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363 results for “harmonics”
Harmonic Baseline Experiments for Landsat-Based Forest Condition Monitoring in Southern New England 2017
This dataset was developed as part of a study of harmonic baseline model parameterization for forest condition monitoring using Landsat time series. We implemented a previously published harmonic modeling approach for forest condition monitoring in Google Earth Engine and systematically assessed the relative ability of condition change products generated using various model parameterizations for predicting pest abundances and defoliation during the 2016-2018 Lymantria dispar outbreak in southern New England. We ran a series of 32 experiments that considered a variety of parameter choices for establishing multi-year “baseline” models representing relatively stable forest conditions for each Landsat pixel in our study area. We tested a full set of factors including (a) spectral vegetation index used for model fitting, (b) baseline-modeling period, (c) frequencies of harmonic regression terms, and (d) differences in Landsat time series input imagery. We generated average condition score estimates for each of these 32 baseline parameterizations for a May 1 to September 30, 2017 monitoring period, then used Generalized Linear Mixed Models to test the relationships between ground-based observations of defoliation and defoliator abundance (larva and egg masses). This archived dataset includes the full set of experimental raster results, as well as a “reanalysis” product from a previous implementation of our condition monitoring workflow. More information on model parameterization rankings can be found in the associated publication (Pasquarella et al. 2021).
Harmonized National Land Cover Dataset Values for HydroBASINS Basins
Water quality is largely reflective of processes occurring on the surrounding landscape. While national landcover data are widely available via remotely sensed products, they are usually not aggregated in a manner that is expeditiously merged with basin-level data. To facilitate national-scale analyses of basin-level landcover with co-located water quality data, we present aggregated land cover 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.
Harmonized Palmer Drought Severity Indices throughout the Contiguous United States for HydroBASINS basins
In times of a changing hydroclimate and growing human population, there is a need to assess how various climatic and demand conditions influence water availability on the landscape. Tendency for drought conditions is a prime example of a key hydroclimatic metric that is useful for understanding water retention in the surrounding landscape. However, merging drought climatological data with co-located aquatic data is challenging. To facilitate national-scale analyses of basin-level drought conditions (i.e., Palmer Drought Severity Index; PDSI) with co-located water quality data, we present aggregated PDSI 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.
Harmonized Soil Organic Carbon and Phosphorus Data for the Contiguous United States
Soil organic carbon (SOC) and soil phosphorus can strongly influence adjacent water quality by introducing nutrients into aquatic ecosystems and also altering the light environment of those ecosystems. However, national-scale data are uncommon, and even when available, they are usually not aggregated in a manner that is expeditiously merged with basin-level data. To facilitate national-scale analyses of soil data with co-located water quality data, we present aggregated SOC and soil phosphorus 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.
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.
Spectral Vegetation Indices from Harmonized Landsat and Sentinel-2 Data for Harvard Forest 2015-2020
The goal of this work is to exploit time series of remotely sensed data sets with ground observations to improve our understanding of how seasonal variation in canopy and environmental conditions affect the relationship between vegetation indices and leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (fAPAR). Using three different common vegetation indices (EVI2, NDVI, NIRV), we can estimate LAI, fAPAR, and daily absorbed photosynthetically active radiation (APAR) using a semi-empirical model.
Harmonized IACS inventory
<h2>Inventory description</h2> <p>The <strong>Harmonized IACS inventory of Europe-LAND</strong> is a harmonised collection of data from the Geospatial Aid (GSA) system of the Integrated Control and Administration System (IACS), which manages and controls agricultural subsidies in the European Union (EU). The GSA data are a unique data source with field-levels of land use information that are annually generated. The data carry information on crops grown per field, a unique identifier of the subsidy applicants that allows to aggregate fields to farms, and information on organic cultivation.</p> <p>The inventory contains all data that can be shared following the General Data Protection Regulations (GDPR) of the data providers. <span lang="EN-GB">It covers 19 EU member states with time series up to 17 years. For most members states, only the crop information can be shared. However, for six member states also the </span><span lang="EN-GB">farm identifier (Czechia, Denmark, Estonia, Ireland, Portugal and Spain)</span><span lang="EN-GB"> and for five also the </span><span lang="EN-GB">organic management information (Austria, Flanders in Belgium, Denmark, Ireland, and Bulgaria)</span><span lang="EN-GB"> can be shared.</span> </p> <p>Due <span lang="EN-GB">to General Data Protection Regulations (GDPR), </span><span lang="EN-GB">we are not allowed to share all data</span><span lang="EN-GB"> that we collected and harmonised. We hold GSA data for six additional member states (Italy, Greece, Poland, Hungary, Romania, and Cyprus) as well as supplementary information on farm-level indicators for 17 additional member states, federal states, or regions (Austria, Cyprus, Greece, Latvia, Netherlands, Romania, Sweden, Slovenia, Slovakia, Wallonia in Belgium, Brandenburg, Lower Saxony, Saxony-Anhalt, and Thuringia in Germany, and Emilia-Romagna, Marche, and Toscana in Italy,) and organic farming information for seven more member states, federal states, or regions (Greece, Netherlands, Sweden, Slovenia, Slovakia, Wallonia in Belgium, and Brandenburg, Lower Saxony, Saarland, Saxony-Anhalt, and Thuringia in Germany). For Luxembourg and Malta, only LPIS data were available. As these datasets contain only reference parcels without detailed land-use information at the parcel level, they were not included in the inventory.</span> </p> <p>I<span lang="EN-GB">f you use the data, please also </span><span lang="EN-GB">cite the original sources of the data</span><span lang="EN-GB">. You can find the references in the </span><span lang="EN-GB">documentation provided</span><span lang="EN-GB"> </span><span lang="EN-GB">in the "_Documentation.zip".</span> </p> <p>The crop information were harmonised using the <strong>Hierarchical Crop and Agriculture Taxonomy (HCAT) </strong>of the <a href="https://zenodo.org/records/14094196" target="_blank" rel="noopener">EuroCrops</a> project (<a href="https://doi.org/10.1038/s41597-023-02517-0" target="_blank" rel="noopener">Schneider et al., 2023</a>). To allow for interoperability with EuroCrops, the harmonised Europe-LAND data come with the same column names that relate to the crop information. All crop mapping tables can be found in our <a href="https://github.com/clejae/europe_land_iacs_prep">GitHub repository</a>.</p> <h3>Column names:</h3> <ul> <li>field_id (mandatory): Unique identifier for each parcel per member state, state, or region</li> <li>farm_id (optional): Unique identifier for each farm per member state, state, or region</li> <li>crop_code (mandatory): Original, member state-specific crop code</li> <li>crop_name (mandatory): Original, member state-specific crop name</li> <li>EC_trans_n (mandatory): Original crop name translated into English</li> <li>EC_hcat_n (mandatory): Machine-readable HCAT name of the crop</li> <li>EC_hcat_c (mandatory): The 10-digit HCAT code indicating the hierarchy of the crop</li> <li>organic (optional): Whether a parcel was conventional (0), organic (1), or is in the conversion process to organic cultivation (2)</li> <li>field_size (mandatory): Size of parcel/reference parcel in hectares</li> <li>crop_area (optional): Area in hectares of the main crop reported in crop column. The crop_area column only occurs if multiple crops are reported per reference parcel.</li> </ul> <p>M<span lang="EN-GB">ore detailed information for all members states in our harmonised inventory can also be found in the documentation.</span> </p> <div> <p><span lang="EN-GB">The inventory will be updated at least annually</span><span lang="EN-GB">. We will update as additional data becomes available and as new versions of the HCAT are released. Moreover, in future versions, we will add new data on information on agri-environmental measures, eco-schemes, and animal numbers per farm. </span> </p> </div> <h2>Information on data provision</h2> <p><strong>Al<span lang="EN-GB">l files come as .geoparquets</span></strong><span lang="EN-GB"> to stay within the space limitations of Zenodo. Geoparquets can simply be opened in QGIS via drag and drop. Additionally, various libraries from different porgramming languages are able to handle geoparquets, e.g. geoarrow and sgarrwo in R, GDAL/OGR in C++, GeoParquet.jl in Julia or Fiona in Python.</span> </p> <div> <p><span lang="EN-GB">We bundled multiple years of each member state to stay below the file number limitation of Zenodo. Each zip file name indicates the member state, federal state, or region and the years covered. The meaning of the abbreviations of the members states, federal states, and regions can be found in the "country_region_codes.xlsx" in the "_Documentation.zip". </span> </p> </div> <div> <p><span lang="EN-GB">The Spanish data are also bundled across regions, as they are separated into 50 regions. See the country_regions_codes.xlsx tables for the meaning of the abbreviations:</span> </p> </div> <ul> <li>ES_Bundle1 (Northeast): BAL, BAR, CAS, GIR, HEC, LLE, NAV, TAR, TER, ZAR</li> <li>ES_Bundel2 (Northwest): ACO, ALA, AST, BUR, CAN, GUI, LEO, LRI, LUG, OUR, PAL, PON, VIZ, VLD, ZAM</li> <li>ES_Bundle3 (West): ALB, AVI, CAC, CIU, CUE, GUA, MAD, SAL, SEG, SOR, TOL</li> <li>ES_Bundle4 (Southwest): ALI, ALM, BAD, CAD, CDB, GRA, HEV, JAE, LAP, MAL, MUR, SAN, SEV, VLC</li> </ul> <h2>Changelog</h2> <p><strong>V</strong><span lang="EN-GB"><strong>ersion 1.2:</strong> </span><span lang="EN-GB">In this version, we have corrected the inventory description and documentation to only refer to data that we share publicly. Additionally, we corrected an error in the Spanish data, where the crop_code column got mixed up during pre-processing. This did not affect the other columns, which were correct. Moreover, we have uploaded new data for Bulgaria to the inventory.</span> </p> <p><strong>Version 1.1:</strong> In this version, we corrected some data errors that occured in v1 due to a failure of our quality checks. First, not all fields got classified in v1, and secondly, there were two different datatypes in the EC_hcat_c in many files. Both errors are now corrected.</p>
Data release for paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models"
<p>This data release for the paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models" [<a href="https://arxiv.org/abs/2010.05830">arXiv:2010.2010.05830</a>] contains posterior samples for the GW190412 binary black hole merger event obtained from public GWOSC data with the parallel bilby Bayesian inference package, dynesty nested sampler and a set of waveforms from the "generation X" of phenomenological waveform models: IMRPhenomXAS, IMRPhenomXHM, IMRPhenomXP, IMRPhenomXPHM, IMRPhenomT and IMRPhenomTHM. The provided file is a "meta file" that can be read with the <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a> python package. The posterior samples included correspond to runs [2,6,10,12,14,26] in Table III of the paper (standard settings for each waveform, standar priors and sampler settings of Nlive=2048 and Nact=10 or 50). If you make use of these samples, please cite both this data release and the paper.</p>
Spherical harmonic models of the shape of Titan
<p>This archive contains spherical harmonic models of the shape of Saturn's moon Titan constructed from data collected by the Cassini mission. Two such models are here archived:</p> <ul> <li>Titan_shape_Mitri2014_unnorm.sh (Mitri et al. 2014)</li> <li>Titan_shape_Corlies2017_unnorm.sh (Corlies et al. 2017)</li> </ul> <p>Both models make use of unnormalized spherical harmonic functions that include the Condon-Shortley phase factor of (-1)^m. The model from Mitri et al. (2014) is developed to spherical harmonic degree 6, whereas the model of Corlies et al. (2017) is developed to degree 8. Note that most gravity models of Titan use spherical harmonic functions that exclude the Condon-Shortley phase factor.</p>
Spherical harmonic models of the shape of asteroid (1) Ceres [JPL SPC]
<p>This archive contains two spherical harmonic models of the shape of asteroid (1) Ceres, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 1023, which was generated from an ICQ shape model with Q=1024.</p> <p>The data used to generate these models are from a JPL stereo photoclinometric shape model based on Dawn framing camera images, as found in the file <code>CERES_SPC181019_1024.ICQ</code> on <a href="https://sbnarchive.psi.edu/pds3/dawn/fc/DWNCSPC_4_01/DATA/ICQ/">NASA's PDS website</a>. The vertices were first converted from Cartesian to spherical coordinates, from which a regular gridline registered netcdf file was created using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>surface</code> with a tension of 0.6 and with a grid spacing of 0.087890625 degrees. This file was then read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The two files in this archive are</p> <ul> <li>Ceres_JPL_SPC_shape_1023.sh.gz</li> <li>Ceres_JPL_SPC_shape_719.sh.gz</li> </ul> <p>The numbers 1023 and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of ~11.4 and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution model was generated by truncating the spherical harmonic coefficients of the highest resolution model.</p>
Spherical harmonic models of the shape of Enceladus [JPL SPC]
<p>This archive contains two spherical harmonic models of the shape of Saturn's moon Enceladus, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 1023, which was generated from an ICQ shape model with Q=1024.</p> <p>The data used to generate these models are from a JPL stereo photoclinometric shape model based on images obtained by the Cassini mission, as found in the file <code>cas_enceladus_ssd_spc_1024icq_v1.bds</code> on <a href="https://naif.jpl.nasa.gov/pub/naif/pds/data/co-s_j_e_v-spice-6-v1.0/cosp_1000/data/dsk/">NASA's PDS website</a>. The vertices were first converted from Cartesian to spherical coordinates, from which a regular gridline registered netcdf file was created using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>surface</code> with a tension of 0.6 and with a grid spacing of 0.087890625 degrees. This file was then read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The two files in this archive are</p> <ul> <li>Enceladus_JPL_SPC_shape_1023.bshc.gz</li> <li>Enceladus_JPL_SPC_shape_719.bshc.gz</li> </ul> <p>The numbers 1023 and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of ~11.4 and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution model was generated by truncating the spherical harmonic coefficients of the highest resolution model.</p>
Spherical harmonic models of the gravity field of the Galilean satellites [Galileo]
<p>This archive contains spherical harmonic models of the gravitational potential of the Galilean satellites derived from data collected by the Galileo mission. For all models, the spherical harmonic coefficients are to be used with unnormalized spherical harmonics that exclude the Condon-Shortley phase factor of (-1)^m. The fist line of each file is a header that contains the reference radius (in km), the GM and its uncertainty (in km^3/s^2), and the k2 Love number and its uncertainty (for Io only).</p> <p>The files in this archive with the asociated references are:</p> <ul> <li>Anderson2001_Io_gravity.sh (Anderson et al. 2001)</li> <li>Anderson1998_Europa_gravity.sh (Anderson et al. 1998)</li> <li>Anderson1996_Ganymede_1_gravity.sh (Anderson et al. 1996, encounter 1)</li> <li>Anderson1996_Ganymede_2_gravity.sh (Anderson et al. 1996, encounter 2)</li> <li> <div>Anderson2001_Callisto_gravity.sh (Anderson et al. 2001)</div> </li> </ul>
Spherical harmonic models of the shape of the Moon (principal axis coordinate system) [LOLA]
<p>This archive contains four spherical harmonic models of the shape of the Moon in a principal axis coordinate system, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5759, which was generated from a lunar shape model sampled at 64 pixels per degree.</p> <p>The data used to generate these models are from the LOLA instrument on the Lunar Reconaissance Orbiter, as found in the file <code>ldem_64_pa.img</code> on <a href="https://pds-geosciences.wustl.edu/lro/lro-l-lola-3-rdr-v1/lrolol_1xxx/data/lola_gdr/cylindrical/pa/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and the resulting gridline-registered netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Moon_LOLA_shape_pa_5759.bshc.gz</li> <li>Moon_LOLA_shape_pa_2879.bshc.gz</li> <li>Moon_LOLA_shape_pa_1439.bshc.gz</li> <li>Moon_LOLA_shape_pa_719.bshc.gz</li> </ul> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>This shape model uses the same coordinate system as most lunar gravity models. The principal axis coordinate system differs from the more common mean Earth/polar axis system by about 1 km at the equator. For a mean Earth/polar axis model, use <a href="../records/10796823">Spherical harmonic models of the shape of the Moon</a>.</p>
Spherical harmonic models of the shape of the Moon [LOLA]
<p>This archive contains four spherical harmonic models of the shape of the Moon truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5759, which was generated from a lunar shape model sampled at 64 pixels per degree in the DE421 mean Earth/polar axis coordinate frame.</p> <p>The data used to generate these models are from the LOLA instrument on the Lunar Reconaissance Orbiter, as found in the file <code>ldem_64_float.img</code> on <a href="https://pds-geosciences.wustl.edu/lro/lro-l-lola-3-rdr-v1/lrolol_1xxx/data/lola_gdr/cylindrical/float_img/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and the resulting pixel registed map was then converted to a gridline registration using the function <code>grdsample</code>. Following this, the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Moon_LOLA_shape_5759.bshc.gz</li> <li>Moon_LOLA_shape_2879.bshc.gz</li> <li>Moon_LOLA_shape_1439.bshc.gz</li> <li>Moon_LOLA_shape_719.bshc.gz</li> </ul> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>Note that this shape model should not be used in conjunction with most gravity models of the Moon. The gravity models use a principal axis coordinate system that differs from the mean Earth/polar axis frame by about 1 km at the equator. For a principal axis coordinate system model, use <a href="../doi/10.5281/zenodo.10796953">Spherical harmonic models of the shape of the Moon (principal axis coordinate system)</a>.</p>
Spherical harmonic models of the shape of asteroid (4) Vesta [DLR SPG]
<p>This archive contains four spherical harmonic models of the shape of asteroid 4 Vesta, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5759, which was generated from a shape model sampled at 64 pixels per degree.</p> <p>The data used to generate these models are from a DLR stereo photogrammetric shape model based on Dawn high altitude mapping orbit framing camera images, as found in the file <code>VE_HAMO_G_00N_330E_EQU_DTM.IMG</code> on <a href="https://sbnarchive.psi.edu/pds3/dawn/fc/DWNVSPG_2/DATA/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and it was then converted to a gridline registration using the function <code>grdsample</code>. The grid was then shifted such that the frist column corresponded to 0 E longitude using the function <code>grdedit</code>, and the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Vesta_DLR_SPG_shape_5759.bshc.gz</li> <li>Vesta_DLR_SPG_shape_2879.bshc.gz</li> <li>Vesta_DLR_SPG_shape_1439.bshc.gz</li> <li>Vesta_DLR_SPG_shape_719.bshc.gz</li> </ul> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p>
Spherical harmonic models of the shape of asteroid (1) Ceres [DLR SPG]
<p>This archive contains four spherical harmonic models of the shape of asteroid (1) Ceres, truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5399, which was generated from a shape model sampled at 60 pixels per degree.</p> <p>The data used to generate these models are from a DLR stereo photogrammetric shape model based on Dawn high altitude mapping orbit framing camera images, as found in the file <a href="https://sbnarchive.psi.edu/pds3/dawn/fc/DWNCHSPG_2/DATA/"><code>CE_HAMO_G_00N_180E_EQU_DTM.IMG</code></a> on <a href="https://sbnarchive.psi.edu/pds3/dawn/fc/DWNCHSPG_2/DATA/">NASA's PDS website</a>. This image file was first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, it was then converted to a gridline registration using the function <code>grdsample</code>, and the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Ceres_DLR_SPG_shape_5399.bshc.gz</li> <li>Ceres_DLR_SPG_shape_2879.bshc.gz</li> <li>Ceres_DLR_SPG_shape_1439.bshc.gz</li> <li>Ceres_DLR_SPG_shape_719.bshc.gz</li> </ul> <p>The numbers 5399, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 60, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p>
Spherical harmonic models of the shape of Mars [MOLA]
<p>This archive contains four spherical harmonic models of the shape of Mars truncated at different maximum spherical harmonic degrees. The highest resolution model has a maximum spherical harmonic degree of 5759, which was generated from a Mars shape model sampled at 64 pixels per degree.</p> <p>The data used to generate these models are from the MOLA instrument on the Mars Global Surveyor spacecraft, as found in the files <code>MEGR00N000GB.IMG</code>, <code>MEGR00N180GB.IMG</code>, <code>MEGR90N000GB.IMG</code> and <code>MEGR90N180GM.IMG</code> on <a href="https://pds-geosciences.wustl.edu/mgs/mgs-m-mola-5-megdr-l3-v1/mgsl_300x/meg064/">NASA's PDS website</a>. These four image files were first converted to netcdf format using the <a href="https://www.generic-mapping-tools.org/">generic-mapping-tools</a> function <code>xyz2grd</code>, and then turned into a single file using the function <code>grdpaste</code>. The resulting pixel registed map was then converted to a gridline registration using the function <code>grdsample</code>. Following this, the resulting netcdf file was read into the <a href="https://shtools.github.io/SHTOOLS/index.html">pyshtools</a> software and expanded into spherical harmonics using the function <code>SHCoeffs.expand()</code>. The spherical harmonic functions were chosen to be "4pi" normalized and to exclude the Condon-Shortley phase factor of (-1)<sup>m</sup>. The units of the coefficients are meters.</p> <p>The four files in this archive are</p> <ul> <li>Mars_MOLA_shape_5759.bshc.gz</li> <li>Mars_MOLA_shape_2879.bshc.gz</li> <li>Mars_MOLA_shape_1439.bshc.gz</li> <li>Mars_MOLA_shape_719.bshc.gz</li> </ul> <p>The numbers 5759, 2879, 1439, and 719 in the filename refer to the maximum spherical harmonic degree of file, which corresponds to effective spatial resolutions of 64, 32, 16, and 8 pixels per degree, respectively. The files are stored in the binary "bshc" format as described in the pyshtools documentation and are furthermore compressed using gzip. The lower resolution models were generated by truncating the spherical harmonic coefficients of the highest resolution model.</p> <p>These models supercede <a href="../records/3870922">Spherical harmonic model of the shape of Mars: MarsTopo2600</a> and <a href="../records/6475460">Spherical harmonic model of the shape of Mars: MarsTopo719</a>.</p>
Spherical harmonic models of the gravity field of Uranus
<p>This archive contains published spherical harmonic models of the gravity field of Uranus. The coefficients are to be used with unnormalized spherical harmonic functions that exclude the Condon-Shortely phase factor of (-1)^m, and the file is formatted in a manner to be read by the <a href="https://shtools.github.io/SHTOOLS/">pyshtools</a> software (using format='shtools'). The header of the file contains the reference radius, GM, GM uncertainty, and maximum degree of the spherical harmonic expansion (all in SI units).</p> <p>* Jacobson2014.sh</p>
Spherical harmonic models of the gravity field of Saturn
<p>This archive contains published spherical harmonic models of the gravity field of Saturn. The coefficients are to be used with unnormalized spherical harmonic functions that exclude the Condon-Shortely phase factor of (-1)^m, and the file is formatted in a manner to be read by the <a href="https://shtools.github.io/SHTOOLS/">pyshtools</a> software (using format='shtools'). The header of the file contains the reference radius, GM, GM uncertainty, and maximum degree of the spherical harmonic expansion (all in SI units).</p> <p>* Jacobson2022.sh</p>
HarP: Harmonized Prior river-lake database
<p><strong>Contact</strong>: Md Safat Sikder (mssikder@illinois.edu), Jida Wang (jidaw@illinois.edu)</p> <p> </p> <p><strong>Citation</strong></p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Crétaux, J.-F., and Pavelsky, T. M., 2024. HarP: Harmonized Prior river-lake database. <em>Zenodo</em>, <a href="https://doi.org/10.5281/zenodo.14205131">https://doi.org/10.5281/zenodo.14205131</a>.</p> <p>If you only use the PLD-TopoCat dataset, please cite the following paper:</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. <em>Earth System Science Data</em>, 15, 3483-3511, <a href="https://doi.org/10.5194/essd-15-3483-2023">https://doi.org/10.5194/essd-15-3483-2023</a>.</p> <p> </p> <p><strong>Data description and components</strong></p> <p><strong>The Harmonized Prior river-lake database (HarP) for SWOT</strong> integrated the SWOT River Database (SWORD) (<em>Altenau et al.</em>, 2021) and the SWOT Prior Lake Database (PLD) (<em>Wang et al.</em>, 2023) into <strong>a geometrically (lake/river) explicit but topologically harmonized vector database</strong> to allow for coupled fluvial-lacustrine applications, including a synergistic use of both river and lake products from SWOT. </p> <p>In addition to the input river network (SWORD v16) and lake database (PLD v106), we used the MERIT Hydro v1.0.1 (<em>Yamazaki et al.</em>, 2019), a high-resolution (~90 m) global hydrography dataset, to develop this database.</p> <p>The SWORD-PLD harmonization process involves three major steps, with Step 3 being divided into three sub-steps. The processing chain is illustrated in the attached Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>", as well as in Section 2 of the product description document. The HarP database consists of the outputs from each of the steps. For convenience, the global landmass (excluding Antarctica) was partitioned to 68 Pfafstetter Level-2 basins/regions, with their IDs shown in Figure "<em>Pfaf2_basins.jpg</em>" attached.</p> <p> </p> <p>The HarP database consists of five datasets or components (outputs from each step), each with multiple features. The five datasets are described below, and more details are elaborated in the product description document.</p> <p><strong>1. Harmonized SWORD-PLD </strong>(file name "<em>Harmonized_SWORD_PLD</em>"): This is the fully harmonized SWORD-PLD dataset, <strong>the primary product of HarP </strong>(i.e., output of Step 3.3 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). This dataset couples SWORD and PLD into a geometrically segmented but topologically integrated dataset at the node, reach, and catchment scales (stored by three feature layers, respectively): </p> <p> (a) Harmonized feature nodes: Harmonized_feature_nodes_pfaf_xx<br> (b) Harmonized river network: Harmonized_river_network_pfaf_xx<br> (c) Harmonized feature catchments: Harmonized_feature_catchments_pfaf_xx<br> Note: ''pfaf_xx'' indicates the Pfafstetter Level-2 basin ID (shown in Fig. 'Pfaf2_basins.jpg').</p> <p>Figure "<em>HarP_example.jpg</em>", attached to this database, is an example of the fully harmonized SWORD-PLD dataset for the Ohio River Basin. The example shows three main features of the dataset: feature nodes (i.e., reach downstream ends, lake inlets, and lake outlets; see Fig. 3 in the product description document for definitions), river reaches (i.e., reaches characterized by SWORD alone, characterized by TopoCat alone, and shared by both SWORD and TopoCat), and catchments segmented by each of the feature nodes.</p> <p><strong>2. Intersected SWORD-PLD drainage configuration </strong>(file name "<em>Intersected_SWORD_PLD</em>"): This dataset is the intersected SWORD-PLD (prior river-lake) features (i.e., output of Step 2 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). This dataset was constructed independently from Step 1 and Step 3. In this dataset, the original geometries of SWORD and PLD are not altered, but instead, their geometric and drainage topological relationships are configured in the attribute tables. This dataset consists of three features:</p> <p> (a) Intersected reaches: Intersected_SWORD_reaches_pfaf_xx<br> (b) Intersected nodes: Intersected_SWORD_nodes_pfaf_xx<br> (c) Intersected lakes: Intersected_PLD_lakes_pfaf_xx</p> <p><strong>3. PLD-TopoCat </strong>(file name "<em>PLD_TopoCat</em>"): This dataset is the lake drainage topology and catchments (TopoCat) for PLD lakes (i.e., output of Step 1 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). PLD-TopoCat was developed to generate detailed lake drainage topology and connecting paths, which were later used to configure the off-SWORD-network PLD lakes into the tributaries that drain to SWORD. PLD-TopoCat was generated from PLD v106 and MERIT Hydro. Details of the developiong process and algorithm for TopoCat can be found at Sikder at al., (2023). PLD-TopoCat dataset contains six features:</p> <p> (a) Lake original polygon: PLD_lakes_pfaf_xx<br> (b) Lake raster polygon: Lake_raster_polygons_pfaf_xx<br> (c) Lake outlets: Lake_outlets_pfaf_xx<br> (d) Lake catchments: Lake_catchments_pfaf_xx<br> (e) Inter-lake reaches: Inter_lake_reaches_pfaf_xx<br> (f) Lake-network basins: Lake_network_basins_pfaf_xx<br> Note: full version of the PLD-TopoCat is available <a href="https://doi.org/10.5281/zenodo.14202301">here</a>.</p> <p><strong>4. SWORD-mirror network </strong>(file name "<em>SWORD_mirror</em>"): The SWORD-mirror network was constructed to facilitate the SWORD-TopoCat network merging process (i.e., output of Step 3.1 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). It is essentially <strong>a replica of SWORD except that the original SWORD reaches are geometrically modified to be aligned with the topological/hydrographic information depicted in MERIT Hydro</strong>. The SWORD-mirror network consists of four features:</p> <p> (a) SWORD-original reaches: SWORD_original_reaches_pfaf_xx<br> (b) SWORD-mirror prelim. reaches: SWORD_mirror_prelim_reaches_pfaf_xx<br> (c) SWORD-mirror reaches: SWORD_mirror_reaches_pfaf_xx<br> (d) SWORD-mirror reach catchments: SWORD_mirror_reach_catchments_pfaf_xx</p> <p><strong>5. Merged SWORD-mirror – TopoCat network </strong>(file name "<em>SWORD_TopoCat_merged</em>"): This dataset is the output of Step 3.2 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>". It is essentially the merged product of the inter-lake reaches (from Step 2) and SWORD-mirror reaches (from Step 3.1). The merged SWORD-mirror – TopoCat network consists of three features:</p> <p> (a) Merged SWORD-TopoCat reaches: SWORD_TopoCat_merged_reaches_pfaf_xx<br> (b) SWORD nodes at SWORD-TopoCat confluence: SWORD_TopoCat_confluence_nodes_pfaf_xx<br> (c) Reach catchments for merged network: SWORD_TopoCat_reach_catchments_pfaf_xx</p> <p>The attribute tables for each of the feature components are explained in Section 4 of the product description document. All files of HarP are available in both shapefile and geodatabase formats.</p> <p> </p> <p><strong>Disclaimer</strong><br>Authors of this dataset claim no responsibility or liability for any consequences related to the use, citation, or dissemination of HarP. For any quesitons, please contact Safat Sikder and Jida Wang.</p>
ScienceDex guides
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International Brain Laboratory public data
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OpenNeuro
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