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156 results for “troposphere”
GNSS troposphere products from a network of low-cost GNSS receivers, Wroclaw, Poland, March-April 2021
<p>This dataset contains multi-GNSS troposphere products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties. These products were obtained using 3 processing strategies:</p> <p>1) real-time (for details see https://link.springer.com/article/10.1007/s10291-020-01014-w, under to "advanced strategy" configuration, with the exception that only GPS and Galileo observations were considered);</p> <p>2) near real-time (NRT, for details see http://egvap.dmi.dk/);</p> <p>3) final (using CSRS online service, https://webapp.geod.nrcan.gc.ca/geod/tools-outils/ppp.php).</p> <p>Products are stored as standard Matlab MAT files. Each file contains a set of table arrays (Matlab format). Each table array contains the selected set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using the in-build Matlab function "writetable.m".</p> <p>For convenience, the same information is stored in alternative data formats:</p> <p>1) for NRT and Final products: troposphere SINEX v1 (TRO / TRP)</p> <p>2) for real-time products: semicolon-delimited text files, with a self-explanatory header line; each file contains daily products for one station.</p>
The ground-based MUSICA dataset: Tropospheric water vapour isotopologues (H216O, H218O and HD16O) as obtained from NDACC/FTIR solar absorption spectra
<p>MUSICA (“MUlti-platform remote sensing of Isotopologues for investigating the Cycle of Atmospheric water”, http://www.imk-asf.kit.edu/english/musica.php) is a European Research Council (ERC) project. The project has developed tropospheric water vapour isotopologue retrievals (H2O and H2O-δD pairs) using ground-based FTIR spectra as well as thermal nadir spectra measured by the satellite sensor IASI. H2O-δD pairs allow studying tropospheric water transport pathways and in combination with models they can improve our understanding of important climate feedback mechanisms (see also WCRP Grand Challenges: http://www.wcrp-climate.org/grand-challenges).<br /> <br /> For MUSICA, the FTIR spectra have been analysed centrally at KIT using uniform and consistent retrieval settings, thereby guaranteeing ultimate consistency of the retrieval products generated for different FTIR stations. The FTIR products are H2O profiles for the lower, middle and upper troposphere as well as H2O-δD pairs for the lower and middle troposphere. The data have been produced for 12 FTIR stations and date back to 1996.</p> <p>The dataset has been extensively characterized and validated (theoretically and empirically). Furthermore, the spectra have been used to perform uniform retrievals of XCO<sub>2</sub>, which is then used for documenting the long-term stability of these kind of FTIR data. The data are provided in the form of two data types. The first type ("ftir.iso.h2o") is best-suited for tropospheric water vapour distribution studies that disregard the different isotopologues (comparison with radiosonde data, analyses of water vapour variability and trends, etc.). The second type ("ftir.iso.post.h2o") is needed for analysing moisture pathways by means of H<sub>2</sub>O-δD pair distribution.</p> <p>The data format is hdf4 and the files have been generated in compliance with GEOMS (Generic Earth Observation Metadata Standard). The complete MUSICA NDACC/FTIR dataset is also publicly available via the NDACC database (ftp://ftp.cpc.ncep.noaa.gov/ndacc/MUSICA).</p> <p>Details on the characteristics of the dataset are described in the paper "Tropospheric water vapour isotopoloque data (H<span class="math-tex">\(_{2}^{16}\)</span>O, H<span class="math-tex">\(_{2}^{18}\)</span>O and HD<sup>16</sup>O) as obtained from NDACC/FTIR solar absorption spectra" that has been prepared for ESSD in the context of the special issue “25th anniversary of NDACC”.</p>
Investigation of spatial and temporal variability in lower tropospheric ozone from RAL Space UV-Vis satellite products - Dataset
<p>This data set represents a long-term (1996-2017) harmonised record of lower tropospheric ozone (surface - 450 hPa or surface - approximately 6 km) from satellite instruments. These instruments include the Global Ozone Monitoring Experiment (GOME-1, 1996–2002), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2004) and the Ozone Monitoring Instrument (OMI, 2005–2017). These original products were produced by the Rutherford Appleton Laboratory (RAL) Space using the retrieval scheme described by Miles et al., (2015 - doi:10.5194/amt-8-385-2015). Pre-print of accepted manuscript can be found at https://doi.org/10.5194/egusphere-2023-1172.</p>
Sensitivity experiment data using the CHASER chemical transport model for investigation of lower-tropospheric spring ozone enhancement over Hanoi
<p>This is the data from the numerical model experiment for investigating the relative importance of different emission source regions on the spring ozone enhancement in the lower troposphere over Hanoi, Vietnam. The details of the investigation are written in the paper by Ogino et al. (2022, Journal of Geophysical Research, Atmosphere, in revision).</p> <p><strong>Experiment description</strong></p> <p>We performed sensitivity experiments using the global chemical-transport model, CHASER (Sudo et al., 2002) with T42 horizontal resolution (approximately 2.8 degrees longitude × 2.8 degrees latitude) and 32 vertical layers from the surface up to 10 hPa in sigma coordinate. The two-hourly model outputs interpolated onto the constant pressure levels at 1000, 990, 970, 930, 870, 790, 700, 610, 530, 460, 400, 350, 300, 260, 230, 200, 176, 153, 133, 116, and 100 hPa were used in this study. Note that the updated model, MIROC-Chem (Miyazaki et al., 2017; Watanabe et al., 2011), includes more detailed chemical processes for both troposphere and stratosphere. Nevertheless, CHASER already includes the most important chemical processes in the NOx-CO-Ozone reactions and can be used to evaluate the impact of NOx emissions on ozone productions. In addition, the simulated ozone performance, as well as ozone response to NOx emissions, are comparable between CHASER and MIROC-Chem (Miyazaki et al., 2020). Thus, the results should not be sensitive to the choice of model.</p> <p>The surface emissions of major ozone precursors, such as carbon monoxide (CO), nitrogen oxide (NOx), and nonmethane hydrocarbons, were included in the model based on the published emission inventories (the Emission Database for Global Atmospheric Research (EDGAR) version 4.2 (EC-JRC/PBL, 2011), the monthly Global Fire Emissions Database (GFED) version 3.1 (van der Werf et al., 2010), and monthly mean Global Emissions Inventory Activity (GEIA) (Graedel et al., 1993)). We employed daily NOx and CO emissions that were optimized using the assimilation of satellite NO2 and CO measurements, where the a priori emissions were constructed based upon bottom-up emission inventories (Miyazaki et al., 2015; 2017). These emissions, including both anthropogenic and biomass burning components, used were obtained from the Tropospheric Chemistry Reanalysis version 1 (TCR-1, Miyazaki et al., 2015) and enabled us to evaluate the emission impacts for individual sources.</p> <p>In the sensitivity experiments, we eliminated the emissions of ozone precursors from the following three source regions: the Indian subcontinent, the northern Indochina Peninsula, and southern China. We conducted spin-up calculations with the optimized emissions for all regions (i.e., standard emissions) from January 1st to the end of February in each year for 10 years from 2005 to 2014. Then, we performed four types of experiments from March 1st to 21st: the control experiment with the standard emissions, and the three sensitivity experiments with the elimination of emission from the above-mentioned three regions, namely the Indian subcontinent, the northern Indochina, the southern China experiments. Because of the non-linear chemistry, the cumulative response from the sensitivity calculations can be different from the total ozone response in the control simulation to some extent as shown by the HTAP modeling works (Turnock et al., 2018; Wild et al., 2012). Nevertheless, they provided important information on the relative contributions of emission sources from different regions. The results of the sensitivity experiments will be compared with the control experiment to investigate the relative contributions of individual emission sources to the ozone enhancement over Hanoi.</p> <p><strong>Files</strong></p> <ul> <li>O3_Fullyear_[YYYY].nc: The 2-hourly data of ozone mixing ratio obtained in the control experiment from January 1 to December 31 in year [YYYY] from 2005 to 2014.</li> <li>[Param]_March_[YYYY].nc: The 2-hourly data obtained in the sensitivity experiment from Mar 1 to 21 in every year [YYYY] from 2005 to 2014. [Param] is one of the following: <ul> <li>O3_Control: Ozone mixing ratio in the control experiment</li> <li>O3_IndianSubcontinent: Ozone mixing ratio in the Indian Subcontinent experiment</li> <li>O3_NorthernIndochina: Ozone mixing ratio in the northern Indochina experiment</li> <li>O3_SouthernChina: Ozone mixing ratio in the southern China experiment</li> <li>CO: Carbon monoxide</li> <li>T: Temperature</li> <li>U: Zonal wind</li> </ul> </li> <li>CO_Emission.nc and NOx_Emission.nc: The monthly mean CO and NOx emissions from the surface used in the model experiments.</li> </ul> <p><strong>Contact</strong></p> <p>Shin-Ya Ogino<br> Japan Agency for Marine-Earth Science and Technology (JAMSTEC)<br> E-mail: ogino-sy@jamstec.go.jp</p>
Assessing tropospheric turbulence impact on VGOS telescope placement in the Indian subcontinent for the estimation of Earth Orientation Parameters
<p>The dataset accompanying this study is composed of three distinct files, each integral to the research conducted. The file, named 'Simulated Data', includes the results derived from the simulations performed within this investigation. This file serves as a repository of the computed outcomes. The file, named 'Data and Code', includes the MATLAB scripts utilized to compute the Cn value from the Zenith Wet Delay (ZWD). Additionally, this file encompasses the relevant datasets for both wind speed and ZWD at various station locations. The third file contains the geographical coordinates of the Indian stations that were used in the study. Together, these files constitute a complete dataset that supports the study’s objectives and verification of the findings. </p>
Data for: Klein et al., Viscosity of aqueous ammonium nitrate--organic particles: Equilibrium partitioning may be a reasonable assumption for most tropospheric conditions, egusphere-2024-1459
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental and modelled data to the figures shown in the main manuscript and Appendix.</p> <p>Figure 3B AIOMFAC-VISC (AIOMFAC-VISC modelling of sucrose)</p> <p>Figure 3B Experimental (Viscosity measurements of sucrose)</p> <p>Figure 4 (Viscosity measurements of ammonium nitrate - sucrose - water mixtures)</p> <p>Figure 5 A and C (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using mixing rules)</p> <p>Figure 5 B and D (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using AIOMFAC-VISC)</p> <p>Figure 6 (Viscosity estimations of inorganic - sucrose - water mixtures using mixing rules)</p> <p>Figure 7 (Mixing times for ammonium nitrate - sucrose - water and Toluene SOA - sucrose - water aerosol particles for varies cities) </p> <p>Figure A2 (Viscosity estimations of ammonium nitrate - sucrose - water mixtures using a mass fraction based mixing rules)</p>
Sentinel-5P Tropospheric Nitrogen Dioxide Density at 2 km from 2018-05 to 2022-11 Monthly Aggregation
<p>Layers include: Tropospheric Nitrogen Dioxide Density monthly median value May 2018 – November 2022. Derived using the <a href="https://eumap.readthedocs.io/en/latest/index.html#">eumap package in Python</a>. We derived three standard statistics: (1) 10th percentile (p10), median (m), and 90th percentile (p10).</p> <p>Band info</p> <table> <tbody> <tr> <td>Name</td> <td>Units</td> <td>Scale</td> <td> <p>Description</p> </td> </tr> <tr> <td>NO<sub>2</sub></td> <td>(µmol m<sup>-</sup><sup>2</sup>)</td> <td>0.1</td> <td>tropospheric nitrogen dioxide density</td> </tr> </tbody> </table> <p>Warning:</p> <p>Original data have the different range of latitude among months. In December, there are no data above N 58°, where is approximately between Iceland and Scotland. Therefore, when it comes to monthly aggregation, there is a strip across N 58° as an artifact. It is not suggested to use this dataset in the region above N 58°.</p> <p>For more info about the s5p NO<sub>2</sub> product see: <a href="https://maps.s5p-pal.com"><strong>https://maps.s5p-pal.com/</strong></a>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention:</p> <ul> <li>no2 = variable: nitrogen dioxide (µmol m<sup>-</sup><sup>2</sup>),</li> <li>s5p.l3.trop.tmwm= determination method: Copernicus Sentinel-5P product, level 3, tropospheric, temporal moving window median</li> <li>p10/p50/p90 = aggregation/statistics method: 10th/50th/90th percentile,</li> <li>2km = spatial resolution / block support: 2 km,</li> <li>a = vertical reference: above ground,</li> <li>start date_end date (i.e. 20180501_20180531) = time reference: from start date to end date</li> <li>go = bounding box: global land without Antarctica</li> <li>epsg.4326 = ESPG code: epsg.4326</li> <li>v20221219 = version code: creation date 20221219</li> </ul>
Data for Figures in "Unexpected Long-Term Variability in Jupiter's Tropospheric Temperatures
<p>This repository provides the data submitted by the primary author that were used to create Figures 1, 2, 3, 4 and 5 in the text for the article: <strong>"Unexpected Long-Term Variability in Jupiter's Tropospheric Temperatures", by </strong> Glenn S. Orton, Arrate Antuñano, Leigh N. Fletcher, James Sinclair, Thomas Momary, Takuya Fujoyoshi, Padma Yanamandra-Fisher, Padraig T. Donnelly, Jennifer Greco, Anna Payne, Kimberly Boydstun, Laura Wakefield.</p> <p>The repository consists of five files in text (ASCII) format: (1) User Guide to Data (16 kb), (2) figure1_data.txt (1.5 MB), (3) figure2_data.txt (142 kb), figure4_data.txt (61 kb), and figure5_data.txt (59 kb). Data for Figure 3 are provided in the User Guide.</p>
Hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign
<p>We provide here the supporting dataset for our study on airborne measurements of oh hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign in 2018.</p> <p> </p>
Observations of trace gases in the lowermost stratosphere and upper troposphere from the SPURT aircraft measurement program
<p><strong>Introduction</strong></p> <p>SPURT (Spurenstofftransport in der Tropopausenregion, trace gas transport in the tropopause region) was an aircraft measurement program funded by the AFO 2000 programme of the German Ministry for Education and Research (BMBF). Eight campaigns (36 flights in total) were conducted between November 2001 and July 2003 to investigate trace gas transport in the extratropical upper troposphere and lowermost stratosphere in all seasons. A wide range of trace gases with different lifetimes and sink/source characteristics were measured in-situ from a Learjet 35A aircraft flying at altitudes up to 13.7 km. The data set is well suited for studies of atmospheric transport, for model validation, and for investigations of seasonal changes in the upper troposphere and lowermost stratosphere as demonstrated in numerous accompanying studies.</p> <p><strong>Dataset content</strong></p> <ol> <li>In-situ measurements of N<sub>2</sub>O, CH<sub>4</sub>, CO, CO<sub>2</sub>, CFC12, H<sub>2</sub>, SF<sub>6</sub>, NO, NO<sub>y</sub>, O<sub>3</sub> and H<sub>2</sub>O along all flight tracks.</li> <li>Position and meteorological quantities recorded by the aircraft along all flight tracks.</li> <li>Meteorological data from ECMWF analysis fields and derived products such as potential vorticity and equivalent latitude interpolated to all flight tracks.</li> <li>Merge files (extension .mrg) of all observations, aircraft positions and other data merged into one single file per flight at 5 sec temporal resolution. Due to different instrument response times or computer clocks, the individual measurements were typically shifted by several seconds relative to each other. These time shifts are corrected for in the merge files.</li> <li>Ten day backward trajectories started every 12 minutes along the flight tracks computed with <em>Lagranto</em> (<a href="https://dx.doi.org/10.5194/gmd-8-2569-2015">doi:10.5194/gmd-8-2569-2015</a>) based on 3-hourly ECMWF IFS analysis/forecast fields.</li> <li>Further information such as flight quicklooks, flight protocols, meteorological reports, etc.</li> </ol> <p>All measurement data are provided in NASA/Ames format (<a href="https://espo.nasa.gov/content/Ames_Format_Specification_v20">https://espo.nasa.gov/content/Ames_Format_Specification_v20</a>), which is a self-explaining ASCII format that can conveniently be read by many software packages, e.g. the nappy library for python.</p> <p><strong>Quick start guide</strong></p> <ol> <li>Download and unpack the gzip compressed tar file (unpacking generates the two directories <em>images </em>and <em>data</em>)</li> <li>Change to the directory <em>data </em>and open the file index.html with a web browser. This will open a web page providing an overview of the eight campaigns and associated data.</li> <li>For most purposes it will be sufficient to work with the merge files: Change to the <em>data</em> directory and list all merge files by typing "ls */*/*.mrg" (Linux) or "dir *\*\*.mrg" (Windows).</li> </ol> <p> </p> <p><strong>References:</strong></p> <p>The reference journal article for the SPURT project is</p> <p><em>Engel, A., Bönisch, H., Brunner, D., Fischer, H., Franke, H., Günther, G., Gurk, C., Hegglin, M., Hoor, P., Königstedt, R., Krebsbach, M., Maser, R., Parchatka, U., Peter, T., Schell, D., Schiller, C., Schmidt, U., Spelten, N., Szabo, T., Weers, U., Wernli, H., Wetter, T., and Wirth, V.: Highly resolved observations of trace gases in the lowermost stratosphere and upper troposphere from the Spurt project: an overview, Atmos. Chem. Phys., 6, 283–301, https://doi.org/10.5194/acp-6-283-2006, 2006. </em></p> <p>Many more scientific publications emerged from the project (see reference list and object identifiers).</p>
Lifetimes and timescales of tropospheric ozone: Ozone emission experiments
<p>The lifetime of tropospheric O<sub>3</sub> is difficult to quantify because we model O<sub>3</sub> as a secondary pollutant, without direct emissions. For other reactive greenhouse gases like CH<sub>4</sub> and N<sub>2</sub>O, we readily model lifetimes and timescales that include chemical feedbacks based on direct emissions. Here, we devise a set of artificial experiments with a chemistry-transport model where O<sub>3</sub> is directly emitted into the atmosphere at a quantified rate. We create three primary emission patterns for O<sub>3</sub>, mimicking secondary production by surface industrial pollution, that by aviation, and primary injection through stratosphere-troposphere exchange (STE). The perturbation lifetimes for these O<sub>3</sub> sources includes chemical feedbacks and varies from 6 to 27 days depending on source location and season. Previous studies derived lifetimes around 24 days estimated from the mean odd-oxygen loss frequency. The timescales for decay of excess O<sub>3</sub> varies from 10–20 days in NH summer to 30–40 days in NH winter. For each season, we identify a single O<sub>3</sub> chemical mode applying to all experiments. Understanding how O<sub>3</sub> sources accumulate (the lifetime) and disperse (decay timescale) provides some insight into how changes in pollution emissions, climate, and stratospheric O<sub>3</sub> depletion over this century will alter tropospheric O<sub>3</sub>. This work incidentally found two distinct mistakes in how we diagnose tropospheric O<sub>3</sub>, but not how we model it. First, the chemical pattern of an O<sub>3</sub> perturbation or decay mode does not resemble our traditional view of the odd-oxygen family of species that includes NO<sub>2</sub>. Instead, a positive O<sub>3</sub> perturbation is accompanied by a decrease in NO<sub>2</sub>. Second, heretofore we diagnosed the importance of STE flux to tropospheric O<sub>3</sub> with a synthetic 'tagged' tracer O3S, which had full stratospheric chemistry and linear tropospheric loss based on odd-oxygen loss rates. These O3S studies predicted that about 40 % of tropospheric O<sub>3</sub> was of stratospheric origin, but our lifetime and decay experiments show clearly that STE fluxes add about 8 % to tropospheric O<sub>3</sub>, providing further evidence that tagged tracers do not work when the tracer is a major species with chemical feedbacks on its loss rates, as shown for CH<sub>4</sub>. </p>
Evaluation of Upper Tropospheric Geopotential Height Anomalies over the Tropical and Subtropical Oceans in CMIP6 Models Using GNSS Radio Occultation Observations
<p>The set-up of CESM2-CAM6 sensitivity experiments for winter season (Dec-Jan-Feb: DJF), with prognostic falling ice radiative effects on (SON) and off (NOS), is an updated two-moment stratiform cloud scheme (MG2, Gettelman & Morrison, 2015) in the CESM2 atmospheric component of CAM6. CESM2-CAM6 participated in CMIP6. Both the NOS and SON simulations were configured following the same approach as the CMIP6 "historical" run spanning from 1980 to 2014.</p> <p> </p> <p>The data are:</p> <p> </p> <p>TS: skin temperature (K)</p> <p>TAUX: zonal surface wind stress</p> <p>TAUY: meridinal surface wind stress</p> <p>DTCOND: moist condensation heating rate</p> <p>QRL: long wave heating rate</p> <p>OMEGA: vertical motion</p> <p>Z3: geopotential height</p> <p> </p>
Synergistic HNO3–H2SO4–NH3 upper tropospheric particle formation: data resources & code
<p>Data presented in the manuscript "Synergistic HNO3–H2SO4–NH3 upper tropospheric particle formation" currently in review.</p> <p>The manuscript associated with this data was written using results from the CLOUD experiment at CERN, and the author list is a subset of the CLOUD collaboration.</p>
Dataset for "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"
<p><strong>Data and scripts used to create the figures in the manuscript titled: "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"</strong></p>
Clumped-isotope constraint on upper-tropospheric cooling during the Last Glacial Maximum
<p>Ice cores and other paleotemperature proxies, together with general circulation models, have provided information on past surface temperatures and the atmosphere's composition in different climates. Little is known, however, about past temperatures at high altitudes, which play a crucial role in Earth's radiative energy budget. Paleoclimate records at high-altitude sites are sparse, and the few that are available show poor agreement with climate model predictions. These disagreements could be due to insufficient spatial coverage, spatiotemporal biases, or model physics; new records that can mitigate or avoid these uncertainties are needed. Here, we constrain the change in upper-tropospheric temperature at the global scale during the Last Glacial Maximum (LGM) using the clumped-isotope composition of molecular oxygen trapped in polar ice cores. Aided by global three-dimensional chemical transport modeling, we exploit the intrinsic temperature sensitivity of the clumped-isotope composition of atmospheric oxygen to infer that the upper troposphere (effective mean altitude 10 – 11 km) was 6-9ºC cooler during the LGM than during the late preindustrial Holocene. A complementary energy balance approach supports a minor or negligible steepening of atmospheric lapse rates during the LGM, which is consistent with a range of climate model simulations. Proxy-model disagreements with other high-altitude records may stem from inaccuracies in regional hydroclimate simulation, possibly related to land-atmosphere feedbacks.</p>
CEDAR Project: A Whole-Atmospheric Perspective on Connections between Intra-Seasonal Variations in the Troposphere and Thermosphere
<p>This collaborative award is aimed at studying the relationship between the variability of thermospheric winds to the variability caused by wave structures generated in the tropical troposphere. This coupling is driven by wave excitation by deep convection in the tropical troposphere that can propagate vertically into the thermosphere. Tropospheric convection associated with the Madden‐Julian Oscillation (MJO), the dominant mode of intra-seasonal variability in tropical convection and circulation, is known to modulate the intensity of upward‐propagating gravity and Kelvin waves. Previous work demonstrated that a 90-day oscillation in tropospheric convection during 2009-2010 was imprinted on both thermospheric mean winds and the eastward propagating wavenumber 3 diurnal (DE3) tidal amplitudes. This modulation was observed by the GOCE and CHAMP satellites and modeled with the TIME-GCM. The research effort would broaden participation by involving and training two undergraduate student interns through the University of Colorado BOLD internship program that focuses on promoting the recruitment, retention, and development of traditionally underrepresented engineering students.<br> <br> The new research will follow up on the results obtained in recent studies that demonstrated that strong coupling between the troposphere and the thermosphere occurs on intra-seasonal timescales. The award will address the following questions:<br> Q1: How frequent, prevalent, and persistent are correlations between 30 to 100-day variations in the three regions of troposphere, mesosphere, and thermosphere, during the past two decades?<br> Q2: What plausible roles do large-scale upward propagating waves play in dynamically coupling tropical tropospheric intra-seasonal variability into the thermosphere?<br> Q3: Is there any observational evidence suggesting a connection between this troposphere-thermosphere intra-seasonal coupling and MJO, Quasi-Biennial Oscillation (QBO) and El Niño-Southern Oscillation (ENSO)?<br> The combination of available upper atmosphere satellite data with ground-, and model-based datasets would be studied to provide insight into whether the intra-seasonal variations in the waves are caused by variability in the tropospheric sources or by wave-mean flow interactions. In the case of the latter, the study would determine at which heights these interactions are occurring. This study will determine the contribution of global-scale wave coupling between the troposphere and the thermosphere, thus addressing outstanding issues of fundamental importance to the CEDAR community.</p> <p>This research primarily involves performing correlation analyses and extracting wave information from satellite (CHAMP, GOCE, Swarm-C, TIMED, OLR), ground (Kauai, Christmas Island, and Adelaide, Maui, Urbana, and Chile), and model (MERRA-2, TIE-GCM, and WACCM-X) -based datasets and processing, plotting, data produced in standard ways to draw scientific conclusions. </p> <p>This project does not generate any new physical or observational data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles are followed by making data resources (e.g. code/software and metadata) resulting from this project publicly available.</p> <p>GOCE, CHAMP, Swarm-C data (V01) are available at ftp://anonymous@thermosphere.tudelft.nl/. SABER data (V2.0, L2B) are available at http://saber.gats-inc.com/data.php. Tl DI data (V3.7) are available at http:// timed.hao.ucar.edu/tidi/. OLR data are available at https://psl.noaa.gov/data/gridded/ data.interp_OLR.html. F10.7 data are available at http://www.swpc.noaa.gov/content/data-access. kp/ap data are available at ftp:// ftp.gfz-potsdam.de/pub/home/obs/ kp-ap/.</p>
The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere (dataset)
<p>Dataset accompanying the journal article titled "The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere". Preprint: doi.org/10.5194/egusphere-2022-33</p>
Data set for "The ion-ion recombination coefficient α: comparison of temperature- and pressure-dependent parameterisations for the troposphere and stratosphere"
<p>The uploaded data are related to the publication "The ion–ion recombination coefficient <span class="math-tex">\(\alpha\)</span>: comparison of temperature- and pressure-dependent parameterisations for the troposphere and stratosphere" in Atmospheric Chemistry and Physics (ACP). The data are the same as shown in the figures of the publication. The naming of the uploaded files indicates the figure (e.g., "Fig_2" indicates Figure 2 of the publication). </p>
Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming
<p>This dataset contains simulated and observed maps of surface temperature change over the satellite-era and domain averaged trends of tropospheric warming. The data accompanies software that was used to disentangle the forced and unforced components of tropical tropospheric temperature change. This work was documented in:</p> <blockquote> <p>Po-Chedley, S., J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, B.D. Santer (2022): "Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming," Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.2209431.</p> </blockquote> <p>The software is available at: https://github.com/LLNL/MDAS</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
<p>This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.</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
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.