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238 results for “Atmosphere modeling”
Transcriptomic analysis to underly the heterogeneity between 4 cellular models derived from patients diagnosed with pediatric high-grade gliomas under controlled atmosphere (modulation of oxygen level
GEO Series GSE101799. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model"
<p>Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model" in JGR-Atmospheres (2020). </p> <p>Output are NetCDF files containing annual means (named 'yearmean', 2007-2013), or multi-annual monthly means ('ymonmean', 2007-2012) of various variables that are of interest and/or used for analysis in this study. The file name starts with the variable name. Fields are global, at a resolution of 0.9 x 1.25 degrees latitude/longitude.</p> <p>The test simulations are named (as discussed in the paper):</p> <p>cam4_clm5<br> cam5_clm5<br> cam6_noicenucl_clm5<br> cam6_noclubb_clm5<br> cam6_mg1_clm5<br> cam6</p>
Data for "Abrupt transitions in an atmospheric single-column model with weak temperature gradient approximation"
<p>These are the main output files for the experiment studied in the paper "Abrupt transitions in a single-column model with weak temperature gradient approximation". File names and descriptions are as follows:</p> <ol> <li>hysteresis.mat: A MATLAB file that contains WRF output from the hysteresis test used to create Figure 5 of the paper. The variable names should be straightforward to someone familiar with WRF. (Full WRF output was not saved for these runs.)</li> <li>input_soil: surface initial conditions for WRF SCM, same for all experiments</li> <li>input_sounding: initial sounding, same for all experiments</li> <li>main_fls1: a WTG SST-ramping experiment showing <span class="math-tex">\(f_{\rm LS}\to 1\)</span> as described in the paper.</li> <li>main_rce_ramp: a SST-ramping experiment without the weak temperature gradient approximation</li> <li>namelist.input.rce: the namelist input file for the "rce_run" experiment</li> <li>namelist.input.wtg: the namelist input files for the "wtg_ramp_run1" experiment</li> <li>rce_run: the main RCE experiment used to create the background WTG profile for the wtg_ramp_run1 experiment and the wtg_noramp experiment</li> <li>wtg_noramp: a 180-day experiment with WTG but no ramping, for comparison</li> <li>wtg_ramp_run1: the main WTG ramping experiment showing the abrupt transitions</li> </ol> <p> </p>
Data for "Abrupt transitions in an atmospheric single-column model with weak temperature gradient approximation"
<p>These are the main output files for the experiment studied in the paper "Abrupt transitions in a single-column model with weak temperature gradient approximation". File names and descriptions are as follows:</p> <ol> <li>hysteresis.mat: A MATLAB file that contains WRF output from the hysteresis test used to create Figure 5 of the paper. The variable names should be straightforward to someone familiar with WRF. (Full WRF output was not saved for these runs.)</li> <li>input_soil: surface initial conditions for WRF SCM, same for all experiments</li> <li>input_sounding: initial sounding, same for all experiments</li> <li>main_fls1: a WTG SST-ramping experiment showing fLS→1 as described in the paper.</li> <li>main_rce_ramp: a SST-ramping experiment without the weak temperature gradient approximation</li> <li>namelist.input.rce: the namelist input file for the "rce_run" experiment</li> <li>namelist.input.wtg: the namelist input files for the "wtg_ramp_run1" experiment</li> <li>rce_run: the main RCE experiment used to create the background WTG profile for the wtg_ramp_run1 experiment and the wtg_noramp experiment</li> <li>wtg_noramp: a 180-day experiment with WTG but no ramping, for comparison</li> <li>wtg_ramp_run1: the main WTG ramping experiment showing the abrupt transitions</li> </ol>
Dataset for Residual Study: Testing Jupiter Atmosphere Models Against Juno MWR Observations
<p>This dataset includes all data values for all 14 figures in paper "Residual study: Testing Jupiter atmosphere models against Juno MWR observations".</p>
Climate model data for "Atmosphere-ocean feedback from wind-driven sea spray aerosol production"
<p>Data from atmosphere-only and coupled climate model simulations performed for "Atmosphere-ocean feedback from wind-driven sea spray aerosol production". Files are in netCDF format.</p>
Selected data analysed in the JGR Atmosphere manuscript "Noah-MP with the generic crop growth model Gecros in the WRF model: Effects of dynamic crop growth on land-atmosphere interaction"
<p>This depository contains the simulated 3-hr data of 2m-temperature (tas), latent heat flux (hfls), sensible heat flux (hfss), soil moisture (mros) of the top 1 m, convective available potential energy (cape), convection inhibition (cin), leaf area index (lai) for the model domain, which centers Germany. Further it contains the namelist.input of the WRF simulations of the CTRL and EXP_CROP run. The simulations are described in the manuscript "Noah-MP with the generic crop growth model Gecros in the WRF model: Effects of dynamic crop growth on land-atmosphere interaction" by Warrach-Sagi et al., 2022</p>
Brown dwarf atmosphere intensity grid produced by PICASO radiative transfer code and based on Sonora model atmospheres
<p>Specific intensity in erg s^−1 Hz^−1 sr^−1 cm^−2 on a grid of the cosine of the viewing angle 𝜇 = cos 𝜙, wavelength 𝜆, surface effective temperature, and surface gravitational acceleration that was computed from the Sonora pressure-temperature and abundance profiles, using version 2.3 of the open source code PICASO (Batalha et al. 2019; Batalha et al. 2022), which has previously been used to compute the thermal emission spectra of brown dwarfs (e.g. Mang et al. 2022) and exoplanets (e.g. Robbins-Blanch et al. 2022).</p> <p>This grid served as input to software PARS (Paint the Atmospheres of Rotating Stars; Lipatov and Brandt 2020) in an article that predicts the observational effects of rotation in brown dwarfs (Lipatov, Brandt, and Batalha 2022).</p>
Modelling PM2.5 during severe atmospheric pollution episode in Lagos, Nigeria: Spatiotemporal variations, source apportionment, and meteorological influences
<p>Data</p>
Dataset supporting "On the resolution sensitivity of equatorical precipitation in a GFDL global atmospheric model"
<p>Specification of the model vertical coordinate, selected analysis script, and selected variables from aquaplanet simulations from a GFDL global atmospheric model at different horizontal resolutions: c192 (corresponding to a nominal resolution of 50 km), c384 (~25 km), c768 (~13 km), c1536 (~6 km). Details of the simulations are documented in a manuscript titled "On the resolution sensitivity of equatorial precipitation in a GFDL global atmospheric model" to be submitted to the Journal of Advances in Modeling Earth Systems.</p>
The second Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL2
<p>Supporting data for figures in GMD draft paper: The second Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL2</p> <p> </p> <p> </p>
Data and scripts for figures for "The modeled seasonal cycles of land biosphere and ocean N2O fluxes and atmospheric N2O"
Open the record for dataset details and reuse information.
Integrative evaluation of biomonitoring data and modelings indicating atmospheric deposition of heavy metals, link to research data and scientific software
<p>Research data and scientific software related to integrative statistical analyses based on deposition data calculated with the model LOTOS-EUROS (LE) and the EMEP/MSC-East model (Germany, Europe) and Biomonitoring data on As, Cd, Cr, Cu, Ni, Pb, Zn concentrations in moss, leaves and needles and soil derived from the European Moss Survey (EMS), the German Environmental Specimen Bank (ESB) and the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests). The modelled HM deposition and respective concentrations in moss (EMS), leaves and needles (ESB, ICP Forests) and soil (ICP Forests) were investigated for their statistical relationships. Regression kriging was applied to calculate maps of Cd and Pb deposition across Germany.</p>
Modelling spatial patterns of correlations between concentrations of heavy metals in mosses and atmospheric deposition across Europe in 2010, link to research data and scientific software
<p>Research data and scientific software related to a study investigating the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites.</p>
Coarse-grained moment from atmospheric model
<p>Data from high-resolution models.</p>
Validation of the atmospheric dispersion model NAME against long-range tracer release experiments
<p>NAME model output files for the CAPTEX and ANATEX experiments.</p>
Developing and bounding ice particle mass-and area-dimension expressions for use in atmospheric models and remote sensing
<p>Ice particle mass- and projected area-dimension (<em>m</em>-<em>D</em> and <em>A</em>-<em>D</em>) power laws are commonly used in the treatment of ice cloud microphysical and optical properties and the remote sensing of ice cloud properties. Although there has long been evidence that a single <em>m</em>-<em>D</em> or <em>A</em>-<em>D</em> power law is often not valid over all ice particle sizes, few studies have addressed this fact. This study develops self-consistent <em>m</em>-<em>D</em> and <em>A</em>-<em>D</em> expressions that are not power laws but can easily be reduced to power laws for the ice particle size (maximum dimension or <em>D</em>) range of interest, and they are valid over a much larger <em>D</em> range than power laws. This was done by combining ground measurements of individual ice particle <em>m</em> and <em>D</em> formed at temperature <em>T</em>  <  −20 °C during a cloud seeding field campaign with 2-D stereo (2D-S) and cloud particle imager (CPI) probe measurements of <em>D</em> and <em>A</em>, and estimates of <em>m</em>, in synoptic and anvil ice clouds at similar temperatures. The resulting <em>m</em>-<em>D</em> and <em>A</em>-<em>D</em> expressions are functions of temperature and cloud type (synoptic vs. anvil), and are in good agreement with <em>m</em>-<em>D</em> power laws developed from recent field studies considering the same temperature range (−60 °C  <  <em>T</em>  <  −20 °C).</p>
Data and Code for: Characterising the vertical structure of buildings in cities for use in atmospheric models
<p>This contains the data, model outputs, and analysis code for the article: Characterising the vertical structure of buildings for atmospheric models, Stretton et al. 2023, Urban Climate, https://doi.org/10.1016/j.uclim.2023.101560</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.