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34 results for “Advection”
Advective nitrate fluxes, sea surface chlorophyll concentrations and other physical metrics in the Santa Barbara Channel (2012-2019)
This data package includes 6 files: (1 & 2) In-situ nitrate concentrations at the surface and mixed layer depth, and collocated remotely-sensed and reanalysis quantities of satellite sea surface temperature, 15-day cumulative wind stress, satellite sea surface chlorophyll with a 5-day lag, index of offshore position of the California Current, indices for along-channel and across-channel distance, and index for day of the year. (3) An R script for generating generalized additive models (GAMs) to predict nitrate concentrations at the surface and at the mixed layer depth using the collocated data in files 1 & 2. (4) Daily maps of satellite sea surface chlorophyll concentrations (SSChl), High-frequency radar (HFR) surface currents, weather research and forecasting (WRF) model wind-derived vertical velocities, estimated nitrate concentrations at the surface and mixed layer depth, horizontal advective nitrate fluxes at the surface and vertical advective nitrate fluxes. (5) Daily time series of spatial mean SSChl, principal component amplitude of the first mode of variability in surface currents estimated using complex empirical orthogonal function (EOF) analysis, alongshore pressure gradient, wind stress, spatial mean horizontal velocities at the western and eastern Santa Barbara Channel boundaries, spatial mean vertical velocities, spatial mean surface nitrate concentrations at the channel boundaries and across the entire channel, spatial mean mixed layer depth nitrate concentrations across the entire channel, spatial mean horizontal advective nitrate fluxes at the channel boundaries, and spatial mean vertical advective nitrate fluxes. (6) A MATLAB script for plotting examples of the daily maps and time series in files 4 & 5. These data were processed in order to investigate the impact of local nutrient delivery mechanisms on phytoplankton blooms in the Santa Barbara Channel, California, details of which are available in the study: Brokaw, R.J., D.A. Siegel, L. Washburn,
Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Advection datasets from the paper:<br> Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - AdvBox<br> - AdvInBox<br> - AdvTaylor<br> - AdvCircle<br> - AdvCircleAng<br> - AdvSquare<br> - AdvEllipseH<br> - AdvEllipseV<br> - AdvSpline<br> - AdvSquareAndCircle<br> - Adv3Circles</p> <p>Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre> <p><br> </p>
Data from Large-eddy simulation data of stratocumulus advecting over cold and warm waters
<p>Data from Large-eddy simulation data of stratocumulus advecting over cold and warm waters. The LES model used is the System for Atmospheric Modeling (SAM) model (http://rossby.msrc.sunysb.edu/~marat/SAM.html). </p>
Time dependence of advection-diffusion coupling for nanoparticle ensembles
<p>Data appearing in the figures of the article DOI:10.1103/PhysRevFluids.6.064201.</p>
Data, plotting scripts, and figures for "A Projective Method for Solving the Single-Group Space-Time Neutron Kinetics Equations with Precursor Advection"
<p>Contains all the files necessary for figure reproduction.</p>
Data produced and used in the publication "Modelling chemical advection during magma ascent" by Dominguez et al., 2024.
<p>Data produced by the numerical models used for the study titled "Modelling chemical advection during magma ascent", by Dominguez et al., 2024. The code used to produce the data is available at https://zenodo.org/records/12624639.</p> <p>Each file contains the data of one numerical model store as HDF5, with 4 timesteps and the initial conditions.</p>
A case study: evaluation of a single column model with advection to simulate fog/stratus during C-FOG experiment
<p>Those datasets are observed by C-FOG (<em>Toward Improving Coastal Fog Prediction</em>) campaign, which was organized as a field experiment combined with modelling and theoretical initiatives. The objective of C-FOG was to advance our understanding and ability to observe, simulate, and predict fog, with a particular focus on warm fog formation, development and dissipation over coastal environments.</p> <p>The uploaded observation data contains liquid water content, droplet number concentration, temperature, SST, wind, visibility, backscatter collected by ceilometer, and atmospheric profile. The details can be found in the dataset.</p> <p>Thanks for the intense observation by the C-FOG project, which collected valuable data for detailed fog research.</p>
Numerical study on advective fog formation and its characteristic associated with cold water upwelling
<p>Recent rapid industrial development in the Korean Peninsula has increased the impacts of meteorological disasters on marine and coastal environments. In particular, marine fog driven by summer cold water masses can inhibit transport and aviation; yet a lack of observational data hinders our understanding. The present study aimed to analyze the differences in cold water mass formation according to sea surface temperature (SST) resolution and its effects on the occurrence and distribution of sea fog over the Korean Peninsula from June 23–July 1, 2016, according to the Weather Research and Forecasting model. Data from the Final Operational Model Global Tropospheric Analyses were provided at 1° and 0.25° resolutions and NOAA real-time global SST (RTG-SST) data were provided at 0.083°. While conventional analyses have used initial SST distributions throughout the entire simulation period, small-scale, rapidly developing oceanic phenomena (e.g., cold water masses) lasting for several days act as an important mediating factor between the lower atmosphere and sea. RTG-SST was successful at identifying fog presence and maintained the most extensive horizontal distribution of cold water masses. In addition, it was confirmed that the difference in SST resolution led to varying sizes and strengths of the warm pools that provided water vapor from the open sea area to the atmosphere. On examining the horizontal water vapor transport and the vertical structure of the generated sea fog using the RTG-SST, water vapors were found to be continuously introduced by the southwesterly winds from June 29 to 30, creating a fog event throughout June 30. Accordingly, high-resolution SST data must be input into numerical models whenever possible. It is expected that the findings of this study can contribute to the reduction of ship accidents via the accurate simulation of sea fog.</p>
Data and supplementary files for: Directing Min protein patterns with advective bulk flow
<p>Simulation files as well as experiment data and analysis for the paper "Directing Min protein patterns with advective bulk flow".</p>
Dataset for "Surface-Atmosphere Decoupling Prolongs Cloud Lifetime Under Warm Advection Due To Reduced Entrainment Drying"
<p>Dataset for "Surface-Atmosphere Decoupling Prolongs Cloud Lifetime Under Warm Advection Due To Reduced Entrainment Drying". The LES model used is the System for Atmospheric Modeling (SAM) model (http://rossby.msrc.sunysb.edu/~marat/SAM.html). </p>
Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679).</p> <p>The training code is available at https://github.com/mario-linov/graphs4cfd.</p>
Hampering Effect of Wave-Driven Turbulence on Advection Speed and Diffusion Rate of Pollutants: A Large Eddy Simulation Study
<p>The source codes and namelist files are prepared to reproduce the results of the study entitled 'Hampering Effect of Wave-Driven Turbulence on Advection Speed and Diffusion Rate of Pollutants: A Large Eddy Simulation Study'. Download the originial source codes for the Parallelized LES model (PALM) at <a href="https://palm.muk.uni-hannover.de">https://palm.muk.uni-hannover.de</a> and use the source codes provided here as 'user-defined code' in PALM.</p> <p>The data can be used to reproduce the figures of the scientific article named after the study.</p>
Data from: Interplay between wind-driven advection and mixing of salt and dissolved oxygen in a microtidal estuary
<p>Most work on how estuarine dynamics impact dissolved oxygen (DO) distributions has focused on tides as the primary mixing mechanism, but in shallow estuaries with large fetch or small tides, wind can be the primary mixing agent and also drives advection. To investigate how these processes interact and affect DO distributions, an observational study was conducted in the shallow, micro-tidal Neuse Estuary (NRE). Salinity, DO, and velocity profiles were measured at multiple positions along and across the estuary over a 6-month period. A one-dimensional model (General Ocean Turbulence Model) provided additional insight into the response of salinity and DO to wind. Salinity and oxygen conservation equation terms were calculated from observations and simulations to investigate the roles of advection and mixing under different conditions. Cross-estuary wind drove lateral circulations and tilted the isohalines, reducing stratification; lateral advection and enhanced vertical mixing reduced vertical gradients and increased the bottom DO. Down-estuary wind tended to increase the exchange flow and increase stratification, but concurrently the wind-driven surface turbulent boundary layer deepened over time. The balance of these processes determined if the water column became fully mixed or remained stratified, and the depth of the pycnocline and oxycline. Up-estuary wind inhibited the exchange flow and ultimately the combination of advection and vertical mixing homogenized the water column. While these patterns generally held for purely across- or along-channel wind, the response was often more complex because the wind vector could have any orientation and wind speed and direction varied continuously with time.</p>
GAIA model simulate data of doubled CO2 (Forces, advections, and winds)
<p>This dataset contains forces, advections, and winds output from the GAIA model, that are related to the Figures in the paper. The forces and advections are divided by the Coriolis parameter or a zonal mean absolute vorticity. </p>
Third-order momentum advection on the quasi-hexagonal C-grid on the sphere: Data and source code
<p>This upload contains data and source code accompanying the paper submitted to JAMES (Journal of advances in modeling Earth systems) under the title 'Third-order momentum advection on the quasi-hexagonal C-grid on the sphere'</p> <p>See README files for further details.</p>
Additional data for: Directing Min protein patterns with advective bulk flow
<p>Data and analysis associated with 2024 addition to:<br>"Direction Min protein patterns with advective bulk flow" (NatComms, 2023)<br>Companion repository to <a href="../records/7339803">10.5281/zenodo.7339803</a></p> <p>Please consider the readme for more information.</p>
Datasets for "LAPS v1.0.0: Lagrangian Advection of Particles at Sea, a Matlabprogram to simulate the displacement of particles in the ocean."
<p>The data, run results and analysis scripts used to produce all of the results presented in the paper "LAPS v1.0.0: Lagrangian Advection of Particles at Sea, a Matlab program to simulate the displacement of particles in the ocean."</p>
ADVEX - The CarboEurope-Integrated Project Advection Experiment Data Set
<p>Extensive field measurements have been performed at three CarboEurope-Integrated Project forest sites with different topography (Renon/Ritten, Italian Alps, Italy; Wetzstein, Thuringia, Germany; Norunda, Uppland, Sweden) to evaluate the relevant terms of the carbon balance by measuring CO2 concentrations [CO2] and the wind field in a 3D multitower cube setup. The same experimental setup (geometry and instrumentation) and the same methodology were applied to all the three experiments. Refer to Feigenwinter et al. (2008), <a href="https://doi.org/10.1016/j.agrformet.2007.08.013">https://doi.org/10.1016/j.agrformet.2007.08.013</a> for more details of the ADVEX campaign.</p>
Data from: Interplay between wind-driven advection and mixing of salt and dissolved oxygen in a microtidal estuary
Open the record for dataset details and reuse information.
Estimates of surface layer net community production based on underway Lagrangian measurements of the dissolved O2/Ar ratio using Equilibrator Inlet Mass Spectrometry (EIMS), based on both steady-state and non-steady-state assumptions of the mixed-layer biological oxygen budget. Also included are estimates of the potential contribution of vertical fluxes: advection, eddy diffusion, and entrainment.
The ratio of dissolved oxygen to argon in surface seawater is frequently employed to estimate rates of net community production (NCP) in the oceanic mixed layer. The in situ O2/Ar-based method accounts for many physical factors that influence oxygen concentrations in the surface ocean, permitting isolation of the biological oxygen signal produced by the balance of photosynthesis and respiration. However, this technique traditionally relies upon several assumptions when calculating the mixed layer O2/Ar budget, most notably the absence of vertical fluxes of O2/Ar and the existence of a steady-state balance between net productivity and the air-sea gas exchange of biological oxygen. Employing a Lagrangian study design and leveraging data outputs from a regional physical oceanographic model, we conducted in situ measurements of O2/Ar in the California Current Ecosystem in spring 2016 and summer 2017 to evaluate these assumptions within a ‘worst-case’ field environment. Quantifying the magnitude of vertical fluxes and comparing NCP estimates obtained using steady-state versus non-steady-state assumptions, we find the importance of the non-steady-state term to be considerable, also observing significant potential effects from vertical flux terms, particularly advection. Additionally, we observe strong diel variability in O2/Ar and calculated NCP rates at multiple stations. Our results reemphasize the importance of accounting for vertical fluxes when interpreting O2/Ar-derived NCP data as well as the potentially large effect of non-steady-state conditions, including diel cycles in surface O2/Ar that can bias interpretation of NCP data based on local productivity and the time of day at which measurements were made.
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