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59 results for “Radiative Transfer”
Single column 1D radiative transfer simulations during PS106 including low-level-stratus clouds in the central Arctic
<p>The collection of datasets published contain the input parameters 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).</p><p>The data set contains simulations for the PS106 research cruise conducted in 2017 in the Central Arctic. The simulations are based on remote sensing data which were processed with the Cloudnet algorithm to derive cloud macro - and microphyiscal products. 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>
Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions - data
<p>Data set pertaining to the manuscript "Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions", accepted for publication in Nature Chemistry.</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data') if applicable.<br> 2. As-measured data ('raw').</p> <p>Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme.</p> <p><br> The following files are provided:</p> <p>Photoemission data pertaining to ETMD measurements:<br> alcl3-K-etmd.h5 (ETMD after Al K-shell photoionization)<br> alcl3-L23-etmd.h5 (ETMD after Al L-shell photoionization)</p> <p>Calculated energies of the ETMD final states after 1s ionization. The energies were calculated at the CAS-CI/cc-pVDZ level. The states were shifted so that the lowest-energy state corresponds to the LC-ωPBE/aug-cc-pVTZ and aug-cc-pCVTZ value obtained in a polarizable continuum:<br> Dataset_ETMD_after_1s_ionization.csv<br> Dataset_ETMD_after_2p_ionization.csv</p> <p>Geometrical coordinates of the clusters that were used for energy calculation:<br> clusters.dat<br> clusters_small.dat</p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p> <p> </p> <p>Version history:</p> <p>v3 - Al L2,3 data: Orientation of the analyser hemisphere corrected. Direction of the linear polarization vector added. All other data unchanged.<br> v2 - cluster coordinates added, all other data unchanged.<br> v1 - initial upload.</p>
Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al.
<p>Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al. which is to be submitted to Atmospheric Measurement Techniques. </p> <p>A comprehensive inter-comparison of seven radiative transfer models in the limb scattering geometry has been<br> performed. Every model is capable of accounting for polarisation within a fully spherical atmosphere. Three models (GSLS, SASKTRAN-HR, and SCIATRAN) are deterministic, and four models (MYSTIC, SASKTRAN-MC, Siro, and SMART-G)<br> are statistical using the Monte Carlo technique. This dataset consists of the raw radiance data used to perform the intercomparisons, atmospheric input data for the optical properties of the atmosphere, and data specifying the geometry of the test cases.</p> <p>Data is provided in NetCDF4 format with documentation present inside the variable attributes.</p> <p>More detail on the comparison scenarios can be found within the published article. (Link to be added when available).</p>
Radiative transfer modeling in structurally-complex stands: what aspects matter most?: Dataset
<p>This repository is linked to the paper "Radiative transfer modeling in structurally-complex stands: what aspects matter most?" submitted to Annals of Forest Science and written by Frédéric ANDRÉ (corresponding author), Louis DE WERGIFOSSE, François DE COLIGNY, Nicolas BEUDEZ, Gauthier LIGOT, Vincent GAUTHRAY-GUYÉNET, Benoit COURBAUD and Mathieu JONARD.</p> <p>The repository contains the three following files :</p> <ul> <li>CalibrationResults.csv: Bayes factors and summary statistics of parameter estimates for each calibration run</li> <li>ParameterPosteriorDistributions.csv: median values and 90% credible intervals for the parameter posterior distributions</li> <li>StatisticalComparison.csv: statistics (Fractional bias, Root mean square error, Paired Student test, Pearson correlation coefficient, Parameters of the Deming regression between observed and predicted values) used to compare the 'Best model configurations'</li> </ul> <p>For more information concerning this repository or the study, please do not hesitate to contact Frédéric ANDRÉ (frederic.andre@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
A dataset of global variations in directional solar radiation exposure for ocular research using the libRadtran radiative transfer model
<p>Directional solar photon flux density has particular relevance to eye disease research (keratitis, cataract formation, macula degeneration) because ocular components (cornea, lens, retina) experience different exposures dependent on global location, structural geometry of the eye and human behaviour (Sliney, 1997). The human macula has a field of view of ~17<strong>°</strong>, or 0.06901537 sr (Strasburger, Rentschler & Jüttner, 2011) and its cone of exposure can be modelled at a range of global locations using a radiation transfer model to estimate different directions of irradiation. This dataset provides examples of spectral radiance within the macula field of vision, calculated with the radiative transfer model libRadtran v2.0.3 (Mayer & Kylling, 2005). Three data sets are provided at different latitudes without correction for spectral ocular transmission. Unless otherwise specified, all simulations were parametrized according to local meteorological condition (altitude, pressure, temperature) and atmospheric conditions on the simulated day (aerosol optical density, water column, O<sub>3</sub> and NO<sub>2</sub> concentrations). The model was parametrized for a subject looking northward toward the ground (-15<strong>°</strong> from horizon), at a height of 170 cm above the ground.</p> <p>For each simulation, a separate file is available for each condition (latitude, time, date, see below) that includes radiance at each wavelength. Radiance values are in mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>.</p> <p>The technique provides future opportunity to model global exposures of different ocular components to spectral solar irradiance using information on ocular transmission, local terrain, albedo and human behaviour in order to explore their relevance in epidemiological studies of age-related eye disease.</p> <p>For each simulation, a separate file is available for each condition (latitude, time, date, see below) that includes radiance at each wavelength. Radiance values are in mW m<sup>-2</sup> nm<sup>-1</sup> sr<sup>-1</sup>.</p> <p>The technique provides future opportunity to model global exposures of different ocular components to spectral solar irradiance using information on ocular transmission, local terrain, albedo and human behaviour in order to explore their relevance in epidemiological studies of age-related eye disease.</p> <p><em>Simulation 1: </em>This data set reports the spectral radiance from 250 - 500 nm at:</p> <ul> <li>3 latitudes (61.0: Southern Finland, 50.1 Northern France, 38.0: Central Spain).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>8 cardinal directions (every 45<strong>° </strong>from North).</li> <li>2 aerosol optical densities (0.1 and 2.5).</li> </ul> <p><em>Simulation 2: </em>This data set reports the spectral radiance from 250 - 2,500 nm at:</p> <ul> <li>3 latitudes (61.0: Southern Finland, 50.1 Northern France, 38.0: Central Spain).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>1 cardinal direction (North).</li> <li>2 aerosol optical densities (0.1 and 2.5).</li> </ul> <p><em>Simulation 3: </em>This data set reports the spectral radiance from 250 - 500 nm at:</p> <ul> <li>1 latitude (61.0: Southern Finland).</li> <li>4 dates (April 17<sup>th</sup>, July 1<sup>st</sup>, September 1<sup>st</sup>, November 6<sup>th</sup> 2019).</li> <li>24 hours.</li> <li>9 cardinal directions (every 40<strong>° </strong>from North).</li> <li>3 bidirectional reflectance distribution functions for the ground (forest, urban, snow).</li> <li>2 tilt angles for the eye direction (0<strong>° </strong> from horizon or -15<strong>°</strong> from horizon, toward the ground).</li> </ul> <p> </p>
Radiative Transfer Edge-on Protoplanetary Disk Images
<p>Dataset used to train a Convolutional Autoencoder model to generate synthetic images of edge-on protoplanetary disks. The work is described in "A machine learning framework to predict images of edge-on protoplanetary disks", Telkamps et al. 2022, submitted to AAS. This image dataset was created using the radiative transfer (RT) modeling code MCFOST (Pinte et al. 2006; Pinte et al. 2009). </p>
Enhanced 3D radiative transfer ACM-RT calculations and output for Cole et al., 2022
<p>For the Earth Cloud, Aerosol, Radiation Explorer (EarthCARE) satellite mission there are a number of algorithms used to process the observations. One of these algorithms, called ACM-RT, is designed to use retrieved geophysical properties to perform forward radiative transfer calculations using 1D and 3D solar and thermal radiative transfer models. The ACM-RT algorithm is documented in an Atmospheric Measurements and Techniques (AMT) article. </p> <p>To illustrate outputs from ACM-RT, including the benefits of 3D radiative transfer, “enhanced” radiative transfer calculations were performed, relative to calculations that would be performed operationally during the EarthCARE mission. In particular, the 3D Monte Carlo radiative transfer calculations used an increased number of samples to reduce the Monte Carlo uncertainty and calculations were performed for more of the input data.</p> <p>The relevant publication for these calculations is:</p> <p>Cole, J. N. S., H. W. Barker, Z. Qu, N. Villefranque and M. W. Shephard : Broadband Radiative Quantities for the EarthCARE Mission: The ACM-COM and ACM-RT Products. Submitted to AMT, November 2022.</p>
Radiative transfer calculation results for Arctic cirrus
<p>Data of the figure content from the radiative transfer calculations in the publication Marsing, Meerkötter et al: Investigating the radiative effect of Arctic cirrus measured in situ during the winter 2015/2016</p> <p>Preprint DOI: https://doi.org/10.5194/acp-2022-395</p>
Radiative transfer simulations for the Arctic research expedition PS106
<p>The set of data files contain the results of radiative transfer simulations for the Arctic research expedition PS106. The simulations are based on remote sensing observations conducted during the PS106 cruise in 2017, which were used synergistically with Cloudnet algorithm to derive macro and microphysical properties of clouds. Moreover, atmospheric profiles of temperature, pressure, and ozone 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 were considered.</p>
Supplement to "On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies"
<p>This supplement contains a set of realistic cloudy and precipitating conditions from the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the corresponding simulated brightness temperatures for the journal article “On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies”, which is under review at the Journal of Quantitative Spectroscopy and Radiative Transfer. These data are available as benchmark for model developers.</p> <p>The IFS profiles correspond to model data interpolated (using the IFS observation operator) to the locations of the Atmospheric Infrared Sounder (AIRS) for a 12 h period centred on 03 UTC on 1 November 2018. Note that only a subset of the full set of AIRS locations are supplied here. The IFS model version cycle 46r1 was used, in a research experiment, to generate these profiles. The model was configured to T1279co resolution (about 8-9 km) with 137 levels in the vertical. The forecast was initialized from operational analyses at 18 UTC on 31 October 2018, and hence corresponds to the 12 h forecast "background" used in the data assimilation cycle.</p> <p>The following information concerns only the IFS model data:</p> <ul> <li>Copyright statement: Copyright "© 2022 European Centre for Medium-Range Weather Forecasts (ECMWF)".</li> <li>Source: www.ecmwf.int.</li> <li>Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.</li> <li>Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0), https://creativecommons.org/licenses/by/4.0/.</li> <li>Further information on the ECMWF data policy, and guidance on how to comply with it, is at https://apps.ecmwf.int/datasets/licences/general/.</li> </ul> <p>The simulated brightness temperatures are also distributed under Creative Commons Attribution 4.0 International (CC BY 4.0).</p> <p>The full journal description of this dataset is currently under review, with the current citation:</p> <p>Barlakas, V., Galligani, V. S., Geer, A. J., Eriksson, P., 2022. On the accuracy of RTTOV-SCATT for radiative transfer at all-sky microwave and submillimeter frequencies. Submitted to Journal of Quantitative Spectroscopy and Radiative Transfer.</p> <p>The work of Vasileios Barlakas at Chalmers University of Technology is funded by a EUMETSAT fellowship program.</p>
Dataset for "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation"
<p>These documents are supplements to the "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation" paper published by the same authors in The Planetary Science Journal in 2022.</p> <p>Are made available:</p> <p>-the Supporting Information document on the performed sensitivity study,<br> "paper_mtWRF_lake_RT_220825_SI.pdf"</p> <p>-the Fortran source code of the radiative transfer module developed for this work,<br> "module_ra_gray.F"</p> <p>-all the netCDF simulation outputs and a list describing their parameters,<br> "run-##.nc.gz"<br> "list_simulations_2D_paper2022_RT_zenodo.pdf"</p> <p>-the Python codes to plot figures from the netCDF output files,<br> "mtwrf_analysis_#D_#.py"</p>
Infrared Radiative Transfer Calculations Dataset
<p>This dataset presents the results of a series of experiments conducted varying combinations of two versions of the HITRAN database (2016 and 2020) and two versions of the MT_CKD water vapor continuum model (3.2 and 4.1.1). Five atmospheric models representing different climatic conditions (Tropical, Mid Latitude Winter, Mid Latitude Summer, Sub Arctic Winter, and Sub Arctic Summer) are employed. Key atmospheric gases, including CO2, O3, CH4, CO, N2O, and O2, are prescribed at each atmospheric model. The dataset includes calculations with all gases present as well as experiments removing individual gases (specifically CO2, O3, and water vapor). Outputs consist of upwelling and downwelling infrared radiation and cooling rate profiles at 44 atmospheric heights from the surface to 95 km. Data are provided at 0.1 cm^-1 resolution across the spectral range from 10 to 3000 cm^-1. For each experiment, a file in “.txt” format is available at each atmospheric height containing the flux each wavenumber. This dataset offers valuable insights into the impact of different atmospheric compositions and models on radiative transfer processes and cooling rates, contributing to a better understanding of Earth's climate system.</p> <p>Similar experiments are compacted in .tar files:</p> <p>var_profile_HITRAN<em>_lev_</em>lllll_mtckd{<em>_wo</em>flag}<em>.tar</em></p> <table> <tbody> <tr> <td>var (3 digits)</td> <td>profile (3 digits)</td> <td>HITRAN (4 digits)</td> <td>lllll (5 digits)</td> <td>mtckd (3 digits)</td> <td>flag (3 digits)</td> </tr> <tr> <td>"olr": upward radiation flux</td> <td>"tro": Tropical</td> <td>2016</td> <td>Output height in meters. 5 digits. Ranging from the surface (i.e. "00000") to 95 km (i.e. "95000")</td> <td>"3.2": v3.2</td> <td>"H2O": experiment removing water vapor.</td> </tr> <tr> <td>"dlr": downward radiation flux</td> <td>"mls": Mid Latitude Summer</td> <td>2020</td> <td> </td> <td>"4.1": v4.1.1</td> <td>"CO2": experiment removing carbon dioxide.</td> </tr> <tr> <td>"coo": cooling rates</td> <td>"mlw": Mid Latitude Winter</td> <td> </td> <td> </td> <td> </td> <td>"O3": experiment removing ozone.</td> </tr> <tr> <td> </td> <td>"sas": Sub Artic Summer</td> <td> </td> <td> </td> <td> </td> <td>"BFX": experiment without the "B linear to tau" aproximation.</td> </tr> <tr> <td> </td> <td>"saw": Sub Artic Winter</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p>To untar the file:</p> <pre><code>tar -xzf var_profile_HITRAN_lev_lllll_mtckd_woflag.tar</code></pre> <p>Each tar file consists of 880 text files.</p>
An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation: datasets
<p>This data record contains datasets used in the study "An SI-traceable protocol for the validation of radiative transfer model-based reflectance simulation":</p> <ul> <li>The <code><span>final_design.ply</span></code> file contains the mesh corresponding to the final artefact design.</li> <li>The <code><span>material_measurements.nc</span></code> file contains goniophotometer records for the material reflectance.</li> <li>The <code><span>artefact_measurements.nc</span></code> file contains goniophotometer records for the artefact reflectance.</li> </ul>
Dataset related to the publication "Anomalous radiative transfer in heterogeneous media"
<p>This repository contains Monte Carlo results for anomalous light transport in a spherical geometry with homogeneous (<code>Hom</code>), heterogeneous layered (<code>Lay</code>), and homogeneous layered (<code>VHom</code>) configurations.</p> <p>Part of these data is described in the publication:</p> <p>F. Tommasi et al. "Anomalous radiative transfer in heterogeneous media" <em>Advanced Theory and Simulations</em> (2024) <a href="https://doi.org/10.1002/adts.202400182">https://doi.org/10.1002/adts.202400182</a></p> <p><br>Data is organized in two types of csv files:</p> <ul> <li><code>filename.csv</code> containing information on the average path length and total path length distribution</li> <li><code>filename_Fluence.csv</code> containing information on the fluence rate and radiance at each spherical layer boundary</li> </ul> <p>Filenames contain information on the simulation parameters used:</p> <table> <tbody> <tr> <td><code>ISO</code></td> <td>isotropic scattering</td> </tr> <tr> <td><code>K0_X</code></td> <td>value of the k coefficient of the Generalized Pareto Distribution used in the Monte Carlo simulation (<code>X</code> = 0.3 or 0.7)</td> </tr> <tr> <td><code>1e7</code></td> <td>number of total trajectories considered</td> </tr> <tr> <td><code>DS</code>, <code>noDS</code></td> <td>simulation performed used the proposed set of rules for anomalous transport in bounded media (<code>DS</code>) or with the standard set of rules (<code>noDS</code>).</td> </tr> <tr> <td><code>Hom</code></td> <td>homogeneous sphere configuration</td> </tr> <tr> <td><code>NLay</code></td> <td>number of layers used in the layered configurations</td> </tr> <tr> <td><code>mism, no_mism</code></td> <td>configuration with (<code>mism</code>) or without (<code>no_mism</code>) refractive index mismatch with the environment or between layers</td> </tr> <tr> <td><code>Vhom_layN</code></td> <td>layered homogeneous sphere configuration with N layers</td> </tr> <tr> <td><code>mus_const</code>, <code>mus_step</code></td> <td>simulations performed using a constant scattering coefficient (<code>mus_const</code>) or different values across different layers (<code>mus_step</code>)</td> </tr> </tbody> </table> <p> </p> <p>The header of each output file contains general information on the simulation settings as detailed below:</p> <ul> <li>The number of layers</li> <li>The radius of each layer</li> <li>The refractive index of each layer and of the external region</li> <li>The critical angle for entering and exiting the sphere</li> <li>Reduced scattering coefficient of each layer</li> <li>Scattering function</li> <li>Type of illumination source</li> <li>Absorption coefficient of the sphere</li> </ul> <p>Simulations for different scattering strengths are classified based on the optical thickness <code>Taud</code> of the sphere, corresponding to the product between the sphere diameter (10 mm) and the reduced scattering coefficient. Since we have results only for isotropic scattering, <code>Taud</code> is also the product of diameter and scattering coefficient.<br>For a layered sphere, <code>Taud</code> is given by the sum of the products between the scattering coefficient and radial thickness of each layer.<br>The symbol <code>Taua</code> is also used to denote the product between the sphere and the absorption coefficient. Since we have considered only non-absorbing media, this value is always zero.<br>Each simulation is carried out for different values of the reduced scattering coefficient in the layers, keeping their reciprocal ratios fixed, resulting in the following values of Taud: 1E-3, 2E-3, 5E-3, 1E-2, 2E-2, 5E-2, 1E-1, 2E-1, 5E-1, 1, 2, 5, 10, 20, 50, 100, which, in the case of the homogeneous sphere, correspond to reduced scattering coefficients comprised between 1E-4 and 10.<br>The standard error (<code>SE</code>) is also provided, based on the results of 100 independent simulations with 1E5 trajectories each, for a total of 1E7 total trajectories considered.</p> <h3>Structure of the pathlength files (<code>filename.csv</code>)</h3> <p>List of content for the calculated quantities shown:</p> <ul> <li><code>Ksc_Max</code>: maximum number of scattering events in the medium</li> <li><code>de_max(mm)</code>: maximum pathlength followed by photons</li> <li>Number of Photons lost (always zero, all trajectories are collected)</li> <li>Total CW Exiting Radiation and Standard Error (SE)</li> <li>Mean Pathlength for Total Exiting Radiation and SE</li> <li>Solutions of the RTE for the non-absorbing sphere with Lambertian illumination given in terms of the mean pathlength in the sphere and partial pathlength in each layer</li> <li>Partial Mean Pathlengths for in each layer for the non-absorbing case</li> <li>Standard Error on Partial Mean Pathlengths in each layer</li> <li>Discrepancy and SE for the Pathlengths for the non-absorbing case</li> <li>Relative SE is shown for the Partial Mean Pathlengths in the non-absorbing case </li> <li>Total Spread Function (mm-1) versus the pathlength of emerging photons l (mm) for each Taud value</li> </ul> <h3>Structure of the fluence files (<code>filename_Fluence.csv</code>)</h3> <p>List of content for the calculated quantities shown:</p> <ul> <li>Mean pathlength and partial pathlength</li> <li>CW fluence at each layer boundary</li> <li><code>SE</code>: standard error for the CW fluence</li> <li>Discrepancy and SE of the CW fluence with respect to the expected value from the invariance properties. This is reported for each layer interface</li> <li>Relative Error of the CW fluence</li> <li><code>CW FLUX_P</code> at each layer boundary</li> <li>Distribution of CW radiance at each layer boundary for all <code>Taud</code> values. The distribution expected from the invariance property is also listed in the columns adjacent to the Monte Carlo results. Radiance data is reported as a function of the angle at each layer boundary</li> </ul> <p>Additional empty fields in the files refer to output quantities that can be optionally calculated during the simulations. These options were not selected for this study.</p> <p>For convenience, we list below which files were used to prepare each Figure (some files are used multiple times for different Figures):</p> <h3>Figure 2</h3> <p><code>Hom/ISO_Hom_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_3_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_3_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_7_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_const_k0_7_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_3_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_3_1e7_noDS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_7_1e7_DS.csv</code><br><code>Lay/ISO_4Lay_mus_step_k0_7_1e7_noDS.csv</code></p> <h3>Figure 3</h3> <p><code>Lay/ISO_10lay_mism_k0_3_1e7_DS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_3_1e7_noDS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_7_1e7_DS_Fluence.csv</code><br><code>Lay/ISO_10lay_mism_k0_7_1e7_noDS_Fluence.csv</code></p> <h3>Figure 4</h3> <p><code>Hom/ISO_Hom_no_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_no_mism_k0_7_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_3_1e7_noDS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>Hom/ISO_Hom_mism_k0_7_1e7_noDS.csv</code></p> <h3>Figure 5</h3> <p><code>Hom/ISO_Hom_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay2_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay4_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay8_mism_k0_7_1e7_DS.csv</code><br><code>VHom/ISO_Vhom_lay16_mism_k0_7_1e7_DS.csv</code></p>
Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"
<p>This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.</p> <p>Subdirectory "training" contains unnormalized (in physical units) training data. Subdirectories "validation" and "testing" contain unnormalized validation and testing data. Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper. Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.</p> <p>Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).</p> <p>Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).</p>
Single-footprint retrievals for AIRS using a fast TwoSlab cloud-representation model and the all-sky infrared radiative transfer algorithm
<p>Dataset for AMT-2017-261 by DeSouza-Machado et. al.<br> <br> 1D-variational retrievals of temperature and moisture fields from<br> hyperspectral infrared satellite sounders use cloud-cleared radiances<br> as their observation. These derived observations allow the use of<br> clear-sky only radiative transfer in the inversion for geophysical<br> variables but at reduced spatial resolution compared to the native<br> sounder observations. Cloud-clearing can introduce various errors,<br> although scenes with large errors can be identified and<br> ignored. Information content studies show that when using multi-layer<br> cloud liquid and ice profiles in infrared hyperspectral radiative<br> transfer codes, there are typically only 2-4 degrees of freedom of<br> cloud signal. This implies a simplified cloud representation is<br> sufficient for some applications which need accurate radiative<br> transfer. Here we describe a single-footprint retrieval approach for<br> clear and cloudy conditions, which uses the thermodynamic and cloud<br> fields from Numerical Weather Prediction (NWP) models as a first<br> guess, together with a simple cloud representation model coupled to a<br> fast scattering radiative transfer algorithm (RTA). The NWP model<br> thermodynamic and cloud profiles are first co-located to the<br> observations, after which the N-level cloud profiles are<br> converted to two slab clouds (typically one for ice and one for water<br> clouds). From these, one run of our fast cloud representation model<br> allows an improvement of the \emph{a-priori} cloud state by comparing the<br> observed and model simulated radiances in the thermal window<br> channels. The retrieval yield is over 90\%, while the degrees of<br> freedom correlate with the observed window channel brightness<br> temperature which itself depends on the cloud optical depth. The cloud<br> representation/scattering package is bench-marked against radiances<br> computed using a Maximum Random Overlap cloud scheme. All-sky infrared<br> radiances measured by NASA’s Atmospheric Infrared Sounder (AIRS) and<br> NWP thermodynamic and cloud profiles from the European Center for<br> Medium Range Weather Forecasting (ECMWF) forecast model are used in<br> this paper.</p> <p> </p>
Randomly sampled coefficients for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This directory contains a training set of 22 million randomly-sampled radiative transfer coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a>, suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. These coefficients can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, coefficients were computed using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, are:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The coefficients were computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each. There are 2,748,835 data rows in total. The data are provided in their original format, split among 500 files, so that smaller subsamples of the data may be loaded easily. A README.md file provides more detailed information.</p>
Trained neural network data for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This archive contains data representing a trained-up neural network suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. The network generates coefficients that can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, networks were trained on a training set of coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a> that is available as <a href="https://doi.org/10.5281/zenodo.1341154">DOI:10.5281/zenodo.1341154</a>. The data were generated using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, were:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The training set was computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each, yielding about 22 million numbers. Training the networks took about 3 hours on an 8-core laptop.</p> <p>For the purposes of <em>neurosynchro</em>, the formats of the files in this package should be regarded as internal implementation details. The <a href="https://pypi.org/project/neurosynchro/">neurosynchro</a> Python package will load up the files in this archive and use them to predict synchrotron coefficients. For specifics, see <a href="https://neurosynchro.readthedocs.io/en/stable/">the neurosynchro documentation</a>.</p>
Results of Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model
<p>This repository contains:</p> <ol> <li>A deep neural network (DNN) based soil moisture (SM) estimates (NN) based on the SMAP TB (Descending, 6 AM) and SMAP SCA-V ancillary data as the input variables. (<a href="../api/records/13309165/draft/files/SMAP_NN_36km_20150331_20220326.nc/content" target="_blank" rel="noopener noreferrer">SMAP_NN_36km_20150331_20220326.nc</a>)</li> <li>Temporally averaged roughness parameter (hNN) and scattering albedo (omegaNN) which are retrieved by inversely tracking the DNN model. (<a href="../api/records/13309165/draft/files/SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc/content" target="_blank" rel="noopener noreferrer">SMAP_hNN_omegaNN_36km_temporal_average_201503_202103.nc</a>)</li> </ol> <p>Summary:</p> <p>The DNN model has been developed by relating SMAP TB and SMAP SCA-V ancillary data with in-situ SM data from the international soil moisture network (ISMN) using DNN. To minimize scale mismatch between gridded SMAP data and point in-situ data, the triple collocation analysis was conducted.</p> <p>The SM estimated from the DNN algorithm (NN) showed a good agreement with the ISMN data that was not used in the model training. Moreover, for a densely vegetated region located in the Amazon (Tambopata site) the NN showed less bias compared to available SM retrievals. </p> <p>Two parameters hNN and omegaNN are retrieved by ingesting NN to the modified dual channel algorithm. When the SM retrieval was conducted using the hNN and omegaNN, the result showed good agreement with the NN (DNN-based SM) with R of 0.986, ubRMSD of 0.015 m3/m3, and bias of -0.001 m3/m3.</p> <p>The paper "Improved SMAP Soil Moisture Retrieval Using a Deep Neural Network-based Replacement of Radiative Transfer and Roughness Model" published in the Transactions on Geoscience and Remote Sensing.</p> <p>For more details, please contact me (wotp12@unist.ac.kr)</p>
Comparison of radiative transfer schemes for the calculation of aerosol radiative forcing in Mars' atmosphere
<p>Output datasets for Figures (fig. 1 to 10) for the intercomparison of radiative transfer algorithms for the calculation of aerosol radiative forcing in the Martian atmosphere.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.