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1,574 results for “atmosphere”
Desert Fertilization Experiment: investigation of Sonoran desert ecosystem response to atmospheric deposition and experimental nutrient addition, ongoing since 2006
Launched in 2006 with support from the National Science Foundation (NSF) and leveraged by the CAP LTER, the Carbon and Nitrogen deposition (CNdep) project sought to answer the fundamental question of whether elemental cycles in urban ecosystems are qualitatively different from those in non-urban ecosystems. Ecosystem scientists, atmospheric chemists, and biogeochemists tested the hypothesis that distinct biogeochemical pathways result from elevated inorganic nitrogen and organic carbon deposition from the atmosphere to the land. To test the hypothesis, scientists examined the responsiveness of Sonoran desert ecosystems to nutrient enrichment by capitalizing on a gradient of atmospheric deposition in and around the greater Phoenix metropolitan area. Fifteen desert study sites were established, with five locations each west and east of the urban core, and in the urban core in desert preserves. In addition to the gradient of atmospheric deposition in and around the urban core, select study plots at each of the fifteen desert locations receive amendments of nitrogen, phosphorus, or nitrogen + phosphorus fertilizer. Measured variables include soil properties, perennial and annual plant growth, and atmospheric deposition of nitrogen. At the close of the initial grant period, the CAP LTER assumed responsibility for the project, renamed the Desert Fertilization Experiment, which provides a remarkable platform to study the long-term effects of nutrient enrichment on desert ecosystem properties.
Harmonized National Atmospheric Deposition Products for HydroBASINS basins
Water quality is largely reflective of processes occurring on the surrounding landscape. While terrestrial inputs often strongly influence water quality, atmospheric deposition can be a significant source of allochthonous constituents to aquatic ecosystems. In the Contiguous United States, the National Atmospheric Deposition Program (NADP) has been collecting in situ atmospheric deposition of several key ions for decades. However, merging these data with co-located aquatic data is challenging. To facilitate national-scale analyses of basin-level atmospheric deposition of sulfate, ammonium, nitrate, and hydrogen with co-located water quality data, we present aggregated atmospheric deposition data for the Contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.
Desert Fertilization Experiment: investigation of Sonoran desert ecosystem response to atmospheric deposition and experimental nutrient addition, ongoing since 2006 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/632/9. The abstract below was extracted from the Level 0 data package and is included for context:
Canopy-Atmosphere Exchange of Carbon, Water and Energy at Harvard Forest EMS Tower since 1991
The tower-based CO2 measurements and key meteorological drivers are intended to examine how regional and ecosystem level processes in a mid-latitude forest contribute to global carbon cycling. Specifically, we endeavor to understand quantitatively how and why forested ecosystems take up or release carbon, on time scales from hours to decades, and to elucidate responses to climate changes and management interventions. The tower was installed 1989 and the resulting eddy-flux measurements constitute the longest running record of the net-ecosystem carbon exchange in a North American Forest. The resulting long-term record of Net Ecosystem Exchange (NEE) has shown the effects of climate anomalies on carbon fluxes for seasonal and annual time scales. For example, reduced soil frost allows greater respiration in the winter leading to lower C sequestration. Cumulative gross photosynthesis depends on when the canopy emerges in the spring. Warmer springtime temperatures lead to greater uptake of C. As the NEE record is extended and augmented by supporting ecological measurements, we can further identify longer-term effects of climate perturbations on carbon fluxes and further define the relationship between stand history and carbon sequestration. Climatic anomalies in one season or year may have a longer-term effect on the sequestration of carbon in subsequent seasons or years. The flux and ecological measurements are coordinated with studies at other sites through the AmeriFlux network. By examining the relationships between carbon fluxes and the driving physical and biological variables across a range of sites we are enhancing understanding of the processes that control NEE.
Column-Averaged Atmospheric Carbon and Water at Harvard Forest since 2018
Ground-based total-atmospheric column measurements of CO2, CH4, CO, and H2O began at Harvard Forest in May 2018 using a solar-viewing Fourier Transform Spectrometer (Bruker EM27/Sun). Observations are made from a platform near the Fisher Meteorological Tower throughout the year on sunny days. Total column observations are sensitive to regional carbon fluxes, and are less impacted by changes in the boundary layer height than tower based in-situ observations. The Harvard Forest observations are complemented by an identical instrument operated on the Harvard campus in Cambridge MA. The two sensors are used to support studies of urban emissions, regional transport, and forest-atmosphere exchange. In addition, the total-column measurements provide key validation data for satellite measurements of greenhouse gases.
Desert Fertilization Experiment: investigation of Sonoran desert ecosystem response to atmospheric deposition and experimental nutrient addition, ongoing since 2006 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/253/3, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/632/9. The abstract below was extracted from the Level 0 data package and is included for context:
Atmospheric Oxygen and Carbon Dioxide at Harvard Forest EMS Tower since 2006
This archive features long-term measurements of the atmospheric mixing ratios of O2 and CO2 at two heights on the Harvard Forest EMS flux tower. The data fields include the time of data collection, the height from which the samples were drawn, the O2 mixing ratio and the CO2 mixing ratio.
Atmospheric Gaseous Elemental Mercury Fluxes at Harvard Forest EMS Tower 2019-2020
In terrestrial ecosystems, dry deposition of atmospheric gaseous elemental mercury (GEM) is considered the dominant source of mercury accounting for 54% to 94% of mercury loads observed in soils, yet direct quantification of GEM deposition across forests is largely missing. The goal of this project is to quantify atmosphere-surface exchange of GEM at Harvard Forest for one full year, providing the first such record in a non-polluted forest. GEM exchange is measured using micrometeorological techniques using a large measurement tower, the only available method for direct, non-intrusive and time-extended measurements of net GEM exchange at the ecosystem level encompassing all underlying sinks and sources. A second objective was to partition GEM fluxes into canopy and soil contributions via deployment of two corresponding flux systems: one system was deployed above the forest canopy to measure ecosystem-level GEM exchange; a second system was deployed below the canopy to quantify soil contributions. This dataset contains an 18-month record of gaseous elemental mercury concentrations and fluxes measured at the EMS tower at Harvard Forest from May 2019 to August 2020.
Simulations of Historical Impacts of Climate Change and Atmospheric Chemistry at Harvard Forest 1850-2019
This study is a model application aimed at simulating historical carbon (C), nitrogen (N), and water dynamics at a hardwood forest stand at Harvard Forest from 1850 to 2019. We applied the PnET-CN-daily model with a reconstructed historical climate and air quality scenario derived from field observations and regional model simulations. The model outputs were calibrated with field measurements conducted at Harvard Forest. We used field measurements of aboveground biomass (AGB) and foliar mass near the EMS tower to calibrate ecosystem C pools. Gross primary production (GPP), net ecosystem exchange (NEE), and respiration from the EMS eddy flux tower were used to calibrate C fluxes. Net N mineralization data from the chronic N amendment experiment, along with other N dynamics data collected at Harvard Forest, were used to calibrate N pools and fluxes. Additionally, evapotranspiration (ET) and soil water content from the EMS tower were used to calibrate water fluxes. To isolate the effects of individual environmental factors on C, N, and water dynamics, we ran the PnET-CN-daily model with a series of theoretical scenarios. These scenarios were developed based on the reconstructed historical climate and air quality data while keeping non-target input factors at pre-industrial levels. The considered environmental factors include climate, carbon dioxide (CO2) concentration, atmospheric N deposition, and ozone (O3) concentration. This approach allowed us to decompose the influence of each factor on ecosystem dynamics by comparing model outputs across different scenarios.
Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"
<p>This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>.</p> <p>The file "goes-samples-2019-128x128.nc" contains the training dataset called "GOES-COT" in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files "gen_weights*.nc" contain the generator weights saved at different time steps during training for the two different datasets described in the paper.<br> </p>
Data presented in Devenish and Cerminara, Journal of Geophysical Research Atmosphere, 2021. doi:10.1029/2020JD033699
<p>The files contain the raw data of the atmospheric and concentration profiles respectively used and calculated by the LES and LSM simulations presented in Devenish and Cerminara (2020).</p> <p>The concentration data have been stored in two ASCII columns, the first being the elevation with respect to the vent level, and the second the concentration normalised by the initial concentration, where the initial concentration is the product of the source mass flux and the exit velocity.</p> <p>For the two cases of the intercomparison study, the initial mass flux is 1.5e6 kg/s and 1.5e9 kg/s for the weak and strong cases, respectively. The respective exit velocities are 135 m/s and 275 m/s.</p> <p>For the twenty cases with ambient wind, the initial mass flux and exit velocities can be extracted from the information given in the paper.</p> <p>Additional information can be found in Costa et al. (2016) and Aubry et al. (2019).</p>
Atmospheric profiling data collected from radiosondes in the Southern Ocean in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The data set consists of the vertical profiles of the atmospheric variables measured using radiosondes (i-Met) during the Antarctic Circumnavigation Expedition from November 2016 to April 2017. The data include the raw variables measured directly by the radiosondes and derived parameters: altitude (km), air pressure (mb), air temperature (ºC), relative humidity (%), frostpoint (ºC), potential temperature (ºK), water vapour mixing ratio (ppmv), total column water (mm w.e.), wind speed (m/s) and wind direction (deg).</p> <p><strong>Dataset contents</strong></p> <ul> <li>aceNNN_yyyymmdd, directory <ul> <li>aceNNN_yyyymmdd.csv, data file, comma-separated values</li> <li>aceNNN_yyyymmdd.kml, metadata, XML</li> <li>aceNNN_yyyymmdd.raw, data file, raw, ASCII DOS</li> <li>aceNNN_yyyymmdd.raw_config, metadata, XML</li> <li>aceNNN.de1, metadata, ASCII text format</li> <li>aceNNNflt.dat, data file, ASCII text format</li> <li>aceNNNpre.dat, data file, ASCII text format</li> </ul> </li> <li>plots, directory <ul> <li>Sounding_ACENNN.png, metadata, portable network graphics</li> </ul> </li> <li>data_file_header_csv.txt, metadata, text format</li> <li>data_file_header_dat.txt, metadata, text format</li> <li>data_file_header_launches.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>overview_radiosonde_launches.csv, metadata, comma-separated value</li> </ul> <p>where NNN is the launch number yyyy is the year, mm is the month and dd is the day. Dates are in UTC.</p> <p>json files make up a Frictionless Data package.</p> <p><strong>Dataset citation</strong></p> <p>Please cite this dataset as:</p> <p>Gorodetskaya, I.V., Thurnherr, I., Tsukernik, M., Graf, P., Aemisegger, F., Wernli, H. and Ralph, F.M. (2021). Atmospheric profiling data collected from radiosondes in the Southern Ocean in the austral summer of 2016/2017 during the Antarctic Circumnavigation Expedition. (Version 1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4382460</p>
Generative convective parametrization of a dry atmospheric boundary layer
<p>The repository contains simulation snapshots of a dry convective boundary layer (CBL). The snapshots comprise horizontal snapshots of vertical velocity (w) and buoyancy (b) field at three heights, namely z/h(t) = 0.2, 0.5, 1.0. Further, the Python scripts for the Generative Adversarial Network (GAN) are also provided, as well as the DNS renormalization procedure.</p>
Datasets for: Generalizing Monin-Obukhov Similarity Theory (1954) for Complex Atmospheric Turbulence, Stiperski and Calaf 2023, PRL
<p>Scaling variables for the generalized flux-variance scaling relations that include turbulence anisotropy. Dataset is a companion to the manuscript Stiperski, I., Calaf, M., 2023: Generalizing Monin-Obukhov similarity theory (1954) for complex atmospheric turbulence. Physical Review Letters, 130 (12), 124001, https://doi.org/10.1103/PhysRevLett.130.124001</p> <p>The dataset contains the turbulence statistics from 13 datasets: AHATS, Cabauw, CASES-99, METCRAX II campaign (NEAR and RIM towers), T-Rex campaign (Central tower - TRexC, West tower - TRexW) and i-Box measurement network (CCS-VF0 tower - i-Box0, CS-SF1 tower - i-Box1, CS-NF10 tower - i-Box10, CS-NF27 tower - i-Box27, CS-MT21 tower - i-BoxTop, im Hinteren Eis tower - imHint).</p> <p><br>Data are organized in csv files for each datasets and only contain high quality (for applied criteria see the Supplemental Material of the companion paper, https://journals.aps.org/prl/supplemental/10.1103/PhysRevLett.130.124001) data with 30 min averaging for unstable stratification and 1 min for stable stratification. Since the data were used for scaling, there is no reference to time, but the measurement height is provided as an additional variable. </p> <p>Meaning of variables:</p> <p>zeta - z/L where z is height above ground and L is the local Obukhov length</p> <p>SigmaU - $\overline{u'u'}/u_*$ scaled standard deviation of streamwise velocity, where $u_*$ is the local friction velocity</p> <p>SigmaU - $\overline{v'v'}/u_*$ scaled standard deviation of spanwise velocity</p> <p>SigmaU - $\overline{v'v'}/u_*$ scaled standard deviation of surface-normal velocity</p> <p>SigmaT - $\overline{T'T'}/T_*$ scaled standard deviation of sonic temperature, where $T_*$ is the local temperature scale</p> <p>SigmaEpsU - scaled dissipation rate of the streamwise velocity</p> <p>SigmaEpsW - scaled dissipation rate of the surface-normal velocity </p>
Atmospheric Halocarbon Observations at Finokalia, Crete, Greece
<p>Atmospheric halocarbon (HFC, HCFC) observations (mole fractions) from the site Finokalia (FKL, 35.34 °N, 25.67 °E, 250 m a.s.l.) on the island of Crete, Greece, covering the period December 2012 to August 2013). The measurements were conducted using a gas chromatograph<br> (Agilent 6890) and:mass spectrometer (Agilent 5973) (GC-MS), coupled to an adsorption desorption system (ADS) for preconcentration of samples from the air (Simmonds et al., 1995).</p> <p>The measurements are described in detail in: Schoenenberger, F., S. Henne, M. Hill, M. K. Vollmer, G. Kouvarakis, N. Mihalopoulos, S. O'Doherty, M. Maione, L. Emmenegger, T. Peter, and S. Reimann (2017), Abundance and Sources of Atmospheric Halocarbons in the Eastern Mediterranean, Atmos. Chem. Phys. Discuss., 2017, 1-46, doi: 10.5194/acp-2017-451.</p> <p>The data format is plain text character-separated and follows that used in the AGAGE community. Further details are given at the AGAGE data archive: http://agage.eas.gatech.edu/data_archive/agage/</p>
A vertically-resolved atmospheric dust reanalysis for Mars Years 28-29 using Analysis Correction
<p>This is a dataset of meteorological variables for the atmosphere of Mars, obtained by assimilating measurements (retrievals) of atmospheric temperature and dust opacity into a 3-dimensional, time-dependent numerical model of the Martian atmospheric circulation (known as a “reanalysis”).</p> <p>The observations come from two spacecraft - the Mars Climate Sounder (MCS) instrument on board NASA’s Mars Reconnaissance Orbiter (e.g. Kleinboehl et al. 2009) and the Thermal Emission Imaging Spectrometer (THEMIS) on board NASA’s Mars Odyssey spacecraft, and cover the period from 21 September 2006 until 5 November 2009 (Mars Years 28:Ls=109.98 - 30:Ls=4.78). MCS observations include profiles of temperature and dust opacity from near the surface up to altitudes of around 80 km obtained from infrared limb-sounding (MCS version 3 retrievals, based on opacities at around 21.6 micron wavelengths), while THEMIS measurements are of column dust opacity in the infrared (centred around 9.3 micron wavelength). Further details can be found on the websites</p> <p>https://pds-geosciences.wustl.edu/missions/odyssey/themis.html,<br> https://atmos.nmsu.edu/data and services/atmospheres data/MARS/aerosols.html</p> <p>The model into which the observations are assimilated is the UK version of Laboratoire de Météorologie Dynamique Mars Global Circulation Model (LMDMGCM), a 3-dimensional, time-dependent numerical circulation model of the Martian atmosphere and near-surface environment, simulating the changing winds, temperature, pressure and dust content of the atmosphere across the whole planet. The model solves the equations of motion, mass and energy conservation using a spherical harmonic representation in the horizontal and finite difference formulation in the vertical direction, but outputs the data here on a regular longitude-latitude grid with 72 points in longitude, 36 points in latitude and 25 terrain-following sigma levels in the vertical direction (where sigma = pressure/surface pressure) on a stretched vertical grid that extends from the surface to an altitude of approximately 100 km. More details can be found in publications by Forget et al. (1999), Newman et al. (2001), Mulholland et al. (2013).</p> <p>The observations and model are linked by an assimilation scheme, based on the Analysis Correction (AC) algorithm developed by Lorenc et al. (1991) and adapted for Mars by Lewis et al. (2007). Previous reanalyses of Mars observations using this scheme include the MACDA dataset (Montabone et al. 2014) and OPENMars (Holmes et al. 2020). This new dataset, however, makes use of an extension of the AC scheme to enable assimilation of both column integrated dust opacity measurements and dust opacity profiles in the vertical direction (see Ruan et al. 2021). This new dataset therefore provides a more realistic representation of the distribution of dust loading in the Martian atmosphere than previous work, which may also result in improved representation of other meteorological variables, notably temperature.</p> <p>Data are provided as 2D and 3D fields of variables in netCDF format as generated by the numerical model on the (longitude, latitude, sigma) grid at 2-hourly intervals. Each file contains 360 time steps covering 30 Martian days or sols. The variables contained in each file are as follows:</p> <p> Variables and attributes<br> 0 lon: FLOAT(72) = FLOAT(lon)<br> 0 long_name: longitude<br> 1 units: degrees_east<br> 1 lat: FLOAT(36) = FLOAT(lat)<br> 0 long_name: latitude<br> 1 units: degrees_north<br> 2 sigma: FLOAT(25) = FLOAT(sigma)<br> 0 long_name: sigma<br> 1 units: sigma_level = p/ps<br> 3 soil: FLOAT(18) = FLOAT(soil)<br> 0 long_name: soil levels (i.e. levels below the surface to represent thermal variations)<br> 1 units: none<br> 4 time: FLOAT(360) = FLOAT(time)<br> 0 long_name: model time<br> 1 units: days since 00:00:00 (the beginning of the file)<br> 5 controle: FLOAT(100) = FLOAT(lentable)<br> 0 long_name: Table of run parameters<br> 1 description: MGCM run 5.000<br> 6 Ls: FLOAT(360) = FLOAT(time)<br> 0 Physics_diagnostic: Solar longitude (such that Ls=0 is northern Spring equinox)<br> 1 units: deg<br> 7 tsurf: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Surface temperature<br> 1 units: K<br> 8 ps: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: surface pressure<br> 1 units: Pa<br> 9 co2ice: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: co2 ice thickness (column mass density)<br> 1 units: kg.m-2<br> 10 fluxsurf_lw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_lw (surface infrared radiative flux)<br> 1 units: W.m-2<br> 11 fluxsurf_sw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_sw (surface visible radiative flux)<br> 1 units: W.m-2<br> 12 temp: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: temperature<br> 1 units: K<br> 13 u: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Zonal (east-west) wind<br> 1 units: m.s-1<br> 14 v: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Meridional (north-south) wind<br> 1 units: m.s-1<br> 15 rho: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: density<br> 1 units: kg.m-3<br> 16 udrag: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Drag velocity<br> 1 units: m/s<br> 17 udragt: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Threshold velocity for dust lifting<br> 1 units: m/s<br> 18 aerosol: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: dust opacity considering layer thickness<br> 1 units: SI (opacity/m)<br> 19 taudustvis: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Dust optical depth<br> 1 units: SI<br> 20 q01: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: mix. ratio<br> 1 units: kg/kg<br> 21 dqsdevtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust devil lift rate<br> 1 units: kg.m-2.s-1<br> 22 dqsstrtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: near surface wind stress dust lifting rate<br> 1 units: kg.m-2.s-1<br> 23 dqssedtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust sedimentation rate<br> 1 units: kg.m-2.s-1</p> <p> </p>
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Spatial and Temporal Patterns in Atmospheric Deposition of Dissolved Organic Carbon
Atmospheric deposition of dissolved organic carbon (DOC) to terrestrial ecosystems is a small, but rarely studied component of the global carbon (C) cycle. Emissions of volatile organic compounds (VOC) and organic particulates are the sources of atmospheric C and deposition represents a major pathway for the removal of organic C from the atmosphere. Here, we evaluate the spatial and temporal patterns of DOC deposition using 70 datasets at least one year in length ranging from 40° south to 66° north latitude. Globally, the median DOC concentration in bulk deposition was 1.7 mg L-1. The DOC concentrations were significantly higher in tropical (< 25°) latitudes compared to temperate (> 25°) latitudes. DOC deposition was significantly higher in the tropics because of both higher DOC concentrations and precipitation. Using the global median or latitudinal specific DOC concentrations leads to a calculated global deposition of 202 or 295 Tg C yr-1 respectively. Many sites exhibited seasonal variability in DOC concentration. At temperate sites, DOC concentrations were higher during the growing season; at tropical sites, DOC concentrations were higher during the dry season. Thirteen of the thirty-four long-term (> 10 years) datasets showed significant declines in DOC concentration over time with the others showing no significant change. Based on the magnitude and timing of the various sources of organic C to the atmosphere, biogenic VOCs likely explain the latitudinal pattern and the seasonal pattern at temperate latitudes while decreases in anthropogenic emissions are the most likely explanation for the declines in DOC concentration.
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (No Long-term Weather Trend) Control Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations without a long term weather trend.
Model Simulations of The Effects of Shifts in High-frequency Weather Variability (With a Long-term Trend) on Carbon Loss from Land to the Atmosphere, Toolik Lake, Alaska, 2022-2122
Climate change is increasing extreme weather events, but effects on high-frequency weather variability and the resultant impacts on ecosystem function are poorly understood. We assessed ecosystem responses of arctic tundra to changes in day-to-day weather variability using a biogeochemical model and stochastic simulations of daily temperature, precipitation, and light. Changes in weather variability altered ecosystem carbon, nitrogen, and phosphorus stocks and cycling rates. Some responses of processes (e.g., respiration) were inconsistent with expectations, indicating that whole-ecosystem interactions and feedbacks moderate or even reverse responses to weather variability. More weather variability led to greater carbon losses from land to atmosphere, and less variability led to higher carbon sequestration on land. The magnitude of response to weather variability was similar to that predicted from climate mean trend effects. This dataset consists of the MEL parameter file, driver files and output files for simulations with a long-term weather trend.
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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.