represent sinking foraminifera. Results show that lateral transport bias is generally small within the Mediterranean Sea and cannot explain the large offsets in proxy-based SST reconstructions in this basin.</p>

Transient Electrophoretic Motion of Charged Particles Through an L-Shaped Microchannel

2009 ◽  
Author(s):  
Ye Ai ◽  
Seungkyung Park ◽  
Junjie Zhu ◽  
Xiangchun Xuan ◽  
Ali Beskok ◽  
...  
Keyword(s):  
Particle Tracking ◽  
Particle Trajectory ◽  
Charged Particles ◽  
Quantitative Agreement ◽  
Uniform Electric Field ◽  
Lagrangian Particle Tracking ◽  
Lagrangian Particle ◽  
Outer Corner ◽  
Particle Tracking Model ◽  
Tracking Model

Direct current dielectrophoretic (DC-DEP) effects on the electrophoretic motion of charged polystyrene particles through an L-shaped microchannel were experimentally and numerically studied. In addition to the electrostatic and hydrodynamic forces, particles experience a negative DC-DEP force arising from the interaction between the dielectric particle and the induced spatially non-uniform electric field occurring around the corner of the L-shape microchannel. The latter force causes a cross-stream DEP motion so that the particle trajectory is shifted towards the outer corner of the turn. A two-dimensional (2D) Lagrangian particle tracking model taking into account the induced DC-DEP effect was used to predict the particle trajectory shift through the L-shaped channel, which achieves quantitative agreement with the experimental data.

The wind-induced bias of the Thies Laser Precipitation Monitor obtained using CFD and a Lagrangian particle tracking model

2021 ◽  
Author(s):  
Enrico Chinchella ◽  
Arianna Cauteruccio ◽  
Mattia Stagnaro ◽  
Luca G. Lanza
Keyword(s):  
Size Distribution ◽  
Particle Tracking ◽  
Drop Size ◽  
Shielding Effect ◽  
Lagrangian Particle Tracking ◽  
Measurement Instruments ◽  
Lagrangian Particle ◽  
Precipitation Measurement ◽  
Particle Tracking Model ◽  
Tracking Model

<p>Environmental sources of measurement biases affect the accuracy of non-catching (mostly contact-less) precipitation gauges (Lanza et al., 2021). Wind is among the most significant influencing variables, since instruments exposed to the wind generate strong airflow velocity gradients and turbulence near their sensing volume. Hydrometeor trajectories are diverted by the induced updraft/downdraft and acceleration near the instrument, affecting the measured particle size distribution, and leading to an over- or underestimation of the precipitation intensity. This bias is common to all precipitation measurement instruments, including traditional catching-type gauges, but is amplified in non-catching gauges due to their complex shapes and measuring principles. Wind also changes the velocity of the falling hydrometeors, introducing further potential biases since velocity is explicitly used by disdrometers (in combination with the hydrometeors size) to determine the type of precipitation and to discard outliers.</p><p>The present work focuses on the Thies laser precipitation monitor, which employs a laser beam to detect hydrometeors in fight. It has a complex, non-axisymmetric shape, due to the physical constraints of its measuring principle. To evaluate the effect of wind on liquid precipitation measurements, Computational Fluid Dynamics simulations were run, using OpenFOAM, together with a Lagrangian particle tracking model. The drag coefficient formulation validated by Cauteruccio et al. (2021) was implemented in the OpenFOAM package. Various drop diameters were considered (0.25, 0.5, 0.75 and from 1 to 8 mm in 1 mm increments), and for each drop size, the vertical and horizontal velocity components were set equal to the terminal velocity and the free-stream velocity, respectively. Nine angles of attack were considered, from 0° to 180°, in 22.5° increments. For each angle, five different wind speed values (2, 5, 10, 15 and 20 m/s) were simulated. Each combination was run twice, first using a constant velocity field (as if the instrument were transparent to the wind) to evaluate the sole shielding effect of the instrument body on the measurement section, and then using the effective velocity fields.</p><p>The data were then processed, using a suitable drop size distribution and for each velocity/angle/rainfall intensity combination the collection efficiency of the instrument was calculated. This work is funded as part of the activities of the EURAMET project 18NRM03 – “INCIPIT – Calibration and Accuracy of Non-Catching Instruments to measure liquid/solid atmospheric precipitation”.</p><p><strong>References:</strong></p><p>Lanza L.G., Merlone A., Cauteruccio A., Chinchella E., Stagnaro M., Dobre M., Garcia Izquierdo M.C., Nielsen J., Kjeldsen H., Roulet Y.A., Coppa G., Musacchio C., Bordianu C., 2021: Calibration of non-catching precipitation measurement instruments: a review. J. Meteorological Applications (submitted).</p><p>Cauteruccio A, Brambilla E, Stagnaro M, Lanza LG, Rocchi D, 2021: Wind tunnel validation of a particle tracking model to evaluate the wind-induced bias of precipitation measurements. Water Resour. Res., (conditionally accepted).</p>

Computer Simulation of the Inclusion Removal Behavior in the Tundish and Model Evaluation

2014 ◽  
Vol 513-517 ◽  
pp. 4314-4318
Author(s):  
Yun Wang
Keyword(s):  
Computer Simulation ◽  
Random Walk ◽  
Particle Tracking ◽  
Random Walk Model ◽  
Motion Simulation ◽  
Lagrangian Particle Tracking ◽  
Lagrangian Particle ◽  
Inclusion Removal ◽  
Particle Tracking Model ◽  
Tracking Model

Computer simulation was applied to study the inclusion behavior in the tundish with Lagrangian particle tracking model including both the deterministic and random walk model. Comparing with the experiment result, the prediction result revealed that random walk model obtained more accuracy than the deterministic walk model by considering the effect of turbulence fluctuation on the inclusion movement. The accuracy of inclusion motion simulation was determined by the turbulence model and boundary condition.

scholarly journals Integration of GIS and a Lagrangian Particle-Tracking Model for Harmful Algal Bloom Trajectories Prediction

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 164 ◽  
Author(s):  
Rufu Qin ◽  
Liangzhao Lin
Keyword(s):  
Particle Tracking ◽  
Algal Bloom ◽  
Red Tide ◽  
Harmful Algal Bloom ◽  
Lagrangian Particle Tracking ◽  
Lagrangian Particle ◽  
Particle Tracking Model ◽  
Tracking Model ◽  
Gis Environment ◽  
Short Term Forecasting

Harmful algal bloom (HAB) is a major environmental problem in coastal waters around the world. The technologies and approaches for short-term forecasting of the HABs trajectories have obtained increasing attention from researchers. In this paper, we present a straightforward physical-based model based on a non-Fickian Lagrangian particle-tracking scheme for understanding the movement of detected HABs. The model adopts the fractional Brownian motion (fBm) technology, and is coupled with the Delft3D and WRF models and GIS. The fBm based Lagrangian particle-tracking model can flexibly control the scale of the particle clouds diffusion through Hurst value, which can be used to account for uncertainties and adjust for better representing the trajectories of HABs. Simulation results demonstrate that the presented model can successfully predict the trends and the main features of red tide drifting. The developed simulation tool enables users to create the model configuration, manage data inputs, run the model, and generate model maps and animations within a GIS environment. It is believed that the model and the tool outlined herein can be very useful for rapidly evaluating potential areas at risk from the HABs events.

Coupling particle tracking model and satellite data for trajectories prediction

2021 ◽  
Author(s):  
Chaojie Li ◽  
Daniel Odermatt ◽  
Damien Bouffard ◽  
Johny Wüest ◽  
Tamar Kohn
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Particle Tracking ◽  
Lake Geneva ◽  
Lagrangian Particle Tracking ◽  
Remote Sensing Images ◽  
Deep Lakes ◽  
Lagrangian Particle ◽  
Particle Tracking Model ◽  
Tracking Model

<p>Various physical, chemical and biological processes take place three- dimensionally in deep lakes, regulated by complex boundary conditions. Propelled by the rapid development of equipment, technology and computational power, the understanding of deep lakes has steadily advanced. In particular hydrodynamic monitoring and simulation studies have benefitted from combining field observation, numerical simulation and other emerging techniques such as remote sensing. In contrast, water quality parameters are less well investigated by this combination of tools. In this study, we integrate remote sensing techniques with a Lagrangian particle tracking model for lake water quality simulations. Specifically, our goal was to establish a successive individual-based model for health-relevant microorganisms in Lake Geneva. To this end, we combined remote sensing images from the current Sentinel 2 and Sentinel 3 satellites and Delft3D hydrodynamic and particle tracking models. Total suspended matter (TSM), which can both be detected by satellites and simulated by numerical models, is chosen as a parameter of concern. Concentration of TSM in Lake Geneva deduced from remote sensing images is used as observation to compare with particle tracking simulation to support the validation of the numerical model. On the other hand, the model allows to bridge gaps in satellite observations due to cloud coverage. Point source releasing and lake-wide dynamic pattern of TSM are employed as scenario studies to indicate the validation of our particle tracking model, focusing on time spans between 1 to 10 days. Our findings demonstrate that remote sensing images can serve to calibrate and validate the particle tracking water quality model, and in return, the particle tracking model provides the possibilities for data inference and interpolation between satellite images. The flexibility of the Lagrangian particle tracking method poses more possibilities to incorporate flow independent movement, mortality and growth of micro-organisms. It is expected that a more universal and accurate tool for water quality simulation can be created which will facilitate decision making.</p>

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