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opencc-by-4.0Jun 2019View details →

Geostatistical inverse modeling with large atmospheric data: data files for a case study from OCO-2

<p>The files in this data repository provide the inputs required to run an inverse modeling case study. This case study will estimate CO<sub>2</sub> fluxes across North America for July 2015 using synthetic observations that have been created to resemble observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite.</p> <p>This data repository is specifically linked to a GitHub code repository (http://doi.org/10.5281/zenodo.3241524 or <a href="https://github.com/greenhousegaslab/geostatistical_inverse_modeling">https://github.com/greenhousegaslab/geostatistical_inverse_modeling</a>). That GitHub repository provides scripts for constructing a geostatistical inverse model that will estimate greenhouse gas fluxes or air pollution emissions using atmospheric observations. The GitHub repository includes a case study that can be run out-of-the-box; the case study provides users an opportunity to test out and explore the inverse modeling code. All of the input data files for that case study are provided for download here.</p> <p>Here is a brief explanation of the different files included in this data repository, but refer to the linked GitHub repository for greater details. All of these files are in a ".mat" file that can be read into Matlab using the <em>load</em> function or can be read into R using the <em>R.matlab</em> package.</p> <ul> <li><strong>H.tar.gz</strong>: This tar file contains the <strong>H</strong> matrices or sensitivity matrices required by the inverse model. These inputs were generated using the Stochastic Time-Inverted Lagrangian Transport (STILT) model as part of NOAA's CarbonTracker-Lagrange program (<a href="https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/">https://www.esrl.noaa.gov/gmd/ccgg/carbontracker-lagrange/</a>). The <strong>H</strong> matrix is too large to store in a single file. We have therefore split up the matrix into 328 different files (all contained within H.tar.gz). Each file contains a vertical strip of the <strong>H</strong> matrix that corresponds to a different time period of fluxes to be estimated as part of the inverse model.</li> <li><strong>Z.mat</strong>: This file contains the synthetic OCO-2 observations used in the case study. </li> <li><strong>areas_us.mat</strong>: This file lists the area of each model grid box used in the case study in units of meters<sup>2</sup>. This file only includes grid box area for model grid boxes that fall within the continental United States. We estimate CO<sub>2</sub> fluxes across terrestrial North America on a 1 degree latitude by 1 degree longitude grid as part of the case study. Each of these model grid boxes will have a different area, depending upon the latitude of that model grid box. </li> <li><strong>distmat.mat</strong>: This file contains a matrix that lists the distance (in kilometers) between the center of each model grid box used in the case study. </li> <li><strong>land_mask.mat</strong>: We only estimate CO<sub>2</sub> fluxes for terrestrial regions of North America as part of the case study. This land mask is used to convert the fluxes estimated by the inverse model to a latitude-longitude grid that can then be plotted.</li> <li><strong>H_all_OCO2.mat</strong>: This file contains the H matrices summed across differnt time periods. I.e., this file is the sum of all the individual H files contained within H.tar.gz.</li> <li><strong>Xvar.tar.gz</strong>: This file contains different environmental variables from ERA5 meteorology that have been reformatted to match the H footprint matrices. These different variables can be used as predictors of CO2 fluxes in an inverse model. The different variables included in this file are as follows: <ul> <li>Xvar_e.mat                     Evaporation</li> <li>Xvar_msdwswrf.mat      Mean surface downward short-wave radiation flux</li> <li>Xvar_q.mat                     Specific humidity</li> <li>Xvar_stl1.mat                 Soil temperature level 1</li> <li>Xvar_stl3.mat                Soil temperature level 3</li> <li>Xvar_swvl1.mat             Volumetric soil water layer 1</li> <li>Xvar_t2m.mat               2 metre temperature</li> <li>Xvar_tp.mat                  Total precipitation</li> <li>Xvar_mer.mat               Mean evaporation rate</li> <li>Xvar_pev.mat               Potential evaporation</li> <li>Xvar_r.mat                   Relative humidity</li> <li>Xvar_swvl3.mat          Volumetric soil water layer 1</li> <li>Xvar_tcc.mat              Total cloud cover</li> </ul> </li> </ul>

Different behavior of density perturbations between dayside and nightside in the Martian thermosphere and the ionosphere associated with atmospheric gravity waves

<div>---------------------</div> <div>GENERAL INFORMATION</div> <div>---------------------</div> <div> </div> <div>1. Title of Dataset: Different behavior of density perturbations between dayside and nightside in the Martian thermosphere and the ionosphere associated with atmospheric gravity waves</div> <div> </div> <div>2. Authors: Nakagawa, England, et al.</div> <div> </div> <div>3. Contact information: hiromu.nakagawa.c1@tohoku.ac.jp</div> <div> </div> <div>4. Date of data collection: April 2024</div> <div> </div> <div> </div> <div>---------------------</div> <div>DATA & FILE OVERVIEW</div> <div>---------------------</div> <div> </div> <div>1. Original MAVEN data access:</div> <div> </div> <div>The MAVEN/NGIMS (level-2, version-8, revision-1) are publicly available in ASCII format on the NASA Planetary Data System (PDS) at https://atmos.nmsu.edu/data_and_services/atmospheres_data/MAVEN/ngims.html.</div> <div> </div> <div>As for references in Figures 2-4, the MAVEN/MAG Calibrated data are publicly available in ASCII format on the NASA Planetary Data System (PDS) at https://pds-ppi.igpp.ucla.edu/search/view/?id=pds://PPI/maven.mag.calibrated.</div> <div> </div> <div> </div> <div>2. File List:</div> <div> </div> <div>[1] correlate_coefficients.txt</div> <div>[2] rmse_170-190_coupling_case.txt</div> <div>[3] rmse_190-210_coupling_case.txt</div> <div>[4] rmse_170-190_ion_specific_case.txt</div> <div>[5] rmse_190_210_ion_specific_case.txt</div> <div> </div> <div>---------------------</div> <div>DATA-SPECIFIC INFORMATION</div> <div>---------------------</div> <div> </div> <div>[1] correlate_coefficients.txt</div> <div>This includes the geometric information and the calculated correlate coefficient between neutrals and ions in all profiles applied in this study. The first header provides the information in the file: Orbit, Year, Month, Day, Hour, Minute, Unix time, SZA(deg), Lon(deg), Lat(deg), LST(hr), CC(CO2-N2), CC(CO2-CO2+). The geometric information corresponds to those at altitude around 190 km. The last two columns represents the correlate coefficients between CO2 and N2 and between CO2 and CO2+. </div> <div> </div> <div>[2] rmse_170-190_coupling_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 170 km and 190 km for the case of ion-neutral coupling case. </div> <div> </div> <div>[3] rmse_190-210_coupling_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 190 km and 210 km for the case of ion-neutral coupling case. </div> <div> </div> <div>[4] rmse_170-190_ion_specific_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 170 km and 190 km for the case of ion-specific case. </div> <div> </div> <div>[5] rmse_190_210_ion_specific_case.txt</div> <div>This includes RMSE (the unit is percentage) between the observed perturbations and the model fit. The first header provides the information in the file: unix_time, orbit number, sza, lon, lat, lst, rmse_neu_gw_%, rmse_neu_aw_%, rmse_ion_gw_%, rmse_ion_aw_%. The geometric information corresponds to those at altitude around 190 km. The last four RMSEs represents those with the model to fit with the gravity waves (gw) parameters and with the acoustic waves (aw) for the neutral (neu) perturbations (N2) and for the ion (ion) perturbations (CO2+) at altitude range between 190 km and 210 km for the case of ion-specific case. </div> <div> </div> <div>---------------------</div> <div>METHODOLOGICAL INFORMATION</div> <div>---------------------</div> <div> </div> <div>Period to analysis: from March 2015 to August 2020 (orbit number from 713 to 11881)</div> <div> </div> <div>Number of files (CO2): 8,911</div> <div> </div> <div>Number of files (N2): 8,913</div> <div> </div> <div>Number of files (CO2+): 7,053(?)</div> <div> </div> <div>Data selection:</div> <div> </div> <div>1. Neutral species with inbound valid (IV)</div> <div> </div> <div>2. Ion species with SCP (quality flag=0)</div> <div> </div> <div>3. Simultaneous observations of CO2, N2, and CO2+ at altitudes 170-210 km</div> <div> </div> <div>4. All three species CO2, N2, and CO2+ are valid with data sample >10 points in a single orbit.</div> <div> </div> <div>Total number of files after data selection = 3,018</div> <div> </div> <div>Fitting:</div> <div> </div> <div>4th-order polynomial fit to extract the perturbation components of density in the range between 160 and 220 km.</div> <div> </div> <div> </div> <div>Correlation coefficient:</div> <div> </div> <div>The correlation coefficients between perturbations are calculated in the range between 170 and 210 km.</div> <div> </div> <div>Ion-neutral coupling cases whose correlation coefficient between CO2 and CO2+ larger than 0.7: Total number = 839</div> <div> </div> <div>Ion-specific case whose correlation coefficient between CO2 and CO2+ smaller than 0.2:  Total number = 823</div> <div> </div> <div>#We also define CME cases based on Lee et al. (2017):  Total number = 31 cases</div> <div> </div>

opencc-zeroJun 2024View details →

dryad36/100

Data from: Patterns and drivers of atmospheric nitrogen deposition retention in global forests

<p>Forests are the largest carbon sink in terrestrial ecosystems, and the impact of nitrogen (N) deposition on this carbon sink depends on the fate of external N inputs. However, the patterns and driving factors of N retention in different forest compartments remain elusive. In this study, we synthesized 408 observations from global forest <sup>15</sup>N tracer experiments to reveal the variation and underlying mechanisms of <sup>15</sup>N retention in plants and soils. The results showed that the average total ecosystem <sup>15</sup>N retention in global forests was 63.04 ± 1.23%, with the soil pool being the main N sink (45.76 ± 1.29%). Plants absorbed 17.28 ± 0.83% of <sup>15</sup>N, with more allocated to leaves (5.83 ± 0.63%) and roots (5.84 ± 0.44%). In subtropical and tropical forests, <sup>15</sup>N was mainly absorbed by plants and mineral soils, while the organic soil layer in temperate forests retained more <sup>15</sup>N. Additionally, forests retained more <sup>15</sup>NH<sub>4</sub><sup>+</sup> than <sup>15</sup>NO<sub>3</sub><sup>−</sup>, primarily due to the stronger capacity of the organic soil layer to retain <sup>15</sup>NH<sub>4</sub><sup>+</sup>. The mechanisms of <sup>15</sup>N retention varied among ecosystem compartments, with total ecosystem <sup>15</sup>N retention affected by N deposition. Plant <sup>15</sup>N retention was influenced by vegetative and microbial nutrient demands, while soil <sup>15</sup>N retention was regulated by climate factors and soil nutrient supply. Overall, this study emphasizes the importance of climate and nutrient supply and demand in regulating forest N retention and provides data to further explore the impacts of N deposition on forest carbon sequestration.</p>

MIROC6-AGCM dataset for "The Influence of Extratropical Ocean on the PNA Teleconnection: Role of Atmosphere-Ocean Coupling"

<p>MIROC6-AGCM simulation dataset used in the paper “The Influence of Extratropical Ocean on the PNA Teleconnection:  Role of Atmosphere-Ocean Coupling" submitted to <em>Geophysical Research Letters</em>. This dataset includes two experimental datasets called "ACLM" and "AHIST". For "AHIST," it is complements data from another repository (<a href="https://doi.org/10.5281/zenodo.10565870" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10565870</a>).</p> <p> </p> <h2><strong>Data Format</strong></h2> <p>All data are 4-byte binary files accompanied by GrADS control file (http://cola.gmu.edu/grads/) to access the data.</p> <p> </p> <h2><strong>Variables</strong></h2> <p>The dataset uploaded includes a 50-member ensemble of winter-mean (December–January–February) fields for 1980–2020 (a year refers to that including January of each DJF season) over the Northern Hemisphere (0°–360°, 10°–90°N). A part of variables includes data in the tropics (0°–360°, 20S°–90°N).</p> <p>Variables include wind velocities (<em>u, v</em>), pressure velocity (<em>omg</em>), temperature (T), geopotential height (<em>z</em>), eddy momentum (<em>UU</em>, <em>VV</em>, <em>UV</em>), heat (<em>UT</em>, <em>VT</em>), and covariance of height (<em>ZZ</em>) fluxes by transient eddies, 10m wind velocity (<em>u10</em>, <em>v10</em>), 10m wind speed (<em>uvabs</em>), surface pressure (<em>Ps</em>), sensible and latent heat fluxes (<em>sens</em>, <em>evap</em>), precipitation (<em>prcp</em>), cumulus precipitation (<em>prcpc</em>), and precipitation by large-scale condensation (<em>prcpl</em>).</p> <p>The diabatic heating rate consists of only ten ensemble members from 1980-2014. The <em>dtphy</em> is the sum of the diabatic heating terms from different physical processes in the model, which include vertical diffusion (<em>dtvdf</em>), cumulus heating (<em>dtcum</em>), large-scale condensation heating (<em>dtlsc</em>), shallow convection heating (<em>dtscn</em>), cloud physics heating (<em>dtcph</em>), and radiative heating by long wave (<em>dtradl</em>) and short wave (<em>dtrads</em>).</p>

Data for: Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model

<p>This dataset, provided in NetCDF format, supports the research presented in the paper titled "Influence of Anomalous Ocean Heat Transport on the Extratropical Atmospheric Circulation in a High-Resolution Slab-Ocean Coupled Model." Please contact Dr. Sun (ltsun@rams.colostate.edu) if you have any questions.</p>

opencc-by-4.0Nov 2023View details →

Atmospheric Response Matrices (ARMs) computed with AtRIS in: The Atmospheric Influence on Cosmic-Ray-Induced Ionization and Absorbed Dose Rates

<p>Ionization and Dose Atmospheric Response Matrices (ARMs) computed with the Atmspheric Interaction Radiation Simulator (AtRIS) and used in the following publication: <br>The Atmospheric Influence on Cosmic-Ray-Induced Ionization and Absorbed Dose Rates.<br><br>All files are in txt format.<br>Files ending in '_energy_bins.txt', contain the informations about the energy binning of the primary particles used in the AtRIS simulations. <br>Files ending in '_ioni.txt', contain the ionization ARMs computed with the above mentioned energy binning.<br>Files ending in '_dose.txt', contain the ICRU water sphere dose ARMs computed with the above mentioned energy binning.</p>

Model for random atmospheric inhomogeneities in engine noise auralization: Audio files for validation

<p>Illustration of engine noise auralization by DLR Institute of Propulsion Technology obtained with the framework PropNoise, VIOLIN, CORAL. Data associated with the following publication: A. Prescher, A. Moreau, S. Schade, "<a href="https://doi.org/10.1007/s13272-024-00764-4" target="_blank" rel="noopener"><em>Model for random atmospheric inhomogeneities in engine noise auralization</em></a>", CEAS Aeronautical Journal, 2024.</p> <p>Selected binaural audio files to illustrate the impact of random atmospheric inhomogenities on the noise characteristics of a turbofan engine.</p> <p>The corresponding time signals and spectrograms are available in the associated paper in Figure 7.</p>

Data for impacts of topography-based subgrid scheme and downscaling of atmospheric forcing on modeling land surface processes in the conterminous US

<p>The effects of small-scale topography-induced land surface heterogeneity are not well represented in current Earth System Models (ESMs). A topography-based subgrid structure and methods of downscaling of atmospheric forcing from the atmospheric grid to the subgrids of the land model grid (TGUs) have been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to improve representation of the effects of small-scale topography-induced land surface heterogeneity on land surface processes. This study evaluates the impacts of the topography-based subgrid structure and downscaling of atmospheric forcing on modeling land surface processes in E3SM over the conterminous United States (CONUS). For this purpose, ELM simulations are performed using two configurations without (NoD ELM) and with (D ELM) downscaling, both using TGUs derived for the 0.5-degree grids and the same land surface parameters. Simulations using the two ELM configurations are compared over the CONUS domain, regional levels, and at observational sites (e.g., SNOTEL). The CONUS-level results suggest that D ELM simulates more snowfall and snow water equivalent (SWE), higher runoff, and less ET during spring and summer. Regional-level results suggest more pronounced impacts of downscaling over regions dominated by higher elevation TGUs and regions with maximum precipitation occurring during cool seasons. Results at the SNOTEL sites suggest that D ELM has superior capability of reproducing the observed SWE at 83% of the sites, with more pronounced performance over topographically heterogeneous TGUs with their maximum precipitation occurring during cool seasons. The results highlight the importance of improving representation of small-scale surface heterogeneity in ESMs and motivate future research to understand their effects on land-atmosphere interactions, streamflow, and water resources management over mountainous regions.</p> <p>The data utilized to evaluate effects of the topography-based subgrid structure and downscaling of atmospheric forcing in land surface modeling include a TGU level land surface data file, atmospheric forcing to drive the land model, ELM user name list configuration parameters, regionalization variables (topographic regions, snow fraction regions, water versus energy limited regions, and regions of season of maximum precipitation), model restart files for both ELM configurations, and model outputs (grid and subgrid levels), model outputs aggregated to TGUs and grid levels.</p> <p>The data files include:</p> <ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/daily_prism_precip.zip?versionId=be97ca8d-182a-4f1e-9ae3-9da3f2b87e24">DELM.zip</a>: Directory containing the following files relevant to the D ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_all_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file:  grd_level_output_disag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_disag_daily_20220512.nc</li> <li>TGU-level monthly output file: tgu_level_output_all_disag_20221109.nc</li> </ol> <li> <a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/dem_4km4.nc">NoDELM.zip</a>: Directory containing the following files relevant to the NoD ELM configuration and model output files.</li> <ol> <li>Restart file: 202201289.tgu_no_disag_yr1850surfdata.ielm.r05_r05.compy.elm.r.2005-01-01-00000.nc</li> <li>Configuration file: user_nl_elm</li> <li>Aggregated grid-level monthly output file: grd_level_output_nodisag_mnly_run_11_new_20221109.nc</li> <li>Aggregated grid-level daily output file: grd_level_output_nodisag_daily_20220512.nc  </li> <li>TGU-level monthly output file: tgu_level_output_no_disag_20221109.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">shared.zip</a>: Directory containing the following files relevant to both the D ELM and NoD ELM configurations.</li> <ol> <li>Subgrid-based surface data file: MASKED.half_degree_merge.surfdata_0.5x0.5_simyr1850_c200924.pft17.10262022v2.nc</li> <li>Regionalization file used to generate regions based on snow fraction, water versus energy limited state, and seasons of maximum precipitation: half_deg_budyko_curve_analysis_20230104_disag.nc</li> <li>Topographic ratio file used to generate topography-based regions: grd_level_output_nodisag_run_11_new_20221109.nc</li> <li>TGU-level surface elevation data file where surface elevation data are derived from high resolution surface elevation data (90 m) obtained from HydroSHEDS [Lehner et al. 2008, Lehner and Grill 2013]:  half_deg_subgrids_with_PFTs_and_stat_20210403.nc</li> </ol> <li><a href="../api/files/12bc9f1c-be98-4721-8c92-6e23845be441/fr_number.zip?versionId=eacb5b60-9561-47f9-97c6-cd91e96afa1f">SNOTEL_files.zip</a>: Directory containing the following SNOTEL data related files used to evaluate model performance.</li> <ol> <li> SNOTEL list of stations file: SNOTEL_halfdegree_intersect4.csv</li> <li>SNOTEL data files/folders: csv</li> </ol> </ol> <p> </p> <p><strong>References</strong></p> <p>Lehner, B., et al. (2008). "New Global Hydrography Derived From Spaceborne Elevation Data." Eos, Transactions American Geophysical Union <strong>89</strong>(10): 93-94.</p> <p>Lehner, B. and G. Grill (2013). "Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems." Hydrological Processes <strong>27</strong>(15): 2171-2186.</p> <p> </p>

Raw and analyzed data for manuscript "In vitro eradication of Candida albicans biofilm through cold atmospheric plasma: Unravelling the interdependence of exposure and voltage in the antifungal mode of action."

<p><strong><span>Raw and analysed data for the manuscript, to be submitted to Journal of Infection and Public Health.</span></strong></p> <p> </p> <p><strong><span>Abstract:</span></strong></p> <p><strong><span>Introduction:</span></strong><span> Since <em>Candida spp</em>.</span> <span>is the fourth leading cause of healthcare-associated infections globally, the need for novel antifungal agents is increasing among scientists. This study investigates the potential of cold atmospheric plasma for <em>C. albicans</em> biofilm treatment. <strong>Methods:</strong> Our research focused on <em>in vitro</em> <em>C. albicans</em> biofilm response to varying parameters of plasma application, specifically the impact of treatment duration and input voltage. Evaluation of plasma influence on <em>C. albicans</em> was assessed with viability, membrane integrity, and oxidative stress measurements, along with observations of biofilm chemical composition and hyphae growth after plasma treatment. <strong>Results and Discussion:</strong> The higher plasma input voltage and increased exposure tme resulted in lower <em>C. albicans</em> cell viability, with complete reductions observed after 5 min plasma treatment duration across all input voltages tested. The effect of plasma treatment was further confirmed with a microscopic examination after BacLight<sup>® </sup>staining.</span> <span>Low (8 V) CAP exposure could potentially lead to a phenomenon known as hormesis, which was observed in <em>C. albicans</em> 24 h growth measurements. Additionally, intracellular oxidative stress assessment further proved that with prolonged treatment times, the intensity of oxidative stress, increased. Plasma treatment also affected the hyphae, which exhibited signs of contraction and compression. The chemical characteristics revealed that increasing plasma voltage and exposure time also have a distinguished impact on lipids, proteins, and carbohydrates, typical constituents of fungi biofilms. <strong>Conclusion:</strong> This study underscores the potential of plasma as a promising approach in combating <em>Candida spp</em>. biofilms, shedding light on the intricate dynamics of its impact on biofilm viability, morphology and composition.</span></p>

Data Release for "First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data"

<p>This archive contains the electronic version in ROOT and pdf formats of the measurements of oscillation parameters obtained with the analyses from the paper “First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data”.<br><br>It is published in <a href="https://doi.org/10.1103/PhysRevLett.134.011801">Physical Review Letters</a> and is available on the <a href="https://arxiv.org/abs/2405.12488">arXiv:2405.12488 [hep-ex]</a>. </p> <p>**************************************<br>***** Results included in this release<br>**************************************<br>This release includes the results of the measurements of the different oscillation parameters obtained with the four analyses appearing in the paper. This corresponds to various 1D and 2D DeltaChi^2 and posterior probability maps as well as 2D confidence/credible regions for the parameters sin^2(theta13), sin^2(theta23), dm^2_32/|dm^2_31|, delta_cp, J_cp.</p> <p>The results are separated into different files for the four analyses. Additional details on these analyses can be found below, but two of them use a Bayesian approach (producing posterior probabilities and credible intervals/regions) and two of them follow a frequentist approach (producing  DeltaChi^2 maps and confidence intervals/regions). A tag in the TGraph and histogram names also allow to differentiate the different types of intervals/regions: "cred" for credible interval from the Bayesian analysis, "conf" for confidence interval from the frequentist analysis. The tag “posterior” indicates that the object corresponds to a posterior probability distribution, while “chi2” indicates a DeltaChi^2 one.</p> <p>Results for each mass ordering hypothesis are provided, denoted "NO" for normal ordering and "IO" for inverted ordering. The Bayesian files also include results marginalised over the mass ordering, denoted by the tag "both" in the object names. The frequentist files include results profiled over the mass ordering, indicated by a tag “profMO” in the object names.<br>The Bayesian and frequentist results use different conventions for the mass splitting in the inverted ordering: the Bayesian results are in term of #Deltam^{2}_{32} for both NO and IO, whereas the frequentist results are plotted versus #Deltam^{2}_{32} for the NO, and |#Deltam^{2}_{31}| for the IO. </p> <p>A constraint on theta13 from reactor experiment measurements is used for all results in this release. It corresponds to the value in the PDG 2019 review: sin^2(2theta_13)=(8.53+-0.27) x 10^{-2}. This is commonly referred to as "the reactor constraint", and a tag “wRC” is included in the name of the different objects as a reminder that it is used for these results.</p> <p>**************************************<br>***** Brief descriptions of the four analyses<br>**************************************<br>Results are provided for the four analyses mentioned in the “Oscillation analysis” part of the paper. They were given names (Bayesian1, Bayesian2, Frequentist1, Frequentist2) based on the statistical approach they follow.</p> <p>The Bayesian analyses are based on the two T2K analyses described in Eur. Phys. J. C 83, 782 (2023), extended to include the Super-Kamiokande atmospheric data, and with modifications to use the model described in the paper to which the present release is attached to. These analyses use Markov Chain Monte Carlo methods to compute marginal likelihoods for the parameter of interests.  </p> <p>For the frequentist analyses, Frequentist1 is a modified version of Bayesian1, optimized for speed to be able to address the computational challenges of producing frequentist results from an ensemble of pseudo-experiments. Frequentist2 is based on the Super-Kamiokande atmospheric analysis described in PTEP 2019, 053F01 (2019), extended to include the T2K data, and also with modifications to follow the model described in the paper. These two analyses compute profile likelihood on a grid of oscillation parameters of interest to produce measurements of these parameters.</p> <p>In terms of the differences between analyses mentioned in the paper, Bayesian2 is the analysis that does a simultaneous fit of the T2K near detector data with the events observed at SK, and the one for which the momentum scale uncertainty is not correlated between the atmospheric and T2K events observed at SK. The three other analyses use a covariance matrix to propagate the constraint on systematic uncertainties from T2K near detector data to the analysis of the events observed at SK, and treat the momentum scale uncertainty as correlated between atmospheric and T2K far detector events.</p> <p>**************************************<br>***** Example codes<br>**************************************<br>Example codes are provided for each of the four analyses, showing how to produce the pdf file from this analysis from the corresponding ROOT file. How to run these example codes is indicated in the comments at the start of each of the example files.</p> <p>**************************************<br>***** Objects inside the ROOT files<br>**************************************<br>The ROOT objects contained inside the files are named first with an identifier of which parameter(s) are being shown, followed by the reactor constraint tag, followed by the mass ordering tag.</p> <p>For the Bayesian results, there is an additional tag to indicate if the results was obtained with a prior probability uniform in deltaCP (“flatdcp”) or uniform in sin(deltaCP) (“flatsindcp”)</p> <p>A glossary is provided at the end of this readme.</p> <p>**************************************<br>*** 2D regions<br>**************************************<br>Objects of the form:<br>gr2D_varX_varY_wRC_<NO,IO,both>_<conf,cred><68,90,955,997>(_N)<br>are TGraphs corresponding to the 2D confidence ("conf") or credible ("cred") regions for the 2 variables (varX, varY). <br>N is the iterator for different TGraphs corresponding to the same region; these occur when confidence regions are discontinuous (for example when deltaCP loops over from +pi to -pi).</p> <p>68, 90, 955, 997 are the percentage credible/confidence levels.</p> <p>Most of the 2D frequentist regions were computed using the standard DeltaChi^2 values (from the Gaussian case), and therefore have only approximate coverage. However, the {sin^2(theta_23), deltaCP} confidence regions of analysis Frequentist1 were built using critical DeltaChi^2 values computed with the Feldman-Cousins method. To distinguish them from other confidence regions, a tag "FC" is included in the name of the corresponding TGraph.</p> <p>The best fit markers are also provided for the 2D results:<br>gr2D_varX_varY_wRC_<NO,IO,both>_bestfit</p> <p>The best fit markers and contour lines are generally for each MO *separately*, i.e. assuming DeltaChi^2 is 0 at the minimum or that the total posterior probability integrates to 1 in the mass ordering considered. There are some exceptions, in particular some 2D regions for (sin^2(theta_23), dcp) are also provided using a best fit over both MO to allow for comparisons with other experiments using this convention. This special set of contours has an extra tag "globalMO" in its name to distinguish it from the others.</p> <p><br>**************************************<br>*** 1D and 2D histograms<br>**************************************<br>Objects of the form<br>h1D_var_<chi2,posterior>_wRC,_<NO,IO, both, profMO><br>h2D_var1_var2_<chi2,posterior>_wRC_<NO,IO, both><br>are respectively TH1D of the DeltaChi^2 ("chi2") or posterior probability ("posterior") for oscillation parameter "var" or TH2D for the couple of parameters (var1, var2)</p> <p>The Bayesian and frequentist results use different conventions with respect to the mass ordering:<br>- DeltaChi^2 plots use a global minimum over both hierarchies<br>- Posterior probability plots integrate to unity *individually*</p> <p>**************************************<br>***** Critical values for frequentist results<br>**************************************<br>For the 1D plots, critical delta chi2 values obtained with the Feldman-Cousins method are provided for theta23 and deltaCP <br>grCritical_{variable}_chi2_wRC_{NO,IO,profMO}_conf{68, 90, 955}<br>variable: th23, dCP</p> <p>The FC-corrected confidence intervals for these 2 variables can be obtained as the region for which the corresponding 1D DeltaChi^2 histogram is below the grCritical graph of a given level.</p> <p>**************************************<br>***** Additional notes for Bayesian results<br>**************************************<br>For plots involving the mass splitting, the mass ordering is given by the sign:<br>  dm32>0 is normal hierarchy (Delta m^2_{32} > 0)<br>  dm32<0 is inverted hierarchy (Delta m^2_{32} < 0)</p> <p>Note that the posteriors have not been smoothed, and may contain small discontinuities due to MCMC statistical uncertainties.</p> <p>Plots with "_bestfit" appended indicate the point in the 2D parameter space (marginalized over the other parameters) with the highest posterior density, and is not necessarily the global minimum of the likelihood.</p> <p>For the 1D posterior distributions, the user can freely calculate credible intervals from the distributions. It is recommended to start at the point of the highest posterior density, and moving down in posterior density to produce highest posterior credible intervals, which is the kind of credible intervals reported in the paper. </p> <p>**************************************<br>***** Glossary of tags used in objects names<br>**************************************</p> <p>"wRC"   - Uses “reactor constraint” on theta13, sin^2(2theta_13)=(8.53+-0.27) x 10^{-2}<br>"FC"    - Feldman-Cousins<br>"NO"    - Normal mass Ordering<br>"IO"    - Inverted mass Ordering<br>"both"  - Marginalised over normal and inverted mass orderings<br>"profMO"  - Profiled over normal and inverted mass orderings<br>"cred"  - Credible interval<br>"conf"  - Confidence interval<br>"68"  - 68.3% (1 sigma)<br>"90"  - 90%<br>"955" - 95.5% (2 sigma)<br>"997" - 99.7% (3 sigma)<br>"chi2"  - DeltaChi^2 (-2lnL) for parameter<br>"Critical" - Critical DeltaChi^2 computed using Feldman-Cousins method<br>"th13"  - sin^2(theta_13)<br>"th23"  - sin^2(theta_23)<br>"dCP"   - delta CP<br>"dm2"   - Delta m^2_{23} (NO), |Delta m^2_{13} (IO)| for confidence intervals; used for frequentist analyses results.<br>"dm32"  - Delta m^{2_{23} regardless of mass ordering; in the Bayesian analyses, Delta m^2_{23} is always the variable that is plotted.<br>"jarlskog" - Jarlskog invariant<br>"flatdcp" - Using prior probability uniform in deltaCP<br>"flatsindcp" - Using prior probability uniform in sin(deltaCP)</p>

Data for "Why Does Atmospheric Radiative Heating Weaken Midlatitude Cyclones?"

<p>Datasets of eddy kinetic energy, generation of eddy available potential energy due to radiation, and Northern Hemisphere composites of the covariance between temperaturea and radiative heating anomalies for the control and climatological radiation experiments. </p>

Data Products for "The Upper Atmosphere of Uranus from Stellar Occultations II: Revised Temperatures in the Upper Stratosphere and Lower Thermosphere"

<p>From the README file:</p> <p>Organization of data products connected to Saunders et al. (2023, PSJ) and Saunders et al. (2024, PSJ).</p> <p>/forward_modeling_results.csv -- Anderson-Darling test values and critical values for each comparison between Voyager 2 profiles and observed stellar occultation light curves. Voyager 2 profiles were forward modeled into stellar occultation light curves to enable a direct comparison to observed, Earth-based stellar occultation profiles using the Anderson-Darling test of normality. Critical values are provided. See Section 3 of Saunders+23 and Section 2 of Saunders+24.</p> <p>/original_occultation_profiles/ -- Previously published atmospheric profiles from Earth-based stellar occultations. Data were extracted using a data extraction software on published papers. The citation for each data source is provided below. <br>/original_occultation_profiles/1977* -- Elliot et al. (1979)<br>/original_occultation_profiles/1981* -- French et al. (1983)<br>/original_occultation_profiles/1982-04* -- Sicardy et al. (1985)<br>/original_occultation_profiles/1982-05* -- French et al. (1987)<br>/original_occultation_profiles/1983* -- Elliot et al. (1987)</p> <p>/reprocessed_occultation_profiles/ -- All atmospheric profiles resulting from reprocessing the 26 occultation profiles.<br>/reprocessed_occultation_profiles/profiles/ -- Only the atmospheric profiles.<br>/reprocessed_occultation_profiles/profiles/* -- Each individual profile, in original vertical resolution. Columns: radius [km] (from center of Uranus), temperature [k], pressure [microbar], number density [m^-3], refractivity, scale_height [km] (pressure scale height H = kT/mg), y [km] (close-approach distance of the line connecting the viewer and the occulted star to the center of Uranus, see Saunders+23 for description). Units are provided in column headers.<br>/reprocessed_occultation_profiles/errors/ -- Only the errors for the atmospheric profiles.<br>/reprocessed_occultation_profiles/errors/* -- 1-sigma srrors for each individual profile.</p> <p>/atmospheric_models/ -- One-dimensional atmospheric model products. See Section 5 of Saunders+24.<br>/atmospheric_models/model_parameters/model_constants -- Values of constants used in the models. See Table 3 of Saunders+24.<br>/atmospheric_models/model_parameters/model_parameters -- Values and uncertainties of model parameters. See Table 3 of Saunders+24.<br>/atmospheric_models/model_profiles/* -- Profiles for the 9 models provided in Appendix C of Saunders+24. "Average" profiles are fit to the average of the 26 reprocessed stellar occultations; "cool" profiles are fit to the lower bound of the reprocessed occultations; "warm" profiles are fit to the upper bound of the reprocessed occultations. "Best-fit" profiles are the best fit model results; "lower" profiles are the lower bound of the family of generated profiles, meant to serve as a range of uncertainty; "upper" profiles are the upper bound. See Table 3 and Appendix C in Saunders+24 for more information. Units are provided in column headers.</p> <p> </p>

opencc-by-4.0Mar 2018View details →

Selected CO2 and Meteorological Data from BErkeley Atmospheric CO2 Observation Network

<p>Selected CO<sub>2</sub> and meteorological data from BErkeley Atmospheric CO<sub>2</sub> Observation Network (BEACO<sub>2</sub>N) for use in the characterization of the heterogeneity of greenhouse gas concentrations around the San Francisco Bay Area and the constraint of CO<sub>2 </sub>emissions from mobile sources.</p>

opencc-by-nc-4.0Aug 2018View details →

SynFlux: a sythetic dataset of atmospheric deposition and stomatal uptake at flux tower sites

<p>SynFlux quantifies atmosphere-biosphere fluxes of ozone at flux tower sites. It combines micrometeorological measurements at eddy covariance flux towers with air pollution monitoring networks. Reported quantities include deposition velocity, stomatal conductance, deposition flux, and stomatal uptake flux. The method and its performance are documented in</p> <p>Ducker, J. A., Holmes, C. D., Keenan, T. F., Fares, S., Goldstein, A. H., Mammarella, I., Munger, J. W., and Schnell, J. (2018). Synthetic ozone deposition and stomatal uptake at flux tower sites, <em>Biogeosciences Discuss</em>. https://doi.org/10.5194/bg-2018-172</p> <p>Developed by Jason A. Ducker and Christopher D. Holmes at Florida State University, <a href="https://acgc.eoas.fsu.edu">https://acgc.eoas.fsu.edu</a></p>

opencc-by-4.0Dec 2018View details →

Revisiting the impacts of Stochastic Multicloud model on the MJO using low-resolution ECHAM6.3 atmosphere model

<p>There are the corresponding source codes and input data used to run the numerical experiments. Analysis scripts and model results are also included.</p>

opencc-by-4.0Jan 2019View details →

Some atmospheric model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes

<p>These files contain 2-D fields of atmospheric T and P maps, as climatologies averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, and from the CESM Large Ensemble control simulation (LENS), the basis for two of the plots in our manuscript that is, as of January 2019, in review at JAMES.  All of these data files are binary, written sequentially, 288 x 192.  Also included are some PowerPoint-generated pdf images of the two fields.</p>

opencc-by-4.0Mar 2019View details →

New full evolutionary sequences of H- and He-atmosphere massive white dwarf stars using MESA

<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2018MNRAS.480.1547L">New full evolutionary sequences of H- and He-atmosphere massive white dwarf stars using MESA</a></p>

opencc-by-4.0Jan 2018View details →

Dataset - Assessing the Added Value of the Intermediate Complexity Atmospheric Research Model (ICAR) for Precipitation in Complex Topography

<p>Abstract. The coarse grid spacing of global circulation models necessitates the application of downscaling techniques to investigate the local impact of a changing global climate. Difficulties arise for data sparse regions in complex topography which are computationally demanding for dynamic downscaling and often not suitable for statistical downscaling due to the lack of high quality observational data. The Intermediate Complexity Atmospheric Research Model (ICAR) is a physics-based model that can be applied without relying on measurements for training and is computationally more efficient than dynamic downscaling models. This study presents the first in-depth evaluation of multi-year precipitation time series generated with ICAR on a 4 × 4 km<sup>2</sup> grid for the South Island of New Zealand for an eleven-year period, ranging from 2007 until 2017. It focuses on complex topography and evaluates ICAR at 16 weather stations, eleven of which are situated in the Southern Alps between 700 m MSL and 2150 m MSL. ICAR is assessed with standard skill scores and the effect of model top elevation, topography, season, atmospheric background state and synoptic weather patterns on these scores are investigated. The results show a strong dependence of ICAR skill on the choice of the model top elevation, with the highest scores obtained for 4 km above topography. Furthermore, ICAR is found to provide added value over its ERA-Interim reanalysis forcing data set for alpine weather stations, improving mean squared errors (MSE) by up to 53 % and 30 % on median. It performs similarly during all seasons with an MSE minimum during winter, while flow linearity and atmospheric stability were found to increase skill scores. ICAR scores are highest during weather patterns associated with flow perpendicular to the Southern Alps and lowest for flow parallel to the alpine range. While measured precipitation is underestimated by ICAR, these results show the skill of ICAR in a real-world application, and may be improved upon by further observational calibration or bias correction techniques.<br> Based on these findings ICAR shows the potential to generate downscaled fields for long term impact studies in data sparse regions with complex topography.</p>

opencc-by-4.0May 2019View details →

Supporting Data for "Antarctic Sea Ice Expansion, Driven by Internal Variability, in the Presence of Increasing Atmospheric CO2"

<p>Supporting CESM1 data for "Antarctic Sea Ice Expansion, Driven by Internal Variability, in the Presence of Increasing Atmospheric CO2", submitted to Geophysical Research Letters in May 2019.</p> <p>Provides monthly time series of surface temperature (TS) and sea ice fraction (ICEFRAC) for each of the three 40-yr CO2Ramp ensemble members.  </p> <p>Data for the CESM1 Large Ensemble Control run are available to the public through NCAR/UCAR.  Access instructions can be found here:  <a href="http://www.cesm.ucar.edu/projects/community-projects/LENS/">http://www.cesm.ucar.edu/projects/community-projects/LENS/</a></p>