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26 results for “Impact flux”
Martian crater ages and crater counting - Does the impact flux of small and large asteroids varied through time on Mars, the Earth and the Moon?
<ul> <li>The SM_mars_crater_dating.xlsx table contains all the information used to date the 49 martian impact craters considered in this study (< 600 Ma). </li> </ul> <ol> <li>CRATER ID </li> <li>CRATER NAME</li> <li>DIAM KM </li> <li>LAT </li> <li>LONG </li> <li>DEPTH RIM KM </li> <li>DEPTH SURF KM </li> <li>DEPTH FLOOR KM </li> <li>NUMBER LAYER</li> <li>MORPHO EJECTA </li> <li>PRESERVATION </li> <li>COUNT AREA KM2: counting area from ejecta banket mapping </li> <li>COUNT AREA ASCI* KM2: counting area after removal of surfaces contaminated by secondary craters </li> <li>THRESHOLD AREA KM2: minimum size of Voronoi polygon area below which all associated detected craters are considered of secondary origin</li> <li>NB SEC: number of secondary craters dentified by ASCI </li> <li>PERCENT SEC</li> <li>NB CRAT 100M: total number of craters > 100 m detected by the CDA** on the CTX global mosaic*** over the counting area</li> <li>NB PRIM 100M: number of craters identified as primaries by ASCI</li> <li>TURNOFF DIAM KM: minimum crater diameter used to fit the crater-size frequency distribution (CSFD) with an isochron</li> <li>NB CRAT FIT: number of craters used to fit the CSFD with an isochron</li> <li>AGE GA: model age based on Hartmann (2005) chronology model**** and Michael et al. (2016) fitting technique*****</li> <li>AGE MAX GA</li> <li>AGE MIN GA</li> <li>N(1): equivalent number of accumulated craters >1km per km2</li> <li>N(1) MAX</li> <li>N(1) MIN</li> </ol> <p>*ASCI: Automatic Secondary Crater Identification: A. Lagain, K. Servis, G. K. Benedix, C. Norman, S. Anderson, P. A. Bland, Model Age Derivation of Large Martian Impact Craters, Using Automatic Crater Counting Methods, Earth and Space Science 8 (2) (2021). doi:10.1029/2020EA001598.</p> <p>**CDA: Crater Detection Algorithm: G. K. Benedix, A. Lagain, K. Chai, S. Meka, S. Anderson, C. Norman, P. A. Bland, J. Paxman, M. C. Towner, T. Tan, Deriving Surface Ages on Mars Using Automated Crater Counting, Earth and Space Science 7 (3) (2020). doi:10.1029/2019EA001005.</p> <p>*** CTX global mosaic: Context Camera global mosaic: J. L. Dickson, L. A. Kerber, C. I. Fassett, B. L. Ehlmann, A Global, Blended CTX Mosaic of Mars with Vectorized Seam Mapping: A New Mosaicking Pipeline Using Principles of Non-Destructive Image Editing, in: Lunar and Planetary Science Conference (2018), p. 2480.</p> <p>**** W. K. Hartmann, Martian cratering 8: Isochron refinement and the chronology of Mars, Icarus 174 (2) (2005) 294–320. doi:10.1016/j.icarus.2004.11.023.</p> <p>***** G. G. Michael, T. Kneissl, A. Neesemann, Planetary surface dating from crater size-frequency distribution measurements: Poisson timing analysis, Icarus 277 (2016) 279–285. doi:10.1016/j.icarus.2016.05.019.</p> <ul> <li>The crater_counting.csv table contains the location and size of impact craters used to derive the ages of the 49 craters younger than 600 Ma old presented in this study. </li> </ul>
Data and code: Disentangling the impact of Atlantic Niño on sea-air CO2 flux
<p>This repository share (1) post-processed data of Norwegian Earth system model version 2 (LM) and (2) codes for calculating carbon system from NorESM variables (MATLAB format). </p>
Modeling the recent drought and thinning impacts on energy, water and carbon fluxes in a boreal forest
<p>This dataset includes the data used for model calibration and validation, as well as the simulation files with accepted runs, which are available for the readers to re-generate the results of this work. The *.bin files are the data for driving the model and for calibration and validation. They are specifically in the format for the CoupModel. Therefore, to check the data the CoupModel software needs to be installed. </p> <p>Additionally, we provide the software for CoupModel, which the readers could install on local computers to check the simulations. For detailed instructions on how to run CoupModel, please visit the CoupModel website www.coupmodel.com.</p>
Climate Impacts of Parameterizing Subgrid Partitioning of Land Surface Heat Fluxes to the Atmosphere with the NCAR CESM1.2
<p>The modified code as well as the CAM5 output for all the simulations in this study (V0 for the CTL run, CON1 for the EXP run, and PCON1R for EXP_COR run).</p> <p>The CESM1.2.1-CAM5.3 source code can be downloaded through the CESM official website https://www.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html#download_ccsm_code. Its output files are named in V0*.nc.</p> <p>The modified code for the EXP run in the study is in CON1.tar, with its CAM5 output files named in CON1*.nc.</p> <p>The modified code for the EXP_COR run in the study is in PCON1R.tar, with its CAM5 output files named in PCON1R*.nc</p>
Meteorological and Ecosystem Flux Data for: Climate Change Impacts on Net Ecosystem Productivity in a Subtropical Scrubland of Northwestern México
<p>This dataset accompains the paper: Climate Change Impacts on Net Ecosystem Productivity in a Subtropical Scrubland of Northwestern México which is submitted for publication at the Journal of Geophysical Research - Biogeosciences </p> <p>With this data set, we calibrate and validate an ecohydrological and a soil carbon model to assess climate change impacts in a subtropical scrubland located in northwest México. Model calibration and validation is performed using five continuous years of water, energy and carbon flux measurements from an eddy covariance tower and remotely-sensed vegetation indices. </p>
Data presented in Dupont et al. 2024 "Impact of dust source patchiness on the existence of a constant dust flux layer during aeolian erosion events"
<p>Meteorological and dust data used in Dupont et al. 2024. Data are based on measurements recorded above two erosive surfaces from two very different desert regions, one at a high-latitude location in Iceland and the other at a low-latitude location in Jordan. The Iceland campaign was co-organized by two projects: FRAGMENT (FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe) and HiLDA (Iceland as a model for high-latitude dust sources – a combined experimental and <br>modeling approach for characterization of dust emission and transport processes); and the Jordan campaign (J-WADI: Jordan Wind erosion And Dust Investigation) was co-organized by the FRAGMENT and Helmholtz Young Investigator Group ``A big unknown in the climate impact of atmospheric aerosol: Mineral soil dust'' projects.</p>
Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </p>
Impact of binary interaction on the evolution of blue supergiants. The flux-weighted gravity luminosity relationship and extragalactic distance determinations
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2019A&A...621A..22F">Impact of binary interaction on the evolution of blue supergiants. The flux-weighted gravity luminosity relationship and extragalactic distance determinations</a></p>
Data for "Impact of prior terrestrial carbon flux on atmospheric CO2 concentration simulation"
<p>Data for "Impact of prior terrestrial carbon flux on atmospheric CO2 concentration simulation"</p>
Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere"
<p>Dataset and Python Scripts used in the manuscript "Global-MHD Simulations using MagPIE : Impact of Flux Transfer Events on the Ionosphere".</p> <p>Authors: Arghyadeep Paul, Antoine Strugarek and Bhargav Vaidya<br> Date: 21 May, 2023</p> <p> </p> <ul> <li>Figure 1 has been plotted from two data files named Bx_By_Bz_prs_t_4783_C0.vtk and Bx_By_Bz_prs_t_4783_C1.vtk using the visualisation toolkit VisIt. Visit can be freely downloaded from https://wci.llnl.gov/simulation/computer-codes/visit</li> <li>Figure 2 has been plotted using the ipython notebook named "figure_2.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 3 has been plotted using the data file named "visit_prs_Bx_By_Bz_t_4964s.vtk" and the visualisation toolkit VisIt.</li> <li>Figure 4 has been plotted using the associated data files and the visualisation toolkit VisIt.</li> <li>Figures 5 and 6 has been plotted using the ipython notebook named "new_fig_5_and_6.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 7 has been plotted using the ipython notebook named "figure_7.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 8 has been plotted using the ipython notebook named "figure_8.ipynb" and the associated data files have been uploaded alongwith.</li> <li>Figure 9 has been plotted using the ipython notebook named "figure_9.ipynb" and the associated data files have been uploaded alongwith. An example swarm CSV data file is added for the python script. The original SWARM data can be downloaded from <a href="https://swarm-diss.eo.esa.int/">https://swarm-diss.eo.esa.int/</a> and the FAC data from two spacecrafts, Swarm A and C, named SW_OPER_FAC_TMS_2F has been used. This data was first published in Dong et.al. 2023 [https://doi.org/10.1029/2022GL102460].</li> </ul>
Impacts of ocean wave-dependent momentum flux on global ocean climate
<p>This is dataset of ocean climate simulations used in the paper "Impacts of ocean wave-dependent momentum flux on global ocean climate" by Shimura et al. (2020, Geophysical Research Letters).</p> <p>The dataset contains results of four experiments; "expWAVE", "expWAVEctrl", "expWIND", "expWINDctrl". The "expWAVEctrl" and "expWINDctrl" are control experiments of "expWAVE" and "expWIND".</p> <p>The data "expXXX_ocean_month_YYYY_MM.nc.zip" contains monthly mean ocean temperature, u and v components of ocean current.</p> <p>The data "expXXX_ocean_surf_YYYY_MM.nc.zip" contains monthly mean surface heat flux, u and v components of momentum flux.</p>
Seasonally Varying Vegetation Impacts on Surface Fluxes (VegFlux)
<p>VegFlux introduced observationally based, seasonal variations in vegetation and bare-soil fractions into the Met Office Unified Model (UM) and quantify impacts on dust emission and moist processes, allowing improvement of climate projections for Africa.</p> <p>In the zipped folder (VegetFlux_data.tar.gz) uploaded here, the data is grouped into five folders containing each one of the following parameters: temperature (tewnt15m), precipitation (tewnpre), latent heat (tewnlhf), sensible heat (tewnshf) and vegetation fraction (vegfrac). In each folder, we have two types of simulation, one whose file names begin with tewnfa and the other whose file names begin with tewnha. The one begining with tewnfa is the simulation using fix vegetation fraction in the unified model (UM) and the tewnha using varying vegetation fraction. The timestep is 12 hours meaning two measures per day (00h and 12h) during five months (may to september).</p>
Short-term Regulation of Energy and Macronutrient Balance: Impact of Energy Flux
ClinicalTrials.gov study NCT03361566. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: New records of very high nitrous oxide fluxes from rice cannot be generalized for water management and climate impacts
Open the record for dataset details and reuse information.
Data for "Drought and Heatwave Impacts on Semi-Arid Ecosystems' Carbon Fluxes along a Precipitation Gradient"
<p>These are flux and meteo data for the measurement sites ES-Abr and ES-LMa for the period from 2016-01-01 to 2019-12-31.</p> <p>These data were used for the publication:</p> <p>El-Madany, et al. (2020) "Drought and Heatwave Impacts on Semi-Arid Ecosystems’ Carbon Fluxes along a Precipitation Gradient" in Philosophical Transactions of the Royal Society B.</p> <p>Variables, units and description are found in the ReadmeDataDescription.csv file</p>
Lorentzian filter correction of turbulence measurements on oscillating floating platforms: impact on wind spectra and eddy covariance fluxes
<p class="MsoTitle">Turbulence and eddy covariance measurements on a floating platform over water surfaces can be contaminated by platform oscillations, which may affect the calculated air–water exchange. The conventional method for decontamination of the platform oscillations from the wind velocity measurements requires the installation of an additional sensitive, and often costly, motion sensor. This paper examines a new mathematical decontamination method, termed Lorentzian filter, which avoids the need for such an instrument. The method, based on the Lorentzian function, capitalizes on the pseudo-harmonic behavior of the platform oscillations and reduces the amplitude of turbulent wind velocity data detected as artifacts at the specific natural frequencies of the platform. The Lorentzian filter was applied to wind velocity data measured by sonic anemometer and eddy covariance system over the Dead Sea, Israel, for 30 days. We examined three approaches of dealing with motion contamination: Lorentzian filter decontamination, motion sensor decontamination, and non-filtered raw wind velocity. Using the 3D wind velocity series, we examined the wind spectra, the co-spectra of water vapor concentration and horizontal wind speed with vertical wind speed and H2O and momentum fluxes. The Lorentzian filter performed very well in decontaminating the wind spectrum, meaning that it efficiently identified the contamination in the natural oscillation frequency and returned a decontaminated wind velocity time series. The co-spectra and fluxes were less prone to the contamination of platform oscillations, presumably due to low correlations between the spurious wind velocity components and other measured scalars, such as water vapor.</p>
Impact of Estuarine Dams on the Estuarine Parameter Space and Sediment Flux Decomposition: Idealized Numerical Modeling Study.
<p>Input data to run the model; output data and scripts used to generate figures.</p>
Lorentzian filter correction of turbulence measurements on oscillating floating platforms: impact on wind spectra and eddy covariance fluxes
Open the record for dataset details and reuse information.
The impacts of sub-grid physical processes on SEP air-sea fluxes
<p>Model outputs from CAM5_CLUBB when parameters of ZM deep convection and CLUBB scheme are disturbed in SEP, ITCZ and SPCZ, respectively. And observations of surface energy fluxes. </p>
Absence of KpsM (Slr0977) impairs the secretion of extracellular polymeric substances (EPS) and impacts carbon fluxes in Synechocystis sp. PCC 6803
GEO Series GSE165073. Synechocystis sp. PCC 6803. 6 samples. Type: Expression profiling by high throughput sequencing.
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