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111 results for “Satellite Observations”
Satellite-observed surface flow speed within Russell sector, West Greenland, bi-weekly average of 2015-2019
<p>An average horizontal surface ice velocity of Russell sector (Greenland) with 2-week temporal and 150m spatial resolution. Derived from satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in https://doi.org/10.5194/tc-2021-170.</p> <p><br> Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with maps of vx and vy velocity components, maps of associated uncertainties per velocity component (STD of the 2-weeks averaged raw satellite measurements), and map of number of averaged measurements.</p>
Seasonal evolution of basal conditions within Russell sector, West Greenland, inverted from satellite observations of surface flow
<p>An annual set of model-inferred basal and surface properties of ice flow at Russell Gletcher sector in Western Greenland with half-month temporal resolution. Derived using the Elmer/Ice ice-flow model by inversion of satellite-observed ice surface velocity (10.5281/zenodo.5535532). The details on the data creatoin can be found in 10.5194/tc-15-5675-2021 .</p> <p>Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with:<br> * alpha - inverted be model basal friction coefficient in log10 (log10(MPa m-1 a)<br> * base - basal topography altitude (m)<br> * lithk - ice thickness (m)<br> * orog - surface altitude (m)<br> * strbasemag - magnitude of basal friction tb (MPa)<br> * xvelbase, yvelbase, zvelbase - 3D basal velocity (m/yr)<br> * xvelmean, yvelmean - vertically average mean horizontal velocity (m/yr)<br> * xvelsurf, yvelsurf, zvelsurf - 3D surface velocity (m/yr)<br> * n - effective pressure (MPa)</p> <p>The additional WinterMeanState NetCDF file (inversion from the mean velocity of january, Febriary, Mars) contains the same set of variables (except the effective pressure), and in addition contains the <em>As</em> Weertman sliding coeffitient.</p> <p>The results have been interpolated from the native unstructured model grid to the regular grid used for the observed velocity (10.5281/zenodo.5535624).</p>
PEATCLSM(Tb): A land surface data assimilation product for peatlands using PEATCLSM and brightness temperature (Tb) satellite observations (Northern Hemisphere output)
<p>The datasets archived here include simulation results shown in the paper, “Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework”, published in Remote Sensing of Environment Journal (Bechtold et al., 2020). The output was produced by combining peatland-specific land surface modeling (Bechtold et al., 2019b) embedded in the NASA Catchment Land Surface Model (CLSM) with L-band brightness temperature (Tb) observations (SMOS), applying the data assimilation framework of the SMAP Level‐4 Soil Moisture product (Reichle et al., 2019). We provide netcdf files (9-km resolution EASEv2 grid, period Jan 2010 – Nov 2019, and between 45°N and 70°N, NE Asia excluded) of the four experiments of the manuscript: model-only (open-loop, OL) and data assimilation (DA) for each land model version, that is CLSM without and with the use of the PEATCLSM modules. The highest accuracy is provided by the DA product using PEATCLSM and Tb observations. When referring to the latter product use the name ‘PEATCLSM(Tb)’. We provide three types of netcdf files:<br> • daily_images_*.nc: Daily land states and fluxes (Table 1), provided as netCDF image-chunked image stack<br> • ObsFcstAna_images_*.nc: Brightness temperature observations, forecasts and analysis (Table 2), provided as netCDF image-chunked image stack<br> • incr_timeseries_*.nc: Data assimilation increments (Table 3), provided as netCDF timeseries-chunked image stack</p> <p>The file content is described in the file PEATCLSM_Tb_Documentation_20200505.pdf</p> <p>Please contact Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.</p> <p>Data usage statement:<br> This work is licensed under a Creative Commons Attribution 4.0 International License: https://creativecommons.org/licenses/by/4.0/<br> If you decide to work with this data, we kindly ask to be informed at the outset of the nature of this work. If the data are essential to the work, or if an important result or conclusion depends on the PEATCLSM(Tb) data product, we would appreciate that you discuss these findings with us to ensure correct use and interpretation of the PEATCLSM(Tb) product. Furthermore, we are continuously improving the data assimilation product, a discussion of your work at an early stage may (i) help us to improve our product, and (ii) allow us to provide you with a newer version. Thanks!</p> <p>References:</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., & Koster, R. D. (2019a). PEAT-CLSM simulation output (Northern Peatlands) version 1. https://doi.org/10.17605/OSF.IO/E58YM</p> <p>Bechtold, M. et al. (2019b). PEAT‐CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(7), 2130–2162. https://doi.org/10.1029/2018MS001574</p> <p>Bechtold, M., De Lannoy, G. J. M., Reichle, R. H., Roose, D., Balliston, N., Burdun, I., Devito, K., Kurbatova, J., Strack, M., & Zarov, E. A. (2020). Improved Groundwater Table and L-band Brightness Temperature Estimates for Northern Hemisphere Peatlands Using New Model Physics and SMOS Observations in a Global Data Assimilation Framework. <em>Remote Sensing of Environment</em>. https://doi.org/10.1016/j.rse.2020.111805</p> <p>Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J. M., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., & Walker, J. P. (2019). Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product. <em>Journal of Advances in Modeling Earth Systems</em>, <em>11</em>(10), 3106–3130. https://doi.org/10.1029/2019MS001729</p>
Leaf moisture content (live-fuel moisture content) at global scale from passive microwave satellite observations of vegetation optical depth (VOD2LFMC)
<p><strong>Related paper:</strong> <a href="https://hess.copernicus.org/preprints/hess-2022-121/">Forkel et al. (2022)</a></p> <p>The VOD2LFMC dataset contains estimates of leaf moisture content as defined as live-fuel moisture content (LFMC) derived from passive microwave satellite observation of vegetation optical depth (VOD). LFMC is defined as the fresh mass of a leaf over the dry mass and is expressed in %:</p> <p><span class="math-tex">\(LFMC = {m_{fresh}-m_{dry}\over m_{dry}}*100\%\)</span></p> <p>LFMC was estimated from the <a href="https://doi.org/10.5281/zenodo.2575599">VODCA version 1</a> dataset of Ku-band VOD using the model approach “B” as described in Forkel et al. (2022).</p> <p>The file VOD2LFMC-B_v01_2000-2017.zip contains (unzipped ~ 57 GB):</p> <ul> <li>daily global data per month netCDF files</li> <li>a README file</li> <li>Ancillary file VOD2LFMC-B_v01_support-by-obs.nc</li> </ul> <p>Grid, time and variable definitions:</p> <ul> <li> <p>Grid-name: Geographic Lat/Lon</p> </li> <li> <p>Pixel-size: 1/4 degrees</p> </li> <li> <p>Size-x: 1440</p> </li> <li> <p>Size-y: 557</p> </li> <li> <p>Time period: February 2000 – July 2017</p> </li> <li> <p>Temporal resolution: daily</p> </li> <li> <p>Variable: Live-fuel moisture content (LFMC) in %</p> </li> <li> <p>Valid-range: 0-400%</p> </li> </ul> <p> </p>
Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning
<p>Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO<sub>2 </sub>by machine learning</p> <p>This dataset is uploaded as a part of the article by Kim et al. (2021). The dataset is the hourly maps of near-surface nitrogen dioxide (NO<sub>2</sub>) concentrations at 100 m resolution for an Alpine domain (Switzerland and northern Italy, 6-12 °E, 42-48 °N). The dataset is provided per day (24 hours) in a netcdf (*.nc ~550MB). In this work, we have generated NO<sub>2 </sub>hourly maps for Feb. 2019 to May 2020 and, here, we upload for March 2019 only (~16 GB). If you need data for another period of time, please contact Gerrit Kuhlmann (gerrit.kuhlmann@empa.ch) or Minsu Kim (minsu.kim@empa.ch). </p>
Plaskett 1.8 metre Observations of Starlink Satellites: Supplemental Information
<p>Release of GitHub repo in conjunction with the publication of "Plaskett 1.8 metre Observations of Starlink Satellites" in The Astronomical Journal, also available at arXiv: 2109.12494. The related paper presents observations of 23 Starlink satellites in the g' bandpass, obtained from the Dominion Astrophysical Observatory's Plaskett 1.8 metre telescope.</p>
Atmospheric Distribution of HCN from Satellite Observations and 3-D Model Simulations - TOMCAT data
<p>This repository contains the model data from the paper "Atmospheric Distribution of HCN from Satellite<br> Observations and 3-D Model Simulations" submitted to ACP.</p> <p>The files contains the monthly mean hydrogen cyanide (HCN) mixing ratios modelled using the TOMCAT 3-D offline chemical transport model with a horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km.</p>
The SPARC water vapour assessment II: Comparison of annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour observed from satellites
<p>Here we provide a NetCDF data set that contains the amplitudes and phases for the annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour as observed by 30 satellite data sets. In addition, we combine the results from all data sets to provide average amplitudes and the corresponding standard deviations, among other.</p> <p>The content description of the NetCDF file looks as follows:</p> <p>netcdf results.amt-10-1111-2017 {<br> dimensions:<br> dataset = 30 ;<br> string_length = 60 ;<br> latitude = 37 ;<br> bands = 2 ;<br> altitude = 59 ;<br> variables:<br> char dataset_short(string_length, dataset) ;<br> dataset_short:standard_name = "data set" ;<br> dataset_short:long_name = "data set name" ;<br> dataset_short:description = "short label of data set" ;<br> char dataset_long(string_length, dataset) ;<br> dataset_long:standard_name = "data set" ;<br> dataset_long:long_name = "data set name" ;<br> dataset_long:description = "long label of data set" ;<br> double latitude(latitude) ;<br> latitude:standard_name = "latitude" ;<br> latitude:units = "degree_north" ;<br> latitude:minimum_value = "-90" ;<br> latitude:maximum_value = "90" ;<br> latitude:axis = "Y" ;<br> latitude:_CoordinateAxisType = "Lat" ;<br> double latitude_bands(bands, latitude) ;<br> latitude_bands:units = "degree_north" ;<br> double altitude(altitude) ;<br> altitude:standard_name = "altitude" ;<br> altitude:long_name = "pressure levels" ;<br> altitude:units = "hPa" ;<br> altitude:axis = "Z" ;<br> altitude:_CoordinateAxisType = "Alt" ;<br> double tropopause(latitude) ;<br> tropopause:standard_name = "tropopause" ;<br> tropopause:long_name = "tropopause pressure" ;<br> tropopause:description = "climatological tropopause pressure based on MERRA reanalysis data 2000 - 2014" ;<br> tropopause:units = "hPa" ;</p> <p>// global attributes:<br> :summary = "this file contains the results published in Lossow et al. (2017)" ;<br> :url = "https://www.atmos-meas-tech.net/10/1111/2017/amt-10-1111-2017.html" ;<br> :project = "second SPARC water vapour assessment (WAVAS-II)" ;<br> :creator_name = "Stefan Lossow & Farahnaz Khosrawi" ;<br> :creator_email = "stefan.lossow@kit.edu & farahnaz.khosrawi@kit.edu" ;<br> :creator_email_supplemental = "stefan.lossow@yahoo.se & f.khosrawi@gmail.com" ;<br> :value_for_nodata = "NaN" ;<br> :date_created = "20190105T112425Z" ;</p> <p>group: AO {<br> dimensions:<br> latitude = 37 ;<br> altitude = 59 ;<br> dataset = 30 ;<br> variables:<br> double amplitude(dataset, altitude, latitude) ;<br> amplitude:standard_name = "amplitude" ;<br> amplitude:long_name = "amplitude of the AO variation" ;<br> amplitude:description = "regression model is given by Eq. (1) in the manuscript; amplitude calculation based on Eq. (2)" ;<br> amplitude:units = "ppmv" ;<br> double phase(dataset, altitude, latitude) ;<br> phase:standard_name = "phase" ;<br> phase:long_name = "phase of the AO variation" ;<br> phase:description = "regression model is given by Eq. (1) in the manuscript; phase calculation based on Eq. (3)" ;<br> phase:units = "month" ;<br> double offset(dataset, altitude, latitude) ;<br> offset:standard_name = "offset" ;<br> offset:long_name = "offset component of the regression model" ;<br> offset:description = "regression model is given by Eq. (1) in the manuscript; meant for calculation of relative amplitudes" ;<br> offset:units = "ppmv" ;<br> double screening(dataset, altitude, latitude) ;<br> screening:standard_name = "screening" ;<br> screening:long_name = "screening for the amplitude and phase data" ;<br> screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;<br> screening:units = "" ;<br> double phase_difference(dataset, altitude, latitude) ;<br> phase_difference:standard_name = "phase difference" ;<br> phase_difference:long_name = "phase difference with respect to the reference data set" ;<br> phase_difference:reference_data_set_short = "MLS" ;<br> phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;<br> phase_difference:description = "phase difference has been adapted so that it not exceeds the [-6,6] months interval by adding +/- 12 months; has been calculated after the screening" ;<br> phase_difference:units = "month" ;<br> double amplitude_standard_deviation(altitude, latitude) ;<br> amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;<br> amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;<br> amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;<br> amplitude_standard_deviation:units = "ppmv" ;<br> double amplitude_mean(altitude, latitude) ;<br> amplitude_mean:standard_name = "mean amplitude" ;<br> amplitude_mean:long_name = "mean amplitude over all data sets" ;<br> amplitude_mean:description = "mean calculation based on Eq. (6)" ;<br> amplitude_mean:units = "ppmv" ;<br> double amplitude_relative_standard_deviation(altitude, latitude) ;<br> amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;<br> amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;<br> amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;<br> amplitude_relative_standard_deviation:units = "ppmv" ;<br> double phase_difference_standard_deviation(altitude, latitude) ;<br> phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;<br> phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;<br> phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;<br> phase_difference_standard_deviation:units = "month" ;<br> double phase_difference_mean(altitude, latitude) ;<br> phase_difference_mean:standard_name = "mean of phase difference" ;<br> phase_difference_mean:long_name = "mean of phase difference over all data sets" ;<br> phase_difference_mean:description = "mean calculation based on Eq. (7)" ;<br> phase_difference_mean:units = "month" ;<br> } // group AO</p> <p>group: SAO {<br> dimensions:<br> latitude = 37 ;<br> altitude = 59 ;<br> dataset = 30 ;<br> variables:<br> double amplitude(dataset, altitude, latitude) ;<br> amplitude:standard_name = "amplitude" ;<br> amplitude:long_name = "amplitude of the SAO variation" ;<br> amplitude:description = "regression model is given by Eq. (4) in the manuscript; amplitude calculation based on Eq. (2)" ;<br> amplitude:units = "ppmv" ;<br> double phase(dataset, altitude, latitude) ;<br> phase:standard_name = "phase" ;<br> phase:long_name = "phase of the SAO variation" ;<br> phase:description = "regression model is given by Eq. (4) in the manuscript; phase calculation based on Eq. (3)" ;<br> phase:units = "month" ;<br> double offset(dataset, altitude, latitude) ;<br> offset:standard_name = "offset" ;<br> offset:long_name = "offset component of the regression model" ;<br> offset:description = "regression model is given by Eq. (4) in the manuscript; meant for calculation of relative amplitudes" ;<br> offset:units = "ppmv" ;<br> double screening(dataset, altitude, latitude) ;<br> screening:standard_name = "screening" ;<br> screening:long_name = "screening for the amplitude and phase data" ;<br> screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;<br> screening:units = "" ;<br> double phase_difference(dataset, altitude, latitude) ;<br> phase_difference:standard_name = "phase difference" ;<br> phase_difference:long_name = "phase difference with respect to the reference data set" ;<br> phase_difference:reference_data_set_short = "MLS" ;<br> phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;<br> phase_difference:description = "phase difference has been adapted so that it not exceeds the [-3,3] months interval by adding +/- 6 months; has been calculated after the screening" ;<br> phase_difference:units = "month" ;<br> double amplitude_standard_deviation(altitude, latitude) ;<br> amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;<br> amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;<br> amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;<br> amplitude_standard_deviation:units = "ppmv" ;<br> double amplitude_mean(altitude, latitude) ;<br> amplitude_mean:standard_name = "mean amplitude" ;<br> amplitude_mean:long_name = "mean amplitude over all data sets" ;<br> amplitude_mean:description = "mean calculation based on Eq. (6)" ;<br> amplitude_mean:units = "ppmv" ;<br> double amplitude_relative_standard_deviation(altitude, latitude) ;<br> amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;<br> amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;<br> amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;<br> amplitude_relative_standard_deviation:units = "ppmv" ;<br> double phase_difference_standard_deviation(altitude, latitude) ;<br> phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;<br> phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;<br> phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;<br> phase_difference_standard_deviation:units = "month" ;<br> double phase_difference_mean(altitude, latitude) ;<br> phase_difference_mean:standard_name = "mean of phase difference" ;<br> phase_difference_mean:long_name = "mean of phase difference over all data sets" ;<br> phase_difference_mean:description = "mean calculation based on Eq. (7)" ;<br> phase_difference_mean:units = "month" ;<br> } // group SAO</p> <p>group: QBO {<br> dimensions:<br> latitude = 37 ;<br> altitude = 59 ;<br> dataset = 30 ;<br> variables:<br> double amplitude(dataset, altitude, latitude) ;<br> amplitude:standard_name = "amplitude" ;<br> amplitude:long_name = "amplitude of the QBO variation" ;<br> amplitude:description = "regression model is given by Eq. (5) in the manuscript; amplitude calculation based on Eq. (2)" ;<br> amplitude:units = "ppmv" ;<br> double phase(dataset, altitude, latitude) ;<br> phase:standard_name = "phase" ;<br> phase:long_name = "phase of the QBO variation" ;<br> phase:description = "regression model is given by Eq. (5) in the manuscript; phase is derived as the shift of the QBO regression fit for which the correlation with the Singapore (1N, 104E) winds at 50 hPa maximises" ;<br> phase:units = "month" ;<br> double offset(dataset, altitude, latitude) ;<br> offset:standard_name = "offset" ;<br> offset:long_name = "offset component of the regression model" ;<br> offset:description = "regression model is given by Eq. (5) in the manuscript; meant for calculation of relative amplitudes" ;<br> offset:units = "ppmv" ;<br> double screening(dataset, altitude, latitude) ;<br> screening:standard_name = "screening" ;<br> screening:long_name = "screening for the amplitude and phase data" ;<br> screening:description = "screening matrix for the amplitude and phase data to calculate the standard deviations described in Sect. 3.3; 1 means screening; 0 means no screening" ;<br> screening:units = "" ;<br> double phase_difference(dataset, altitude, latitude) ;<br> phase_difference:standard_name = "phase difference" ;<br> phase_difference:long_name = "phase difference with respect to the reference data set" ;<br> phase_difference:reference_data_set_short = "MLS" ;<br> phase_difference:reference_data_set_long = "Aura/MLS v4.2" ;<br> phase_difference:description = "phase difference has been adapted so that it not exceeds the [-14,14] months interval by adding +/- 28 months; has been calculated after the screening" ;<br> phase_difference:units = "month" ;<br> double amplitude_standard_deviation(altitude, latitude) ;<br> amplitude_standard_deviation:standard_name = "standard deviation of amplitude" ;<br> amplitude_standard_deviation:long_name = "standard deviation of amplitude over all data sets" ;<br> amplitude_standard_deviation:description = "standard deviation calculation based on Eq. (6)" ;<br> amplitude_standard_deviation:units = "ppmv" ;<br> double amplitude_mean(altitude, latitude) ;<br> amplitude_mean:standard_name = "mean amplitude" ;<br> amplitude_mean:long_name = "mean amplitude over all data sets" ;<br> amplitude_mean:description = "mean calculation based on Eq. (6)" ;<br> amplitude_mean:units = "ppmv" ;<br> double amplitude_relative_standard_deviation(altitude, latitude) ;<br> amplitude_relative_standard_deviation:standard_name = "relative standard deviation of amplitude" ;<br> amplitude_relative_standard_deviation:long_name = "relatuve standard deviation of amplitude " ;<br> amplitude_relative_standard_deviation:description = "relavtive standard deviation calculation based on Eq. (6); uses \"amplitude_mean\" as reference" ;<br> amplitude_relative_standard_deviation:units = "ppmv" ;<br> double phase_difference_standard_deviation(altitude, latitude) ;<br> phase_difference_standard_deviation:standard_name = "standard deviation of phase difference" ;<br> phase_difference_standard_deviation:long_name = "standard deviation of phase difference over all data sets" ;<br> phase_difference_standard_deviation:description = "standard deviation calculation based on Eq. (7)" ;<br> phase_difference_standard_deviation:units = "month" ;<br> double phase_difference_mean(altitude, latitude) ;<br> phase_difference_mean:standard_name = "mean of phase difference" ;<br> phase_difference_mean:long_name = "mean of phase difference over all data sets" ;<br> phase_difference_mean:description = "mean calculation based on Eq. (7)" ;<br> phase_difference_mean:units = "month" ;<br> } // group QBO<br> }</p> <p> </p>
Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"
<p>This datasset contains data to reproduce the following figures of the paper <em>Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site</em>:</p> <ul> <li> <p>Time series data of Figures 1c and 2</p> </li> <li> <p>Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4)</p> </li> <li>Pléiades snow depth map (Figure S1)</li> <li> <p>Data used for plotting Figure S2</p> </li> </ul> <p> </p>
Hrycyna et al. 2022 - Satellite observations of NO2 indicate legacy impacts of Redlining in US Midwestern cities
<p>This dataset contains remotely sensed estimates of nitrogen dioxide (NO2, via TROPOMI accessed via Google Earth Engine) for HOLC neighborhoods in 11 US Midwestern cities, and corresponding coarse geographic and demographic data of those cities. NO2 data is reported daily for the entire calendar year of 2019, geographic and demographic variables are fixed for each city for the entire year. Each HOLC-graded neighborhood included in this dataset was filtered to be greater than 2 km2. The number of pixels used to calculate the area-weighted mean of NO2 is also reported, as is the area of the neighborhood. The dataset has also been filtered for observations that did not pass quality filters for L3 TROPOMI data. The cities included in the study are: Chicago IL, Milwaukee WI, Saint Paul MN, Minneapolis MN, Indianapolis IN, Cleveland OH, Wichita KS, Greater Kansas City KS and MO, Columbus OH, Detroit MI, and Omaha NE. HOLC neighborhood shapefiles were obtained from the Mapping Inequality project website, hosted by the University of Richmond, and resulting polygons used in analysis were created by dissolving shared boundaries in Google Earth Engine. City populations and population density were obtained from the US 2010 Census data. All data was collected and organized to assess if current day NO2 levels varied with HOLC grades in these major cities.</p> <p> </p> <p>Data was used in the study: Hrycyna et al. (2022) <em>Elementa</em> 10(1):00027 </p> <div> <div><a href="https://doi.org/10.1525/elementa.2022.00027" target="_blank" rel="noopener">https://doi.org/10.1525/elementa.2022.00027</a></div> </div> <p>Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. https://dsl.richmond.edu/panorama/redlining/#loc=5/39.1/-94.58&text=downloads</p> <p><strong>Dataset for all analyses presented in Hrycyna et al. Columns described below:</strong></p> <p>HOLC_grade: A, B, C, D (neighborhood grade categories obtained from Mapping Inequality project, indicate historic HOLC designations of neighborhoods).</p> <p>HOLCAreaKm2: continuous area value in km2 of the HOLC neighborhood polygon, which may be more than one HOLC designated polygon merged from the shapefiles downloaded from Mapping Inequality.</p> <p>pixelcount: integer values of the number of TROPOMI NO2 pixels used to produce the area-weighted mean NO2 value.</p> <p>NO2_mol_m2: area-weighted mean value of TROPOMI NO2 for that HOLC neighborhood polygon in mol m-2</p> <p>system.index: designated date and time boundary of the observation collected via TROPOMI</p> <p>date: date of observation</p> <p>month: month of observation</p> <p>City: city in the US Midwest</p> <p>State: state for the city of focus</p> <p>Population: urban population obtained from 2010 census</p> <p>PopDensity: urban population density obtained from 2010 census, based on modern city boundaries (in people per square miles)</p> <p>CityArea_mi2: Area of the city of interest, in square miles.</p> <p>ln_NO2: natural log transformed NO2 values in mol m-2</p> <p>NO2_DU: NO2 value converted from mol m-2 to DU (Dobsons Units, converted by multiplying 2241.15)</p> <p>NO2_lnDU: natural log transformed NO2 values in DU<br><br></p>
Neural Network and objective analysis reconstruction of 3D Mediterranean physical fields from surface satellite and in situ observations at 1/24 deg
<p>Daily Mediterranean 3D fields of temperature, salinity and geostrophic current at 1/24° of resolution, up to 150m-depth and from 2016 to mid 2022, obtained through a 3 steps approach: (1) Temperature and salinity 3D fields have been first estimated by a machine learning approach by using mediterranean reanalysis outputs (https://doi.org/10.25423/CMCC/MEDSEA_MULTIYEAR_PHY_006_004_E3R) together with satellite observations, (2) a combination of this first step with in situ observations through an Optimal interpolation to remove part of large scale biases, (3) the computation of geostrophic currents using the thermal wind equation. This work has been funded by the European Space Agency through the 4DMED-SEA project [ESA contract No. 4000141547/23/I-DT].</p>
Estimating three-dimensional structures of eddy in the South Indian Ocean from the satellite observations based on the isQG method
<p>Supporting data for Estimating three-dimensional structures of eddy in the South Indian Ocean from the satellite observations based on the isQG method</p> <p>Matlab Codes to reconstruct the subsurface structures (Codes without Figure_*.m) and plot the figures (Figure_*.m) in the manuscript. The file in Netcdf format is our reconstructed 3D density and currents.</p> <p> </p>
Lagrangian Water Age trajectories initiated from the coastal 500m isobath and derived from surface velocities obtained from satellite observations
We conduct a Lagrangian particle trajectory analysis of surface velocities. We define an “offshore water age” as the time taken by a water parcel to be advected backward in time from its current position along its trajectory until it crosses the 500 m isobath. The rationale of this diagnostic is to detect filaments of coastal water advected offshore by horizontal transport and to estimate the time for water parcels in the filament o leave the coastal area. For example, a value of “20 days” assigned to a pixel means that the water parcel in that area was in the coastal area approximately 20 days before, where it was likely enriched in nutrients.
Ground-Truthing Satellite Imagery with Phenological Observations: Visual Observations from Grasslands at the Sevilleta National Wildlife Refuge, New Mexico
Phenology is the study of recurring natural phenomena. The seasonal "greening-up" and "greening-down" of dominant vegetation can be used as a predictor for a variety of processes and variables at local to global scales. The use of satellites to monitor land surface phenology is important for understanding local and regional ecosystem variability, identifying change over time, and potentially predicting ecosystem response to short and long-term changes in climate. However, the relationship between how phenology is expressed on the ground and how it is interpreted from satellites is poorly understood because phenological stages do not always correspond well to changes in spectral reflectance. In this study, we explored the relationship between greenness as measured by digital camera, the human eye, and ASTER imagery in two perennial grasslands at the Sevilleta National Wildlife Refuge in central New Mexico.
Eyjafjallajökull satellite observations
<p>Satellite observations based on "Hourly non-gridded volcanic ash properties retrieved from SEVIRI measurements for the Eyjafjallajökull 2010 eruption" (Kylling and Sollum 2020, CC BY-SA 4.0, 10.5281/zenodo.3830363). The data has been regridded using fimex 1.4.2 to match the simulation domain MACC14 and thus be colocated with "Three-hourly gridded volcanic ash emissions for the Eyjafjallajökull 2010 eruption" (Brodtkorb et al., 2020, 10.5281/zenodo.3818196). These two datasets are used as input for the volcanic ash inversion routines in "metno/VolcanicAshInversion" (Brodtkorb, 2020, 10.5281/zenodo.3818001)</p> <p> </p> <p> </p>
Primary Sea Ice Edge from Satellite Passive Microwave Observations
<p>These are matlab output files with smoothed and unsmoothed primary ice edges around Antarctica. The primary ice edge is defined as the northernmost contour of 15% sea ice concentration, and defines the outer boundary of sea ice extent. The brightness data come from three satellites; SSM/I, AMSR-E, and AMSR2 and are converted to sea ice concentrations with the NASA Team 2 algorithm and the ARTIST algorithm.</p>
Data from: Leveraging satellite observations to reveal ecological drivers of pest densities across landscapes
<p>Landscape ecologists have long suggested that pest abundances increase in simplified, monoculture landscapes. However, tests of this theory often fail to predict pest population sizes in real-world agricultural fields. These failures may arise not only from variations in pest ecology but also from the widespread use of categorical land-use maps that do not adequately characterize habitat availability for pests. We used 1163 field-year observations of <em>Lygus hesperus</em> (Western Tarnished Plant Bug) densities in California cotton fields to determine whether integrating remotely sensed metrics of vegetation productivity and phenology into pest models could improve pest abundance analysis and prediction. Because <em>L. hesperus</em> often overwinters in non-crop vegetation, we predicted that pest abundances would peak on farms surrounded by more non-crop vegetation, especially when the non-crop vegetation is initially productive but then dries down early in the year, causing the pest to disperse into cotton fields. We found that the effect of non-crop habitat on pest densities varied across latitudes, with a positive relationship in the north and a negative one in the south. Aligning with our hypotheses, models predicted that <em>L. hesperus</em> densities were 35 times higher on farms surrounded by high versus low productivity non-crop vegetation (EVI area 350 vs. 50) and 2.8 times higher when dormancy occurred earlier versus later in the year (May 15 vs. June 30). Despite these strong and significant effects, we found that integrating these remote-sensing variables into land-use models only marginally improved pest density predictions in cotton compared to models with categorical land cover metrics alone. Together, our work suggests that the remote sensing variables analyzed here can advance our understanding of pest ecology, but not yet substantively increase the accuracy of pest abundance predictions.</p>
Replication Data for figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s
<p>Supporting data to reproduce figures in: Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s</p>
AIMS - Earth Observation Satellite Data of Wave and Wind in the Tyrrhenian Sea
<p>This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible.</p> <p>In particular, this dataset aggregates earth observation satellite data from 10 different satellites, measureing the significant wave height and the wind speed at 10 meters above sea leavel in the Tyrrhenian Sea, from January 2021 to May 2024.</p>
Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds
<p>This is data from several atmosphere-only GCM experiments used to investigate the impacts of changing mixed-phase microphysical parameters in the CAM6 atmospheric model. Details and results from these simulations is presented in the submitted manuscript "Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds". A preprint of this manuscript can be found at https://www.essoar.org/doi/10.1002/essoar.10506728.2.</p> <p>An included README file describes organization of files. For any questions, please contact jonah.shaw@colorado.edu.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.