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2,322 results for “precipitations”
INCA Example Data - Precipitation
<p>This <a href="https://www.unidata.ucar.edu/software/netcdf/">netcdf</a> includes the output of a single run of the <a href="https://www.meteoswiss.admin.ch/home/services-and-publications/produkte.subpage.html/en/data/products/2021/nowcasting-inca-ch.html">INCA nowcasting system</a> for variable precipitation (mm/h).</p>
Nanobeam electron diffraction dataset from ion irradiated DIN 1.4970 austenitic stainless steel with G-phase precipitates collected on pixelated TVIPS detector
<p><strong>Summary</strong></p> <p>This is a 4D scanning transmission electron microscopy (4D STEM) dataset collected in near-parallel beam mode (NBED) from a sample of ion irradiated austenitic (FCC) stainless steel of the DIN 1.4970 specification, collected on a high quality pixelated detector inside a transmission electron microscope (TEM). The dataset is represented by a 4D array, comprising a 2D grid of scan points, with each scan point mapping to an electron diffraction spot pattern. From this kind of dataset it is possible to derive local crystal orientations and strains. The dataset is in the .hspy format, the native hdf5 format of the <a href="https://zenodo.org/record/5082777">HyperSpy</a> library.</p> <p>The main features in this dataset are:</p> <ul> <li>a single crystal of the matrix is sampled, close to a 110 zone axis</li> <li>inside the matrix, irradiation induced G-phase precipitates of 10-20 nm in size can be found which contribute weakly to some of the diffraction patterns. From these patterns it is possible to derive the orientation relationship of the precipitates with respect to the matrix.</li> <li>irradiation also resulted in the formation of faulted frank loops, which also show up in some diffraction patterns.</li> </ul> <p><strong>Material and sample preparation</strong></p> <p>The sample was prepared from DIN 1.4970 steel (composition by weight: 15% Ni, 15% Cr, 1.8% Mn, 1.2% Mo, 0.5% Ti, 0.5% Si, 0.1% C, Fe Bal.) with the intended application of nuclear fuel cladding material. The material was originally in the shape of thin walled tubes and cold worked to 24% (measured by cross sectional area reduction). The material was aged for 2 hours at 800 °C. It was then irradiated to 40 dpa surface damage as calculated using the SRIM program and the Kinchin and Pease model with displacement energy of 40 eV, using 4.5 MeV Fe<sup>2+</sup> ions with a flux of arround 9x10<sup>11</sup> ions.s<sup>-1</sup>.cm<sup>-2</sup>. The irradiation was performed at 600 °C. Full details on the material, irradiation conditions, and context can be found in:</p> <p>Cautaerts, N., Delville, R., Stergar, E., Pakarinen, J., Verwerft, M., Yang, Y., Hofer, C., Schnitzer, R., Lamm, S., Felfer, P., & Schryvers, D. (2020). The role of Ti and TiC nanoprecipitates in radiation resistant austenitic steel : A nanoscale study. <em>Acta Materialia</em>, <em>197</em>, 184–197. https://doi.org/10.1016/j.actamat.2020.07.022</p> <p>A TEM sample was prepared by regular focused ion beam (FIB) lift-out techniques in a Ga-ion FIB. Additional details on the dataset can be found in the paper and supplementary materials of</p> <p>Cautaerts, N., Rauch, E. F., Jeong, J., Dehm, G., & Liebscher, C. H. (2021). Investigation of the orientation relationship between nano-sized G-phase precipitates and austenite with scanning nano-beam electron diffraction using a pixelated detector. <em>Scripta Materialia</em>, <em>201</em>, 113930. https://doi.org/10.1016/j.scriptamat.2021.113930</p> <p><strong>Microscopy parameters and data collection</strong></p> <p>NBED was performed in a JEM-2200FS TEM (JEOL) operating at 200 kV. The microscope was operated in nanobeam diffraction mode with the smallest spot size (Spot 5). The probe diameter was ~ 1 nm with a semi-convergence angle of ~0.5 mrad. Data was collected on a TemCam-XF416 pixelated CMOS detector (TVIPS). The camera length as indicated in the operating software was 80 cm, and collected images were 1024 by 1024 in size (hardware binning of 4). The dataset comprises 260x200 scan points and pixel depth is 2 bytes (unsigned 16 bit integers).</p> <p><strong>Data processing</strong></p> <p>The raw data was collected in the .tvips format. The original dataset was about 50 GB in size and can be shared upon request to the author. This dataset was converted to the .hspy format using the <a href="https://zenodo.org/record/4288857">TVIPSconverter</a> tool. In the conversion, the images were binned by an additional factor of 4 to a final size of 256x256. A median filter was also applied to the data to remove pixel noise.</p> <p><strong>Data characteristics</strong></p> <p>Scan shape: 260 x 200 pixels</p> <p>Image shape: 256 x 256 pixels</p> <p>Pixel dtype: uint16</p> <p>Scan pixel size: about 1 nm, scan dimensions were never calibrated</p> <p>Image pixel size: 0.01261 Angstrom<sup>-1</sup> / pixel</p> <p>Note that scale factors are not stored in the dataset! The dataset can be read with HyperSpy using the load function (please see the HyperSpy documentation) and the pixel scale can be set through the axes manager. It is highly recommended to have a working installation of <a href="https://zenodo.org/record/5075520">Pyxem</a> as well to process the data.</p> <p><strong>Additional notes</strong></p> <p>Data was collected with the TVIPS scan generator which can be quite buggy. The scan lines show "jitters" due to the unstable snake-scan pattern, hysteresis and instability.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly accumulated total precipitation
<p><strong>Please refer to the latest-released version (v2) of this dataset</strong></p> <p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at a hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly accumulated total precipitation for the period 1995-2020.</p> <p><strong>Update 09/10/2025</strong>: tpH Dataset Version 2 Released:</p> <p>A new version of the dataset (v2) has been published, incorporating the following improvements and corrections:</p> <ul> <li>Precipitation data have been cleaned to remove duplicated fields that were inadvertently included in the initial release. Additionally, the data have been decumulated to represent hourly precipitation values. In the original version, precipitation was reported as accumulations increasing over the day from 00 UTC to 23 UTC.This format has now been replaced by actual hourly precipitation totals, offering a representation that is more relevant and useful for most applications.</li> <li>Grid inconsistencies present in some GRIB messages have been resolved to ensure structural uniformity across the dataset.</li> <li>Data have been rescued for some of the data holes. In the cases when only 1 hour was missing from the original extraction, the field has been produced by averaging the two fields associated with the previous and next hours to ensure the most continuous data series as possible. Particularly this is the case for the following grib messages: <ul> <li>23 UTC of 31 December 1995</li> <li>23 UTC of 31 December 1998</li> <li>23 UTC of 19 February 2020</li> <li>23 UTC of 13-17-22-30 July 2020</li> <li>23 UTC of 4-14 August 2020</li> </ul> </li> </ul> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and the hourly surface air temperature at 2-meter height:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
Recovery of Lithium Carbonate from Dilute Li-Rich Brine via Homogenous and Heterogeneous Precipitation
<p>An extensive experimental campaign on Li recovery<br> from relatively dilute LiCl solutions (i.e., Li+ ∼ 4000 ppm) is<br> presented to identify the best operating conditions for a Li2CO3<br> crystallization unit. Lithium is currently mainly produced via solar<br> evaporation, purification, and precipitation from highly concentrated<br> Li brines located in a few world areas. The process requires<br> large surfaces and long times (18−24 months) to concentrate Li+<br> up to 20,000 ppm. The present work investigates two separation<br> routes to extract Li+ from synthetic solutions, mimicking those<br> obtained from low-content Li+ sources through selective Li+<br> separation and further concentration steps: (i) addition of<br> Na2CO3 solution and (ii) addition of NaOH solution + CO2<br> insufflation. A Li recovery up to 80% and purities up to 99% at 80<br> °C and with high-ionic strength solutions was achieved employing NaOH solution + CO2 insufflation and an ethanol washing step.</p>
CLGAN: Guizhou ML-AWS precipitation dataset
<p>This repository provides the preprocessed datasets and source codes, which are used in the study 'CLGAN: A GAN-based video prediction model for precipitation nowcasting' by Ji et al. (2022). This allows the user to reproduce the presented results for the precipitation nowcasting task.<br> <br> To build the dataset, minute-level precipitation measurements by rain gauges of Automatic Weather Stations over Guizhou, China is collected and preprocessed. The raw AWS data comprise measurements from 93 national basic stations and 1740 automatic weather stations across Guizhou at a high observation frequency (every minute). The raw data spans from 1st Jan 2015 and 31st Dec 2019 and is provided by the Guizhou Meteorological Bureau. A series of preprocessing are performed to obtain the proposed dataset, including bilinear interpolation, rainy sequence selection, log transformation and normalization. The details of preprocessing can be found in the paper mentioned above.<br> <br> This folder holds five single netCDF-files which respectively record the data from 2015 to 2019. Each netCDF-file contains preprocessed precipitation sequences with a shape of [n_sequence, len_sequence, n_lat, n_lon]. n_sequence is the number of the recorded sequences, len_sequence is the length of each sequence, n_lat and n_lon are the number of grid points in the meridian and zonal directions. In our case, the target domain covers from 24.625°N to 29.50°N and 103.625°E to 109.50°E (40x48 grid points) with a resolution of 0.125° and the length of each sequence is 24.<br> </p>
Precipitation oxygen isoscape for mainland China from 1870 to 2017 generated based on data fusion and bias correction of iGCMs simulations
<p>The dataset includes the stable oxygen isotope of precipitation for the mainland of China over the 1870-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. In order to make full use of observations to integrate the advantages of various iGCMs, the combination of data fusion and bias correction methods are used. Some physical-based ancillary data are introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion. Specifically,</p><p>(1) for the 1979-2001 period, nine simulations from six iGCMs (CAM2, GISS E, HadAM3, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused with observations by using the CNN fusion method;</p><p>(2) for the 2002-2007 period, seven simulations from four iGCMs (GISS E, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused by using the CNN fusion method;</p><p>(3) for the 1969-1978 period, four simulations from three iGCMs (CAM2, GISS E, and HadAM3) and ancillary data are fused by using the CNN fusion method;</p><p>(4) for the 1958-1968 and 2008-2017 periods, two iGCM simulations (CAM2 and HadAM3 for 1958-1968 and IsoGSM2 and LMDZ4 zoomed for 2008-2017) are corrected by using two BCMs, and ensemble mean (mean of four simulations) is then calculated;</p><p>(5) for the 1870-1957 period, one iGCM simulation (HadAM3) is corrected by using two BCMs, and the ensemble mean (mean of two simulations) is then calculated.</p><p>Compared with the existing iGCMs, the isoscape has high quality and stability for a large region in China at the monthly scale. However, it should be noted that the isoscape may be more reliable for the common periods of most iGCMs (1969-2007), but mediocre for other periods. </p>
Indonesia, monthly Standardized Precipitation-Evapotranspiration Index (SPEI) blend 1960 - 2021
<p>IDN_CLI_SPEI_blend_0p042_1961_2021 is currently the only comprehensive high resolution Indonesia gridded historical dataset of SPEI blend and available for public.</p> <p>The SPEI - https://spei.csic.es/ is an extension of the widely used SPI. The SPEI is designed to take into account both precipitation and potential evapotranspiration (PET) in determining drought. Thus, unlike the SPI, the SPEI captures the main impact of increased temperatures on water demand.</p> <p>The IDN_CLI_SPEI_blend_0p042_1960_2021 is derived using precipitation and potential evapotranspiration from TerraClimate data - https://www.climatologylab.org/terraclimate.html, it has 0.042 degree gridded resolution, a monthly and available from 1958 to 2021. The calibration period is January 1961 to December 2020. The starting date of the dataset is 1960 in order to provide common information across the different SPEI time-scales.</p> <p>The SPEI blend integrate several SPEI scales into a single product, combine 3-, 6-, 9-, 12- and 24-month SPEI to estimate the overall dry/wet condition. </p> <p>The SPEI processed using climate_indices, an open source Python library providing reference implementations of commonly used climate indices. https://pypi.org/project/climate-indices/</p>
High-resolution IMERG satellite precipitation data data (1km) in Iberia Peninsula
<p>In summary, the SMPD method with the use of surface water balance principle has a solider physical basis than previous downscaling methods. Through introducing SSM as an auxiliary variable, the impact of inherent bias in satellite estimates on the downscaled results can be moderately reduced compared to the conventional statistical method. The validation with rain gauge data highlights the importance of SSM as a fully independent source of information that can be effectively used for downscaling coarse-resolution precipitation at a daily scale, which is rarely conducted in current related studies.</p> <p>He, K., Zhao, W., Brocca, L., and Quintana-Seguí, P.: SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation, Hydrol. Earth Syst. Sci., 27, 169–190, https://doi.org/10.5194/hess-27-169-2023, 2023.</p> <p> </p>
Extreme precipitation records in Antarctica [Dataset]
<p>This is the dataset associated to the research 'Extreme precipitation records in Antarctica' published in <em>International Journal of Climatology</em>.</p> <p>This repository contains:</p> <ul> <li>Precipitation extremes for each <em>model</em> at every grid point for a duration of <em>xxx</em> days. Files named: <ul> <li>[<em>model</em>]_PCP_max_[<em>xxx</em>]d.csv <ul> <li>Dimensions for ERA5: [lons, lats]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: mm</li> </ul> </li> </ul> </li> <li>Dimensions to plot the precipitation extremes: lons (longitudes), lats (latitudes) and duration. Files named: <ul> <li>[<em>model</em>]_PCP_max_lats.csv <ul> <li>Dimensions for ERA5: [lats]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: degrees</li> </ul> </li> <li>[<em>model</em>]_PCP_max_lons.csv <ul> <li>Dimensions for ERA5: [lons]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: degrees</li> </ul> </li> <li>[<em>model</em>]_PCP_max_duration.csv <ul> <li>Dimensions: [time]</li> <li>Units: days</li> </ul> </li> </ul> </li> <li>World Precipitation Records from 1 day. File named: <ul> <li>Max_WR_from1day.csv (first row duration [days]; second row precipitation [mm])</li> </ul> </li> </ul> <p> </p> <p><strong>How to cite</strong></p> <p>If you use this dataset, please cite the accompanying paper as:</p> <p>González-Herrero, S.,Vasallo, F., Bech, J., Gorodetskaya, I., Elvira, B., & Justel, A. (2023). Extreme precipitation records in Antarctica.International Journal of Climatology, 43(7), 3125–3138. <a href="https://doi.org/10.1002/joc.8020">https://doi.org/10.1002/joc.8020</a></p> <p> </p> <p><strong>Complementary code</strong></p> <p>You can find the jupyter notebooks to complement the research in: <a href="https://github.com/sergigonzalezh/Extreme_PCP_Scaling_Antarctica">https://doi.org/10.1002/joc.8020</a></p> <p> </p> <p><strong>Contact</strong></p> <p>If you have any question, please contact with Sergi at <a href="mailto:sergi.gonzalez@slf.ch">sergi.gonzalez@slf.ch</a></p>
High-Resolution Daily Precipitation Data over the Philippines (Alcantara and Ahn, 2023)
<p>The high-resolution nationwide 20-year (2001-2020) daily precipitation data over the Philippines generated by Alcantara and Ahn (2023) is a dataset that provides daily precipitation data for the entire country with a spatial resolution of 0.1° x 0.1°. This dataset is created using the Quadruple Collocation (QC) approach, which combines four parent datasets - ERA5, PERSIANN, CHIRPS, and GPM - to produce a more accurate dataset than any of the individual parent datasets. The dataset is available in NetCDF format (version 4), which is a standardized data format commonly used for the exchange of large data files. This format can be read using major programming languages such as R, Python, C++, and others, making it easily accessible for use in various research applications. With its high spatial and temporal resolution, the dataset can be a valuable resource for climate research, hydrological modeling, and other related fields. In the future, data from 2021 up to present will also be added.</p>
Precipitation and fire history at landslide sites
<p>These data include precipitation and burned area histories for events listed in the NASA Global Landslide Catalog. Each landslide includes a location uncertainty estimate. Precipitation values are the mean of all values within the uncertainty radius, while the fraction burned is computed for burned area.</p> <p>These data are intended to be used with the an RMarkdown notebook available at <a href="http://doi.org/10.5281/zenodo.7653683">this GitHub repository</a></p>
Dataset for: "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)"
<p>The version 1.0 contains the supporting data for the work (still under submission) "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)".</p> <p>The following files are here available (all file are georeferenced in EPSG: 3003):</p> <p>- AVG_Rainfall_1990-2019.tif -> Raster map of the mean annual precipitation for the northern Tuscany, Italy. It encompasses the portion of the Tuscany region northern of the cities of Livorno - Florence. The interpolation was validated via a leave one out cross-validation procedure.</p> <p>- D3-1_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1921 to 1950 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy).</p> <p>- D3-2_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1951 to 1980 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy).</p> <p>- DeltaSHP_Points_AVG_Annual_Rainfall.zip -> Shape file of the raingauges locations with the mean annual precipitation values of the period 1990 to 2019.</p> <p>- RaingaugesSHP_Points_AVG_Annual_Rainfall_1990-2019.zip -> Shape file of the raingauges locations with the following information: differences in the mean annual precipitation values between the two 3-decades periods 1951 to 1980 and 1990 to 2019 (named D3-2); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1951 to 1980 and 1990 to 2019; difference in the mean annual precipitation values between the two 3-decades periods 1921 to 1950 and 1990 to 2019 (named D3-1); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1921 to 1950 and 1990 to 2019.</p>
Precipitation data from Vatten i Göteborg
<p>Precipitation data from Drakegatan weather station close to Westlink construction site.</p>
Precipitation data from SMHI
<p>Precipitation data from SMHI weather station close to Westlink construction site.</p>
Global Urban Precipitation Anomalies
<p>This research reports the global urban precipitation anomalies for over one thousand cities worldwide. We provide the shapefiles of one urban domain and three rural domains (of different distances from the urban edges) for each city. The precipitation data include the mean daily precipitation, extreme precipitation magnitude, and extreme precipitation frequency in the urban and rural domains between 2001 and 2019 based on the IMERG precipitation dataset. Besides, data about mean elevation, wind, land surface temperature, aerosol optical thickness, and urbanization are also provided.</p>
Grain-Scale In-situ Study of Discontinuous Precipitation in Mg-Al
<p>Submitted manuscript and data for paper "Grain-Scale In-situ Study of Discontinuous Precipitation in Mg-Al"</p> <p>Files:</p> <p>Manuscript: insitu_dp_pap.pdf</p> <p>Video: Mg DP.mp4</p> <p>Area 1 image stack: Area_10xxx.tif </p> <p>Area 2 image stack: Aligned 752 of 7520xxx.tif</p> <p>Matlab code: boundarytracker.m, concprof_oblate.m (see code for details)</p> <p>Figures and associated data: *.fig (Matlab figure format), *.txt (ASCII text format)</p> <p> </p>
Updated gridded reconstruction of sea level pressure, temperature, and precipitation during winter in the North Atlantic region covering 1241-1970 CE
<ul> <li>This dataset is an updated version of the gridded climate reconstruction by Sjolte et al. 2018 (SEA18): Solar and volcanic forcing of North Atlantic climate inferred from a process-based reconstruction, <em>Climate of the Past,</em> 14, 1179–1194, https://doi.org/10.5194/cp-14-1179-2018. </li> </ul> <p> </p> <ul> <li>Relevant results of this new version (SEA18v2) are available in our recent paper: Tao, Q. , Sjolte, J. , & Muscheler, R. (2023). Persistent model biases in the spatial variability of winter North Atlantic atmospheric circulation. Geophysical Research Letters, 50, e2023GL105231. https://doi.org/10.1029/2023GL105231</li> </ul> <p> </p> <ul> <li>This dataset contains the gridded reconstruction of winter sea level pressure (slp), 2m temperature (t2m) and precipitation (precip) for the North Atlantic region over 1241-1970.</li> </ul> <p> </p> <ul> <li><strong>Methodology:</strong> The new reconstruction (SEA18v2), has been optimized for a better representation of the variability of the main modes of sea level pressure. The original reconstruction, SEA18, was an ensemble of 39 model analogues for each year and the reconstruction comprised of the mean of the analogues. For the new version, SEA18v2, a different approach to calculating the ensemble mean of the analogues has been applied. While the overall evaluation and ranking of model analogues are the same as for SEA18, we now apply a weighting function so that poor-fitting model analogues receive less weight and good-fitting analogues receive more weight. Furthermore, we evaluate the main modes of the reconstructed SLP and test the minimum number of ensemble members that can be used and still retain skill for the temporal and spatial variability of the first three modes. Retaining 16 ensemble members gives better performance for the spatial patterns for the first three EOFs of SLP compared to SEA18 and good skill for the temporal variability of the NAO.</li> </ul>
ECHAM6-wiso nudged simulation water isotopes and precipitation for the period 1990-2020 at the EastGRIP drilling location, Greenland
<p>This model dataset contains output produced with the isotope-enabled atmosphere GCM ECHAM6-wiso at T127L95 spatial resolution, nudged to the ERA-5 reanalysis product. The 6-hourly model output is provided for the period 01/1990-12/2020 for the grid cell containing EastGRIP drilling location in Greenland, centered at 75.27N, -36.57 E.</p> <p>The complete description of the simulation can be found in:</p> <p><em>Cauquoin, A. and Werner, M., 2021. High‐Resolution Nudged Isotope Modeling With ECHAM6‐Wiso: Impacts of Updated Model Physics and ERA5 Reanalysis Data. Journal of Advances in Modeling Earth Systems, </em><a href="https://doi.org/10.1029/2021MS002532">https://doi.org/10.1029/2021MS002532</a></p> <p>The provided files (netCDF) contain the ECHAM6-wiso model data used as input for the SNOWISO snow pack model in:</p> <p><em>Dietrich, L.J., Steen-Larsen, H.C., Wahl, S., Jones, T.R., Town, M.S. and Werner, M., 2023. Snow-atmosphere humidity exchange at the ice sheet surface alters annual mean climate signals in ice core records. Geophysical Research Letters, </em><a href="https://doi.org/10.1029/2023GL104249">https://doi.org/10.1029/2023GL104249</a><em>.</em></p> <p>The provided variables are:</p> <p> d18O_vapor: delta value for <sup>18</sup>O (‰) in the vapor of the lowest atmospheric layer (ECHAM level 95).<br> dD_vapor: delta value for H<sub>2</sub> (D) (‰) in the vapor of the lowest atmospheric layer (ECHAM level 95).<br> aprt: total precipitation (mm water equivalent per month)<br> d18O_precip: delta value for <sup>18</sup>O (‰) in the precipitation<br> dD_precip: delta value for H<sub>2</sub> (D) (‰) in the precipitation</p> <p><strong>Data usage notice:</strong></p> <p>If you use<strong> any of these data</strong> you should refer to:</p> <p><em>Cauquoin, A. and Werner, M., 2021. High‐Resolution Nudged Isotope Modeling With ECHAM6‐Wiso: Impacts of Updated Model Physics and ERA5 Reanalysis Data. Journal of Advances in Modeling Earth Systems, </em><a href="https://doi.org/10.1029/2021MS002532">https://doi.org/10.1029/2021MS002532</a></p>
PyFLEXTRKR Global MCS Tracking Dataset using GPM MergedIR Tb and IMERG precipitation data
<p>This is the global mesoscale convective system (MCS) tracking dataset developed by <a href="https://doi.org/10.1029/2020JD034202">Feng et al. (2021) JGR</a>. It contains the MCS track data (location, time, lifecycle evolution of MCS cloud and precipitation characteristics), monthly mean and 20-year climatological MCS statistics on 0.1 degree x 0.1 degree (lat x lon) grid. All data files are in netCDF format.</p><p>The periods are from June 2000 to December 2020. The geographic coverage is 180°W-180°E, 60°S-60°N. For more detailed documentations, please refer to the README "PyFLEXTRKR_MCS_Tracking_Data_Readme.pdf".</p><p>Due to the large file size of the native 1-hourly resolution pixel-level data on the 0.1 degree x 0.1 degree grid, they are not included in this dataset. Please contact Zhe Feng (<a href="mailto:zhe.feng@pnnl.gov">zhe.feng@pnnl.gov</a>) if you are interested in obtaining the pixel-level data.</p>
Lake Sunapee Gloeotrichia echinulata density near-term hindcasts from 2015-2016 and meteorological model driver data, including shortwave radiation and precipitation from 2009-2016
Hindcasts were generated for density of Gloeotrichia echinulata, a toxin-producing cyanobacterium, at a nearshore site (South Herrick Cove) in Lake Sunapee, NH, USA, from May-October in 2015 and 2016 using several different Bayesian state-space models as part of a Global Lake Ecological Observatory Network working group project (Lofton et al. 20XX). Hindcasts were produced for one-week to four-week forecast horizons. Models ranged in complexity from a random walk to dynamic linear models with up to two environmental covariates. A subset of the model meteorological driver data for calibration and hindcasting was downloaded from the North American Land Data Assimilation System (NLDAS-2; https://ldas.gsfc.nasa.gov/nldas/) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://www.prism.oregonstate.edu/) for Lake Sunapee, New Hampshire, USA. The model driver data derived from NLDAS-2 data are daily summaries of solar radiation on G. echinulata sampling days from 2009-2016. The model driver data derived from PRISM data are daily sums of precipitation on G. echinulata sampling days from 2009-2016. All other model driver data are also published on the Environmental Data Initiative repository and are specified in the Notes and Comments of this data publication. All code to import data, calibrate models, and generate and analyze hindcasts are available on Github at https://github.com/GLEON/Bayes_forecast_WG/tree/eco_apps_release.
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.