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364 results for “Convection”
Rain gauge data used in the study "Characteristics of Precipitation and Mesoscale Convective Systems over the Peruvian Central Andes in Multi 5-Year Convection-Permitting Simulations"
<p>The rain gauge data in Peru and Brazil used in the study,</p> <p>Yongjie Huang, Ming Xue, Xiao-Ming Hu, et al. Characteristics of Precipitation and Mesoscale Convective Systems over the Peruvian Central Andes in Multi 5-Year Convection-Permitting Simulations. <em>ESS Open Archive .</em> November 14, 2023.<br><span>DOI: <a href="https://doi.org/10.22541/essoar.170000370.07634797/v1" target="_blank" rel="noopener noreferrer">10.22541/essoar.170000370.07634797/v1</a></span></p> <p><span>The original data source:</span></p> <ul> <li>The rain gauge data in Peru are available at <a href="https://piscoprec.github.io/webPISCO/en/raingauges">https://piscoprec.github.io/webPISCO/en/raingauges</a> (last access: 18 July 2021).</li> <li>The rain gauge data in Brazil are available at <a href="https://bdmep.inmet.gov.br">https://bdmep.inmet.gov.br</a> (last access: 19 January 2023).</li> </ul>
Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations: data and code
<p>This is the data and code used for a journal paper entitled "Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations", written by Mu-Ting Chien and Daehyun Kim in 2024. This paper is in minor revision in the Journal of Advances in Modeling Earth System. The submitted paper is here: (https://essopenarchive.org/doi/full/10.22541/essoar.171322728.86206700/v1).</p>
Simulation data for convection in radiatively heated melt ponds on sea ice
<p>This dataset includes data and processing code from simulations of radiatively heated convection in melt ponds on sea ice. </p> <p> </p> <p>Simulation results produced using code written by Andrew Wells and Tom Langton for use with and exploiting examples from the code Dedalus:</p> <p>http://dedalus-project.org/index.html</p> <p> </p> <p>Contact andrew.wells@physics.ox.ac.uk for further details. </p> <p>The data included in this version correspond to figures 3 and S5 , movies S2 and S3, and code described in supporting information in the study:</p> <p><strong>Salinity control of thermal evolution of late summer melt ponds on Arctic sea ice </strong></p> <p>Joo-Hong Kim<sup>1</sup>, Woosok Moon<sup>2,3</sup>, Andrew J. Wells<sup>4</sup>, Jeremy P. Wilkinson<sup>5</sup>, Tom Langton<sup>4</sup>, Byongjun Hwang<sup>6,7</sup>, Mats A. Granskog<sup>8 </sup>and David Rees Jones<sup>9</sup></p> <p><sup>1</sup>Korea Polar Research Institute, Incheon, South Korea</p> <p><sup>2</sup>Nordic Institute for Theoretical Physics, Stockholm, Sweden</p> <p><sup>3</sup>Department of Mathematics, Stockholm University, Stockholm, Sweden</p> <p><sup>4</sup>Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK</p> <p><sup>5</sup>British Antarctic Survey, Cambridge, UK.</p> <p><sup>6</sup>Scottish Association for Marine Science, Oban, UK</p> <p><sup>7</sup>University of Huddersfield, Huddersfield, UK</p> <p><sup>8</sup>Norwegian Polar Institute, Fram Centre, Tromsø, Norway</p> <p><sup>9</sup>Dept. of Earth Sciences, University of Oxford, Oxford, UK</p> <p> </p> <p>Citation:</p> <p>Kim, J.-H., Moon, W., Wells, A. J., Wilkinson, J. P., Langton, T., Hwang, B., Granskog, M. A., & Rees Jones, D. W. (2018). Salinity control of thermal evolution of late summer melt ponds on Arctic sea ice. Geophysical Research Letters, 45. https://doi.org/10.1029/2018GL078077</p> <p>Alternative weblink:</p> <p>https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018GL078077</p> <p> </p> <p>v1: submitted during review of the manuscript.</p> <p>v2: updated with details of accepted publication.</p>
Convective boundary mixing in a post-He core burning massive star model: Collapse and starlog data
<p>The starlog data and collapse profiles from the publication, Convective boundary mixing in a post-He core burning massive star model. </p> <p>The full directories including the MESA profiles can be found here: http://www.canfar.net/storage/list/nugrid/data/projects/Davis2019_CBM_M25</p>
On the discrepancy of modelling the heat transferfor pure natural convection
<p>Data set for the proceedings contribution (included in data repository)</p> <p><strong>On the discrepancy of modelling the heat transferfor pure natural convection</strong><br> by <strong>A. Belt, M. Böhler, L. Rommeswinkel and L. Arnold</strong></p> <p>at the European symposium on fire safety sciences (ESFSS2018) in 2018.</p>
Extended Data for Publication "Testing the Spectroscopic Extraction of Suppression of Convective Blueshift"
<p>Efforts to detect low-mass exoplanets using stellar radial velocities (RVs) are currently limited by magnetic photospheric activity. Suppression of convective blueshift is the dominant magnetic contribution to RV variability in low-activity Sun-like stars. Due to convective plasma motions, the magnitude of RV contributions from the suppression of convective blueshift is roughly correlated with the depth of formation of photospheric spectral lines used to compute the RV time series. Meunier et al. (2017), used this relation to demonstrate a method for spectroscopic extraction of the suppression of convective blueshift in order to isolate RV contributions, including planetary RVs, that contribute equally to the timeseries for each spectral line. In this publication, we extract disk-integrated solar RVs from observations over a 2.5 year time span made with the solar telescope integrated with the HARPS-N spectrograph at the Telescopio Nazionale Galileo (La Palma, Canary Islands, Spain). We apply the methods outlined by Meunier et al. (2017) - as part of this analysis, we fit Gaussian line profiles to 765 iron lines measured over 457 exposures.</p> <p>Here, we provide the complete line list (Table 1; Table1_LineList.csv) used in our analysis, and the resulting RVs time series in their entirety (Table 3; Table3_TimeSeries.csv). Wavelengths are given in Angstroms, and RVs in m/s.</p> <p>We also include 4 CSV files with the line fit parameters of each line profile: Each row corresponds to a single exposure time (corresponding to the JDs in Table 3) and each column corresponds to a specific spectral line (with wavelength specified in Table 1).</p> <p>We fit each spectral line to a Gaussian of the form:</p> <p><span class="math-tex">\(f(\lambda) = p_1 - p_2 \exp \left[- {1 \over 2} \left({{\lambda - p_3} \over p_4}\right)^2 \right]\)</span></p> <p>p<sub>1</sub> is the continuum level in arbitrary units (LineProfiles_Continuum.csv)<br> p<sub>2</sub> is the line strength in arbitrary units (LineProfiles_Amplitude.csv)<br> p<sub>3</sub> is the line shift in Angstroms (LineProfiles_Shift.csv)<br> p<sub>4</sub> is the line width in Angstroms (LineProfiles_Width.csv)</p>
The dataset for the paper titled "Convergence of convective updraft ensembles with respect to the grid spacing of atmospheric models" by Sueki et al.
<p>This repository contains data, analysis codes, and model configuration files for NICAM and SCALE-RM, which is used for the paper titled "Convergence of convective updraft ensembles with respect to the grid spacing of atmospheric models" by Sueki et al.</p> <p>There are 6 fortran codes at the top directory.<br> 1. deep_convective_area.f90<br> 2. algorithm01.f90<br> 3. algorithm02.f90<br> 4. statistics_algorithm01.f90<br> 5. statistics_algorithm02.f90<br> 6. spectrum_calculation.f90<br> You can find description for each code at the top it.</p> <p>All data are archived in subdirectories. Directory tree is following:</p> <p>Sueki-et-al.2020/<br> |-- exp-a<br> | |-- dx0200<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0400<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0800<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx1600<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | `-- dx3200<br> | |-- algorithm01<br> | `-- algorithm02<br> | |-- r00000-00500<br> | |-- r00500-01000<br> | |-- r01000-02000<br> | |-- r02000-04000<br> | |-- r04000-08000<br> | `-- r08000-99999<br> |-- exp-b<br> | |-- dx0050<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0100<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0200<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0400<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | `-- dx0800<br> | |-- algorithm01<br> | `-- algorithm02<br> | |-- r00000-00500<br> | |-- r00500-01000<br> | |-- r01000-02000<br> | |-- r02000-04000<br> | |-- r04000-08000<br> | `-- r08000-99999<br> |-- model-configuration-file<br> | |-- nicam<br> | `-- scale-rm<br> | |-- exp-a<br> | | |-- init<br> | | |-- pp<br> | | `-- run<br> | `-- exp-b<br> | |-- init<br> | |-- pp<br> | |-- run01<br> | |-- run02<br> | `-- run03<br> |-- spectrum<br> `-- statistics</p>
South American Regional Indices on Convectively Coupled Waves
<p>This dataset provides regional indices for Convectively Coupled Waves (CCWs) over South America. The indices include data on wave activity a covering a specified temporal and spatial range. The data is valuable for researchers studying the influence of CCWs on regional climate and weather patterns.</p> <p><strong>Keywords</strong>: South American Regional Indices, Convectively Coupled Waves, Climate Data, CCWs Indices</p> <p><strong>Data Description</strong>:</p> <ul> <li><strong>Content</strong>: Includes indices for CCWs and time series data.</li> <li><strong>Format</strong>: Available in text and NetCDF format.</li> <li><strong>Coverage</strong>: Temporal coverage from 1979 to 2023 spatial coverage includes tropical South America domain.</li> </ul> <p><strong>Methodology</strong>: The indices are derived using EOF analisysis after filtering OLR.</p> <p><strong>Usage</strong>: The data can be used to analyze the seasonal and interannual variability of CCWs and their impact on South American weather patterns. Example applications include climate modeling, weather prediction, and atmospheric research.</p> <p><strong>Acknowledgments</strong>: This work was supported by NOAA grant number NA22OAR4310611 and NSF CAREER grant number 2236433 .</p> <p><strong>License</strong>: This dataset is licensed under Creative Commons Attribution 4.0 International License.</p> <p><strong>Contact Information</strong>: For more information, please contact : mayta@wisc.edu</p> <p> </p>
GoAmazon convective systems datasets (systems and systems_filtered)
<p>These datasets show convective systems characteristics extracted from TATHU algorithm applied to SIPAM weather radar data during GoAmazon experiment. Both zips stores tables in GeoJSON format with point, grid and geometry information. <a href="https://geopandas.org/en/stable/" target="_blank" rel="noopener">GeoPandas</a> can open the zip files directly: </p> <pre><code>import geopandas as gpd gds = gpd.read_file("zip:///home/[path to file]/systems.zip")</code></pre> <p>More information about the datasets can be found in the paper "Deep convection lifecycle characteristics: a database from GoAmazon experiment" (Lopes et al., <em>to be submitted</em>).</p>
Relative Random Errors in the Convective Atmospheric Boundary Layer Estimated by the Relaxed Filtering Method from Large Eddy Simulations
<p>Data supporting the paper "How representative are uncrewed aircraft system measurements of the convective boundary layer?" by Brian R. Greene, Leia M. Otterstatter, and Scott T. Salesky, submitted to Geophysical Research Letters in 2024. Data are postprocessed from large-eddy simulations of the convective atmospheric boundary layer that are used to produce the figures within the paper. Details on the production of these files are included in the supplementary informatin of this paper.</p>
Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse
<p>Data products from ApJ article Three-Dimensional Hydrodynamic Simulations of Convective Nuclear Burning In Massive Stars Near Iron Core Collapse, 2021. Four 3D core-collapse supernova progenitor models. Works that utilize these progenitor models are required to cite article. The models were evolved to times listed in Table 1 of the article, the collapse time according to the 1D MESA model. All 3D data are in FLASH4 format using the HDF5 data structure. </p>
Datasets for Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China
<p>These datasets are the processed and refined data that support and lead to the described results and allow other readers to assess the conclusions in the paper, entitled “<strong>Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China </strong> ”.</p>
Supplementary data for the manuscript entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" (JGR Atmospheres)
<p>This dataset contains additional material to reproduce the simulation and some of the figures of the manuscipt entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" in the Journal of Geophysical Reasseach - Atmospheres.</p>
Kilometer-scale global warming simulations and active sensors reveal changes of tropical deep convection
<p>This zip file contains data and codes to reproduce the figures of a manuscript on X-SHiELD.</p> <p>Contact mbolot@princeton.edu for questions.</p>
Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals
<p>Heavens, Nicholas G. (2023), “Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals”, Zenodo, V1, doi: 10.5281/zenodo.7828010.</p> <p> </p> <p><strong>Title:</strong> Probabilistic Estimates of Convective Instability in Individual MRO-MCS Temperature Retrievals</p> <p> </p> <p><strong>Author:</strong> Nicholas G. Heavens, Space Science Institute, Boulder, CO, USA and London, UK (nheavens@spacescience.org)</p> <p> </p> <p>Date: 15 April 2023</p> <p> </p> <p><strong>Overview</strong>: This dataset contains analyses of convective instabilities in individual MRO-MCS temperature profiles for a manuscript currently under review at the <em>Planetary Science Journal</em>.</p> <p> </p> <p>Averaged data products based on these analyses already have been archived as Heavens (2022):</p> <p> </p> <p>The data products are estimates of the probability of convective instability derived from diagnosis of convective instability in retrieved temperature profiles from Mars Climate Sounder on board Mars Reconnaissance Orbiter (MRO-MCS)</p> <p> </p> <p>If you are using this dataset and are feeling confused or wish there were some additional information from the article in this dataset, please contact me. A complete account of the contents and a restatement of this description is included as MCS_CONVINST_INDFILE_SURVEY_DESC.PDF</p> <p> </p> <p>Acknowledgments: The archiving of this dataset is supported by NASA’s Mars Data Analysis Program (80NSSC19K1215).</p> <p> </p> <p><strong>Contents:</strong></p> <p> </p> <ol> <li><em>MCS_CONVINST_INDFILE_SURVEY_DESC.pdf</em></li> </ol> <p>A copy of this description in PDF format.</p> <p> </p> <ol> <li><em>MCSconvinst_indivncfiles.tar.gz</em></li> </ol> <p>This compressed tar archive contains 63196 Network Common Data Format (netCDF) files (~ 20 GB uncompressed) named in the form:</p> <p> </p> <p>xxxxxx_convinst.nc, where xxxxxx is the re-centered orbit number of MRO defined by Heavens et al. (2018). The contents of this file type is:</p> <p> </p> <p>Variables:</p> <p> </p> <ol> <li>entrynum: Number to index retrievals in the file</li> <li>Pgrid: The pressure (Pa) level grid</li> <li>lapserateunstableprob_rets: The probability (%) of convective instability in an individual profile at a given pressure level</li> <li>altrets: The altitude grid (km) of each individual profile in relative to the Mars Orbiter Laser Altimeter (MOLA) areoid</li> <li>ampmrets: A flag to indicate whether MRO is in its ascending PM orbit (~ 15:00 LST at the Equator) or in its descending AM orbit (~ 3:00 at the Equator). Precise local time can be estimated by using the MRO-MCS dataset directly or using sclkrets.</li> <li>latrets: The latitude of the profile in degrees north.</li> <li>longrets: The longitude of the profile in degrees east</li> <li>loopflagrets: A flag to indicate the presence of nearby loops; flag = 1 indicates the presence of a nearby loop.</li> <li>lsrets: Areocentric longitude (degrees) of each individual profile</li> <li>myrets: Mars Year of each individual profile</li> <li>sclkrets: SCLK (seconds since 1 January 1980 00:00:00 UTC) for each individual profile</li> </ol> <p> </p> <p><strong>References</strong></p> <p> </p> <p>Heavens, N. (2022), Epitomic Data for a Multiannual Record of Convective Instability in Mars's Middle Atmosphere from the Mars Climate Sounder, Mendeley Data, V1, doi: 10.17632/fmgvbpwdk7.1</p> <p> </p> <p>Heavens, N.G., A. Kleinböhl, M.S. Chaffin, J.S. Halekas, D.M. Kass, P.O. Hayne, D.J. McCleese, S. Piqueux, J.H. Shirley, and J.T. Schofield, 2018, Hydrogen escape from Mars enhanced by deep convection in dust storms, <em>Nature Astron</em>., 2, 126–132, doi: 10.1038/s41550-017-0353-4.</p>
Potential impact of assimilating visible and infrared satellite observations compared to radar reflectivity for convective‐scale NWP
<p>Contains</p> <ul> <li>raw_data <ul> <li>nature run initial conditions for the cases "random" and "warm-bubble"</li> <li>ensemble initial condition sounding profiles</li> </ul> </li> <li>evaluation_metrics <ul> <li>csv files of FSS, RMSE, MAE, ensemble spread, mean difference<br> to reproduce figures of FSS, RMSE and spread in the paper and more.<br> See the README.txt</li> </ul> </li> <li>figures <ul> <li>Map_VIS06 ... Simulated satellite images of visible reflectance for ensemble members and truth</li> <li>Map_VIS06_probability ... Map of ensemble probability for visible reflectance > 0.6 and the nature in red contours</li> </ul> </li> </ul> <p> </p>
Timelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign
<p>Timelapse footage of clouds taken during the<a href="https://cloudsense.ac.uk/dcmex/"> Deep Convective Microphysics Experiment</a> (DCMEX) research project, funded by the UK Natural Environment Research Council.</p> <p>Cameras were pointed towards the Magdalena mountains. The main camera location was Socorro airport, a secondary location was Econolodge, Socorro, and a third location was Magdalena airport.</p> <p>These are a subset of the footage collected. 20s interval photographs from a number of days are available from another archive.</p> <p>During these timelapses the FAAM aircraft was flying through the clouds collecting thermodynamic, dynamics, microphysics and aerosol <a href="https://catalogue.ceda.ac.uk/uuid/b1211ad185e24b488d41dd98f957506c">measurements</a>. Radars were also sometimes operational.</p> <p>The full campaign and data is decribed in detail in <a href="https://essd.copernicus.org/articles/16/2141/2024/">Finney et al. (2024) ESSD</a>.</p> <p><strong>Video descriptions</strong></p> <p>19th July - A number of convective cloud bursts, but no anvil formed over the Magdalena mountains during this footage.</p> <p>23rd July - Cumulus are present and growing from the start of the footage. Cloud bases stay rooted to the mountain. Shear appears low until a detrainment layer is reached.</p> <p>27th July - Footage begins with clear skies. Strong low levels winds carry clouds northward. Deep convective clouds form and are detrained south westward. Later in the footage scene becomes overcast with high cloud, and this suppresses the earlier deep convection. Late in the camera 1 footage a gust front cloud passes across the scene.</p> <p>29th July - Footage taken from the Magdalena airport and begins with fast moving cumulus cloud. The cloud grows and moves over the camera.</p> <p>31st July - Cumulus clouds form almost immediately over the Magdalena mountains and steadily grow from multiple thermals. Cloud bases stay rooted to the mountain, but shear aloft carries cloud northward from a detrainment layer. The cloud takes a structure similar to anvil but winds are strong and cloud in the detrainment layer appears to be fairly long-lived, warping the anvil shape somewhat.</p> <p>2nd August - Footage begins overcast but clears. Cumulus clouds and congestus begin to form, with strong low level winds carrying them southward.</p> <p><strong>Related datasets</strong></p> <p>Facility for Airborne Atmospheric Measurements; Finney, D.; Blyth, A.; Gallagher, M.; Wu, H.; Nott, G.J.; Biggerstaff, M.; Sonnenfeld, R.G.; Daily, M.; Walker, D.; Dufton, D.; Bower, K.N.; Boeing, S.; Choularton, T.W.; Crosier, J.; Groves, J.; Field, P.; Coe, H.; Murray, B.J.; Lloyd, G.; Marsden, N.A.; Flynn, M.; Hu, K.; Thamban, N.M.; Williams, P.I.; Connolly, P.J.; McQuaid, J.B.; Robinson, J.; Cui, Z.; Burton, R.R.; Carrie, G.; Moore, R.; Abel, S.J.; Tiddeman, D.; Aulich, G.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Morris, F.; Hampton, J. (2022): DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment. NERC EDS Centre for Environmental Data Analysis, <em>30 April 2024</em>. <a href="https://dx.doi.org/10.5285/B1211AD185E24B488D41DD98F957506C">https://dx.doi.org/10.5285/B1211AD185E24B488D41DD98F957506C</a></p> <p>Individual image archive for camera 1 - Finney, D.; Groves, J.; Walker, D.; Dufton, D.; Moore, R.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Blyth, A. (2023): DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022. NERC EDS Centre for Environmental Data Analysis, 15 December 2023. <a href="https://dx.doi.org/10.5285/b839ae53abf94e23b0f61560349ccda1">https://dx.doi.org/10.5285/b839ae53abf94e23b0f61560349ccda1</a></p> <p>Individual image archive for camera 2 - Finney, D.; Groves, J.; Walker, D.; Dufton, D.; Moore, R.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Blyth, A. (2023): DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022. NERC EDS Centre for Environmental Data Analysis, 15 December 2023. <a href="https://dx.doi.org/10.5285/d1c61edc4f554ee09ad370f6b52f82ce">https://dx.doi.org/10.5285/d1c61edc4f554ee09ad370f6b52f82ce </a></p> <p>Oklahoma University radar data - <a href="https://doi.org/10.5281/zenodo.8051426">https://doi.org/10.5281/zenodo.8051426</a></p> <p> </p>
Land-Locked Convection as a Barrier to MJO Propagation across the Maritime Continent
<p>This dataset contains the subset model output variables that were used to derive the results presented in the following publication:</p> <p>Savarin, A. & S. S. Chen (2023): Land-Locked Convection as a Barrier to MJO Propagation across the Maritime Continent. <em>Journal of Advances in Modeling Earth Systems</em>, 15, e2022MS003503. https://doi.org/10.1029/2022MS003503</p> <p>For the sake of saving space, the model output variables (file names starting with <em>uwincm_</em>) are separated into 2D fields (latitude, longitude, land mask, precipitation, and surface zonal winds) for the duration of model simulation and 3D fields (pressure, temperature, and 3D winds) for selected times used in the manuscript. </p> <p>Additionally, results of large-scale precipitation tracking for MJO are stored in the file names that begin with <em>lpt_. </em></p> <p>HYCOM (ocean model) bathymetry and surface temperature for initial conditions are stored in <em>HYCOM_IC_</em> files. </p> <p> </p>
InSight's seismic and meteorological data related to the Martian convective vortices
<p><strong>Overview:</strong></p> <p>This repository includes the catalog related to Martian convective vortices observed by NASA's InSight mission. The detailed description is made in the JGR Planet paper entitled "Systematic catalog of Martian convective vortices observed by InSight" by Onodera et al. When you use the information in the catalog, please refer to the following citation.</p> <ul> <li>Onodera, K. et al. (2023), InSight's seismic and meteorological data related to the Martian convective vortices, Zenodo,<em><strong> </strong></em>doi:10.5281/zenodo.7801343<em><strong>.</strong></em></li> </ul> <p><strong>Files:</strong></p> <p>The first numbers in each file name correspond to the ID number included in the catalog file (InSight_CV_Catalog.pickle). All files are in csv format including time in Local Mean True Time in sol, respective observation records. If a number is missing, that means the corresponding data were not available on that sol (at least with the sampling rate we focused on in our paper).</p> <ul> <li><strong>InSight_CV_Catalog.pickle</strong>: It includes all estimated parameters presented by Onodera et al. (2023).</li> <li> <p><strong>PS.zip</strong>: 20 min long pressure data centered at the maximum pressure drop time (LMST, Pressure).</p> </li> <li> <p><strong>VBB_ACC.zip</strong>: 20 min long acceleration data centered at the maximum pressure drop time (LMST, Z comp., N comp., E comp.).</p> </li> <li> <p><strong>WSpeed_WDir_ATemp_calib.zip</strong>: 20 min long calibrated wind & air temperature data centered at the maximum pressure drop time (LMST, Wind speed, Wind direction, Air temperature).</p> </li> </ul>
Cloud Feedbacks in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2023).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2023).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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