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59 results for “Radiative Transfer”
Dataset - Impact of 3D radiative transfer on airborne NO2 imaging remote sensing over cities with buildings
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2021) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.md </strong></em>text file.</p> <p>The dataset contains:</p> <p>- libRadtran output (radiances and AMFs)</p> <p>- Synthetic SCDs</p>
Research Compendium for Harrington et al. (2021): "An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code: I. Design, Tests, and Application to Exoplanet HD 189733 b"
<p>This archive is the Reproducible Research Compendium for<br> <br> An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code: I. Design, Tests, and Application to Exoplanet HD 189733 b<br> <br> by Harrington et al. (2021), published in The Planetary Science Journal.<br> <br> BART is an atmospheric parameter retrieval code. It infers the properties of planetary atmospheres from spectroscopic observations. The compendium includes all the software, documentation, configuration files, plots, and data published in the paper. The compendium is under the Reproducible Research Software License; see LICENSE file. The README provides additional information and describes the contents of each compressed .tar.gz file.</p>
Multi-fidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models
<p>This repository contains several datasets of spectral atmospheric transfer functions (i.e. path radiance, transmittances, spherical albedo) simulated with MODTRAN6 atmospheric radiative transfer model. The simulations are stored in hdf5 files using the Atmospheric Look-up table Generator (ALG) toolbox (<a href="https://doi.org/10.5194/gmd-13-1945-2020">https://doi.org/10.5194/gmd-13-1945-2020</a>). Each dataset has an associated .xml file that includes the configuration of ALG/MODTRAN6 executions. All datasets include the input atmospheric/geometric variables that are summarized in the following table. Each dataset file has a random distribution (based on latin hypercube sampling) these input variables with varying number of points (e.g. train500.h5 contains 500 samples). The <em>reference </em>dataset contains 10000 samples and was used as reference for evaluating Gaussian Processes emulators.</p> <table> <tbody><tr> <th>Input Variables</th> <th>Units</th> <th>Min</th> <th>Max</th> </tr> </tbody><tbody> <tr> <td>O3 column concentration</td> <td>atm-cm</td> <td>0.25</td> <td>0.45</td> </tr> <tr> <td>Columnar Water Vapor</td> <td>g/cm2</td> <td>0.2</td> <td>4</td> </tr> <tr> <td>Aerosol Optical Thickness</td> <td>-</td> <td>0.04</td> <td>0.6</td> </tr> <tr> <td>Asymmetry parameter</td> <td>-</td> <td>0.5</td> <td>0.85</td> </tr> <tr> <td>Angstrom exponent</td> <td>-</td> <td>0.1</td> <td>2</td> </tr> <tr> <td>Single Scattering Albedo</td> <td>-</td> <td>0.8</td> <td>1</td> </tr> <tr> <td>Surface elevation</td> <td>km</td> <td>0</td> <td>2.5</td> </tr> <tr> <td>Solar Zenith Angle</td> <td>deg</td> <td>0</td> <td>70</td> </tr> <tr> <td>Relative Zenith Angle</td> <td>deg</td> <td>0</td> <td>180</td> </tr> </tbody> </table> <p> </p>
Input and Output simulation data of the THOR GCM for the paper Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme
<p>The input and ouput simulation data of the THOR GCM for Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme</p> <p>Global circulation models (GCMs) play an important role in contemporary investigations of exoplanet atmospheres. Different GCMs evolve various sets of dynamical equations which can result in obtaining different atmospheric properties between models. In this study, we investigate the effect of different dynamical equation sets on the atmospheres of hot Jupiter exoplanets. We compare GCM simulations using the quasi-primitive dynamical equations (QHD) and the deep Navier-Stokes equations (NHD) in the GCM THOR. We utilise a two-stream non-grey "picket-fence" scheme to increase the realism of the radiative transfer scheme. We perform GCM simulations covering a wide parameter range grid of system parameters in the population of exoplanets. Our results show significant differences between simulations with the NHD and QHD equation sets at lower gravity, higher rotation rates or at higher irradiation temperatures. The parameter exploration shows the relevance of choosing dynamical equation sets dependent on system and planetary properties.Climate states of hot Jupiters seemed to be more diverse than previously thought. There are exceptions to prograde superrotation. Overall, our study shows the evolution of different climate states which arise just due to different selection of Navier-Stokes equations and approximations. We show the shortcomings of approximations in GCMs made for Earth, but used for non Earth-like planets.</p>
Spatially resolved mock observations of stellar kinematics: full radiative transfer treatment of the simulated galaxies of the Auriga project
<p>We present realistic mock spectroscopic observations for state-of-the-art hydrodynamical simulations, using high spectral resolution stellar population models (FSPS) and full radiative transfer treatment with SKIRT. The dataset contains IFS-like spectroscopy of the stellar continuum for 10 (of the 30) galaxies in the Auriga Project at z=0. One galaxy at z=0.8 (Au 6) and another at z=3 (Au 29).</p>
Dataset of Paper "Enhanced numerical simulation of photocatalytic reactors with an improved solver for the radiative transfer equation"
<p>Dataset of Paper "Enhanced numerical simulation of photocatalytic reactors with an improved solver for the radiative transfer equation":</p> <p>- Data of the incident radiation profiles in the annular reactor.</p> <p>- Average and net values of main radiation magnitudes in the tubular reactor at different photocatalyst concentration </p> <p> </p>
Dataset - Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2020) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.txt</strong></em> file.</p> <p>The dataset contains:</p> <p>- libRadtran input files</p> <p>- libRadtran output</p> <p>- name lists</p> <p>- GRAL simulation outputs</p>
Simulating gas giant exoplanet atmospheres with Exo-FMS: Comparing semi-grey, picket fence and correlated-k radiative-transfer schemes.
<p>NetCDF dataset for paper titled: Simulating gas giant exoplanet atmospheres with Exo-FMS: Comparing semi-grey, picket fence and correlated-k radiative-transfer schemes.</p> <p> </p> <p>Contains:</p> <p>1. Heng benchmark GCM data</p> <p>2. Rauscher benchmark GCM data</p> <p>3. HD209 model using semi-grey RT</p> <p>4. HD 209 model using non-grey picket fence RT</p> <p>5. HD 209 model using corr-k RT scheme</p>
Data in "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study"
<p>The files contain the data of results in the paper "Impact of microstructure on solar radiation transfer within sea ice during summer in the Arctic: A model sensitivity study".</p>
Fine-scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data
<p>In recent years, LiDAR technology has gained widespread attention for its ability to provide precise 3D vertical structural data for various objects, particularly forests. In our dataset, we utilized LiDAR data to reconstruct intricately detailed three-dimensional representations of specific larch forest landscapes. These detailed forest structural models enable us to drive three-dimensional radiative transfer models, analyze the radiation budget of the forest canopy, and gain valuable insights into fine-scale forest management strategies.</p> <p>This is the research work we conducted by combining the aforementioned 3D forest scenes with the 3D RTM LESS. If you use our data, please cite our article. You can access our publication via DOI: 10.34133/plantphenomics.0166.</p> <p>We welcome researchers interested in a wide range of fields, such as vegetation ecological applications, to communicate with us by combining 3D vegetation modeling.</p> <p><br><br></p>
DirtyGrid: 3D dust radiative transfer modeling of spectral energy distributions of dusty stellar populations
<p>Output global SEDs of a large grid of 3D stellar+dust radiative transfer models spanning the range of star formation and dust contents of regions of galaxies.</p> <p>Paper describing the DirtyGrid is Law, Gordo, & Misset (2018, ApJ, submitted)</p> <p>Code to make to access this data at: https://github.com/karllark/pydirtygrid</p>
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
Single column 1D radiative transfer simulations for a case study of multilayer mixed-phase cloud in Punta Arena Chile
<p>This data set contain the input files and output simulations from a single column 1D radiative transfer simulations using the <strong>R</strong>apid <strong>R</strong>adiative <strong>T</strong>ransfer <strong>M</strong>odel for <strong>G</strong>eneral Circulation Model (GCM) applications (RRTMG). The simulations are focused on a selected case study of multiple-layer mixed phase cloud observed in Punta Arenas Chile. The input parameters of the simulations are based on remote sensing observations, which were synergistically used with the Cloudnet and VOODOO algorithm to derive macro and microphysical properties of clouds. The atmospheric profiles of temperature, pressure, and ozone are from ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) and values of surface albedo from CERES (Clouds and the Earth's Radiant Energy System) SYN1deg Ed. 4.1.</p>
Brightness Temperature Spectra 400-1200 cm**-1 from satellite METEOR-29 instrument SI-1 in February 1979 along with radiative transfer simulations from 4 different reanalyses, clear scenes only over ocean
<p>This dataset is published in support of a tentative journal publication in a peer-reviewed journal. The full data record is scheduled for release under DOI:10.15770/EUM_SEC_CLM_0086</p>
Data - A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces
<p>Data and scripts associated with the article "A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces"</p>
Radiative transfer model and datasets for Li et al. (2023), 'Wintertime low-level clouds over sea ice cool the Arctic climate system'
<p>Source code for the radiative transfer model (RAPRAD) and cloud radiative flux data used in the study Li et al. (2022).</p>
Synthetic Observation of Column Density Maps Derived from Cloud Factory Data via Radiative Transfer Simulations
<p>This dataset contains synthetic observation data derived from Cloud Factory data, processed through radiative transfer simulations using POLARIS. The data focuses on several star-forming regions, providing valuable insights into the evolution of filaments within these areas. The dataset is presented at three different resolutions: high (0.25 pc/pixel), medium (0.5 pc/pixel), and low (1 pc/pixel), to cater to various analytical needs. Files labeled as RA, RB, and RC correspond to three distinct regions under study. Additionally, each file is tagged with a 's' followed by a number, indicating the snapshot sequence. </p>
Modeling Data from "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars. Insights from Radiative Transfer Modeling"
<p>This dataset includes the results from the radiative transfer modeling done in the paper "The VLA/ALMA Nascent Disk and Multiplicity (VANDAM) Survey of Orion Protostars: Insights from Radiative Transfer Modeling" by Sheehan et al. Included are the full posteriors from the model fitting for each source as a Python pickle file that contains a dictionary with keys given by the source names, e.g. "HOPS-2", that point to numpy arrays containing the posterior distributions. The information can be loaded like so:</p> <pre><code class="language-python">import pickle data, keys = pickle.load(open("posteriors.p","rb"))</code></pre> <p>Here "keys" is a list containing the names of the parameters from the model fit that are a part of the posterior distribution for each source.</p> <p>Also included are the configuration files and datasets used in the modeling for each source, as well as the results from the fit so that anyone can work with these models for their own purposes. An example script that shows how to use these files is included, and further information can be found at <a href="http://pdspy.readthedocs.io">http://pdspy.readthedocs.io</a>.</p>
Data for "Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison"
<p>All data recorded for reflectance and e-folding depth measurements associated with "Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison"</p>
Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"
<div> <p>This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"</p> <p> </p> </div>
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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.