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108 results for “wind modelling”
Documentation for "Modelling wind farm effects in HARMONIE-AROME"
<p>This archive provides the configuration files for the Weather Research and Forecasting (WRF) and the wind farm information for HARMONIE-AROME as well as the scripts to repoduce the simulations and analysis in "Modelling wind farm effects in HARMONIE-AROME".</p>
A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps
<p>Deployment of wind energy is proposed as a mechanism to reduce greenhouse gas emissions. Yet, wind energy and large birds, notably soaring raptors, both depend on suitable wind conditions. Conflicts in airspace use may thus arise between wind energy development and wildlife protection due to the risks of collisions of birds with the blades of wind turbines. Using locations of GPS-tagged bearded vultures, a rare scavenging raptor reintroduced into the Alps, we built a spatially-explicit model to predict potential areas of conflict with future wind turbines deployments in the Swiss Alps. We modelled the probability of bearded vultures flying within or below the rotor-swept zone of wind turbines as a function of wind and environmental conditions, including food supply (presence of wild ungulates). Flight activity at potential risk of collision was generally high, concentrating on south-exposed mountainsides, especially in areas where ibex carcasses have a high occurrence probability, with critical areas covering vast expanses throughout the Swiss Alps. Our model provides a spatially-explicit decision tool that will guide authorities and energy companies for planning the deployment of wind farms in a proactive manner to reduce risk to emblematic Alpine wildlife.</p>
Processed model output and observational products used in `Observed winds crucial for September Arctic sea ice loss'
<p>Processed model output from wind-nudging experiments used to investigate Arctic sea ice loss. Also includes processed observational data shown in the manuscript.</p> <p> </p> <p>For further details, see </p> <p>Roach, L. A and Blanchard-Wrigglesworth E. (2022). Observed winds crucial for September Arctic sea ice loss. Accepted at Geophysical Research Letters</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>
Brazil-Offshore Wind Model
<p><strong>Installation and running the model</strong></p> <p>It is necessary to install Calliope to run the model. Instructions for installation and running the model are available at:<a href="https://calliope.readthedocs.io/">https://calliope.readthedocs.io/</a>.</p> <p><strong>Temporal resolution</strong></p> <p>The temporal resolution of the model is 6 hours by default. You can set the model with another resolution in the "overrides" file: </p> <p>time_resampling:</p> <p> model.time: {function: resample, function_options: {'resolution': '6H'}}</p> <p>Note that running the model might be computationally expensive. The full model contains one year of data. To test the model, specify a shorter time subset in the "overrides" file > weather years. For instance, over ten days of data:</p> <p> year_2010:<br> model.subset_time: ['2010-01-01', '2010-01-10']</p> <p> </p> <p><strong>Scenarios</strong></p> <p>The scenario names are structured as follows: bias correction factor case + scenario name+ weather year.</p> <p>Example:</p> <p>low_baseline_2019</p> <p> </p> <p><em>Bias correction factor case:</em></p> <p>Low: represents the 25<sup>th</sup> percentile of bias correction factor at farm level aggregated by state;</p> <p>Median: represents the 50<sup>th</sup> percentile of bias correction factor at farm level aggregated by state;</p> <p>Up: represents the 75<sup>th</sup> percentile of bias correction factor at farm level aggregated by state;</p> <p> </p> <p><em>Scenario name</em></p> <p>baseline: status quo;</p> <p>offshore wind farm capex reduction: capex is reduced by 10%, 30%, 50%, and 70%;</p> <p>natural gas prices: in gas low, the gas price is US$ 24.87, while in gas high, US$ 62.05;</p> <p>offshore wind farm capex reduction + natural gas price: capex reduction (10%,30%,50%, and 70%) combined with the high price of natural gas.</p> <p> </p> <p><em>Weather year</em></p> <p>Weather years include data from 2000 to 2019.</p>
Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models
<p>Dataset of the paper "Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models" published on Energies [1].</p> <p>[1] Lin, M., & Porté-Agel, F. (2019). Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models. <em>Energies</em>, <em>12</em>(23), 4574.</p>
Model generated open-closed field topology maps produced by ISSI team "Magnetic Open Flux And Solar Wind Structuring Of Interplanetary Space"
<p>Open and closed magnetic field topologies of the solar corona generated using four PFSS based coronal models (EUHFORIA, WSA, MULTI-VP, PSI-PFSS) and one full MHD coronal model (PSSI-MHD) using two different types of HMI-ADAPT magnetic field maps (with and without Active Regions (AR) added retrospectively). The magnetograms used are accessible here <a href="https://doi.org/10.5281/zenodo.10211762" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10211762</a> (Henney, Carl. ‘ADAPT Global Solar Magnetic Maps - 2010 Sep 18-20 (w/ & W/o Farside Active Region Input)’. Zenodo, 28 November 2023).</p> <p>These model outputs were generated by the ISSI team "Magnetic Open Flux And Solar Wind Structuring Of Interplanetary Space" and were used for the paper accessible on Arxiv via this link: https://arxiv.org/abs/2311.04024</p>
Considerations for high-resolution regional meteorological wind modelling over complex terrain: a typhoon case study for assessing forestry damage (data)
<p>This is the experiment data.</p> <p>The Weather Research and Forecasting (WRF) model is a popular and easily used as a numerical weather prediction (NWP) model, but configuring WRF to produce accurate results can be time-consuming. This is especially so when simulating extreme events, over complex terrain, or at high resolutions. In this study, a strong wind event from Tropical Cyclone (TC) Thad in year 1981 was simulated at 200 m resolution over an experiment forest in a mountainous region of Hokkaido island, Japan. The simulation configuration is challenging, in order to cover a larger area to produce a TC with appropriate track and intensity, and at the same time to resolve the smallest domain of sub-km grid spacing with computational stability. A mixed nesting method was applied with two-way nesting up for the first three domains, followed a separate simulation over the smallest domain. The mixed method could produce 10 min wind speed distributions similar to that of the full simulation with two-way nesting of all four domains, if a 30-minute boundary update interval was used for the separate simulation. Mixed nesting improves the efficiency of the simulation process, since the larger phenomenon scale and smaller human impact scale can be tuned separately. </p>
Data-driven surrogate model for wind turbine damage equivalent load
<p>There are four zip files in this data set:</p> <ul> <li>PythonCode_OpenFAST: The code used to generate 32768 OpenFAST fst files to build the database.</li> <li>ML_TrainingCode: The code that used to train the TCN-FCNN and FCNN models for both free stream and wake</li> <li>Trained_Models: All the trained models are saved in Keras format. The models with max in their filenames were trained on maximum values. The models with XY in their naming were trained on wind in the X and Y directions.</li> <li>data: It includes all the CSV files for training and testing.</li> </ul>
Modeling the early evolution of massive OB stars with an experimental wind routine. The first bi-stability jump and the angular momentum loss problem
<p>MESA run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017A&A...598A...4K">Keszthelyi et al. (2017)</a>. MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.1051/0004-6361/201629468">10.1051/0004-6361/201629468</a></p>
Dataset for "Estimating the offshore wind power potential of Portugal by utilizing gray-zone atmospheric modeling" article
<p>This dataset is used for analysis and visualization, that supports the article titled "Estimating the offshore wind power potential of Portugal by utilizing gray-zone atmospheric modeling", which has been accepted for publication in the Journal of Renewable Sustainable Energy.</p>
FESOM output supporting: Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?
<p>AWI-CM1 and FESOM1.4 simulation results used in the manuscript "Atmospheric wind biases: A challenge for simulating the Arctic Ocean in coupled models?".</p>
Corrigendum of "Assessment of the Elliptic Blending RSM Turbulence Model on a Wind Turbine-Type Rotor Under Mildly Compressible Blade Tip Flow Regime" and updated dataset
<p>This data accompanies the paper "Assessment of the Elliptic Blending RSM Turbulence Model on a Wind Turbine-Type Rotor Under Mildly Compressible Blade Tip Flow Regime", published in the 18th Brazilian Congress of Thermal Sciences and Engineering (2020): <a href="http://www.sistema.abcm.org.br/articleFiles/download/28958">http://www.sistema.abcm.org.br/articleFiles/download/28958</a>. It includes the corrected integral load values described in the PDF document.</p>
CESM 1.2 climate model simulation output for: The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model 'Wind Stress Shaving' Experiments
<p>Westerly wind bursts (WWBs)—brief but strong westerly wind anomalies in the equatorial Pacific—are believed to play an important role in El Niño Southern Oscillation (ENSO) dynamics, but quantifying their effects is challenging. Here, we investigate the cumulative effects of WWBs on ENSO characteristics, including the occurrence of extreme El Niño events, via modified coupled model experiments within Community Earth System Model (CESM1) in which we progressively reduce the impacts of wind stress anomalies associated with model-generated WWBs. In these "wind stress shaving" experiments we limit momentum transfer from the atmosphere to the ocean above a preset threshold, thus "shaving off" wind bursts. To reduce the tropical Pacific mean state drift, both westerly and easterly wind bursts are removed, although the changes are dominated by WWB reduction. As we impose progressively stronger thresholds, both ENSO amplitude and the frequency of extreme El Niño decrease, and ENSO becomes less asymmetric. The warming center of El Niño shifts westward, indicating less frequent and weaker Eastern Pacific (EP) El Niño events. Removing most of wind bursts-related wind stress anomalies reduces ENSO amplitude by 22%. The essential role of WWBs in the development of extreme El Niño events is revealed in the suppressed eastward migration of the western Pacific warm pool and hence a weaker Bjerknes feedback under wind shaving. Overall, our results reaffirm the importance of WWBs in shaping the characteristics of ENSO and its extreme events and imply that WWB changes with global warming could influence future ENSO.</p>
supplementary to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" , 2D wind forcing
<p>This is a supplementary material to article "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" ( unpublished at the publication date of this dataset)</p> <p>It contains the 2D wind forcing for Crocus-SnowPappus simulations on the Grandes Rousses test zone. It was generated using DEVINE wind downscaling method ( Le Toumelin et al., 2022 )</p>
Data set used in article: On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach
<p>Step-wise pitch simulation of two wind turbines interacting using SOWFA. More information in the paper.</p>
Hybrid Machine Learning Model for Ultra-Short-Term Wind Power Forecasting with Multi-Model Training Approach
<p>This is the core data code of the "<strong>Hybrid Machine Learning Model for Ultra-Short-Term Wind Power Forecasting with Multi-Model Training Approach".</strong></p>
A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps
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Global coastal wind hazard maps from the CHAZ tropical cyclone model
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CESM 1.2 climate model simulation output for: The Essential Role of Westerly Wind Bursts in ENSO Dynamics and Extreme Events Quantified in Model 'Wind Stress Shaving' Experiments
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