Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,322
datasets available to search
ShareScore release 0.7.1
Dataset results
2,322 results for “precipitation”
Data for: Ring Current Electron Precipitation During the 17 March 2013 Geomagnetic Storm: Underlying Mechanisms and Their Effect on the Atmosphere
<p>All data are included as MATLAB figure files, png files and MATLAB MAT files.</p><p>File precipitated_flux.mat contains a 4-D array of values of precipitated electron flux in [1/(s cm^2 keV)] for 289 time points from 16 March 2013 to 19 March 2013, with a 15 min time step; 100 values of energy in a range from 10 keV to 1 MeV, with a 10 keV step; on a spatial grid of 28 by 49 (P, R).</p><p>netCDF data can be opened with a variety of software tools, including Matlab, Origin or Python.</p>
Hydrological regime in a model High Arctic catchment (Bratteggdalen, Svalbard) under warming and precipitation rise
<p><span>Climate change is impacting water flow worldwide and is particularly important for High Arctic basins. Thawing permafrost and melting of glaciers, as well as higher air temperatures and precipitation, affect hydrological regimes and retention in polar basins. However, knowledge is limited as regards long-term changes in discharge from catchments in the High Arctic. Our aim was to evaluate the impact of local conditions on hydrological regime in glacial-fluvio-lacustrine model system in the High Arctic. We used mainly hydrological and meteorological data from 9 summer seasons (June-September) between 2005 and 2019 extracted from the entire database (16 seasons in 1972-2019). Wide range of statistical methods was applied including bootstrapping, random forest and multiple regression, to determine the coupling between hydrometeorological parameters (air and water temperature, discharge, sunshine duration, precipitation). The hydrological regime exhibits a distinct seasonal pattern with a pronounced, snowmelt-derived peak (maximum discharge) in the early part of the season (June-July) affected by precipitation. In the late part of the season (August-September), low-intermediate discharge is primarily governed by air temperatures and, only secondarily by precipitation. The hydrometeorological coupling in August-September is stronger that in June-July. The statistically significant increase in air temperature (0.45°C per decade) in August-September during 1979-2018 makes this part of the season important in terms of long-term changes in the permafrost-underlain catchment. Thawing of the permafrost active layer thaw is clearly reflected by air–temperature-dependent low-to-intermediate discharge.</span></p> <p><span>Database consists of following data obtained from long-term discharge analyses: daily discharge data at the gauging station from 1983-2019 (1983-2019</span><span>_Brattegg_River_Discharge_v1.csv</span><span>), daily water stage data from 1972-1983 (1972-1983 </span><span>_ Brattegg_River_Water_Stage_v1.csv</span><span>), daily water level at gauging station and outflow from Bratteggbreen from 2017 (</span><span>2017_Brattegg_River_water_stage_gauging_station_Bratteggbreen_v1.csv</span><span>).</span></p> <p><span>This study is a contribution to the National Science Centre projects: 2021/43/D/ST10/00687 (SONATA17 funding scheme, ŁS), 2020/39/I/ST10/02129 (OPUS-LAP funding scheme, MB), 2017/27/B/ST10/01269 (OPUS funding scheme, KM), and SONATA 2015/19/D/ST10/02869 (SONATA funding scheme, MK). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission. ŁS was also supported from the Bekker Programme (award no. BPN/BEK/2021/1/00431) at the Polish National Agency for Scientific Exchange. The study was carried out by DI, EL as part of scientific activity of the Centre for Polar Studies (University of Silesia in Katowice) with the use of research and logistic equipment (monitoring and measuring equipment, sensors, multiple AWS, GNSS receivers, snowmobiles and other supporting equipment) of the Polar Laboratory of the University of Silesia in Katowice. MW and HM acknowledge the </span><span>statutory fund of University of Wrocław for suport during fieldwork in 2005-2010.</span></p> <p> </p> <p> </p>
Recent Upper Colorado River Streamflow Declines Driven by Loss of Spring Precipitation
<div> <div> <p>The dataset accompanying the manuscript titled "Recent Upper Colorado River Streamflow Declines Driven by Loss of Spring Precipitation" provides comprehensive information on streamflow patterns in the Colorado River since 2000. The dataset is needed to run the analysis available on GitHub available <a href="https://github.com/dlhogan97/Spring-Precipitation-Effect-CO-River.git">here</a>. This is version 2, please use this version for the most up-to-date results.</p> <p><strong>Please read the accompanying README (available in the README.md file) for individual file descriptions and file nesting strategy that should be employed to easily reproduce this analysis.</strong></p> <p>The dataset covers a range of variables related to streamflow and precipitation, including but not limited to discharge measurements, seasonal variations, and relevant meteorological data. The primary focus of the dataset is to elucidate the observed streamflow deficits in the Colorado River, attributing these changes to decreased spring precipitation.</p> <p>Key features of the dataset include:</p> <ul> <li> <p>Time Coverage: The dataset spans a specified time range that aligns with the investigation into recent streamflow deficits in the Colorado River between 1964 and 2022.</p> </li> <li> <p>Spatial Scope: It includes data from relevant monitoring stations along within the Upper Colorado River, but focusing in the hydrologically vital headwater regions, providing a spatially distributed perspective.</p> </li> <li> <p>Variables: The dataset encompasses a variety of variables essential for understanding streamflow dynamics, with a particular emphasis on the impact of reduced spring precipitation.</p> </li> </ul> <p>Researchers and stakeholders interested in hydrological patterns, climate-driven changes, and water resource management in the Colorado River Basin will find this dataset valuable. It serves as a foundational resource for reproducibility, further analysis, and collaboration within the scientific community. The dataset is deposited on Zenodo to facilitate open access, sharing, and citation for broader research endeavors.</p> </div> </div>
TROPRAIN: Tropical Precipitation anomalies
<p>The TROPRAIN dataset is a product containing the daily rainfall anomalies across the entire tropical region (180°W - 180°E/30°S - 30°N). These are calculated from the GOES, GPCP, CPC and TRMM data sets. The data is in NetCDF format and is 3D grids. The objective of this data set is to facilitate researchers to study rainfall anomalies during the occurrence of short and medium duration physical processes.</p>
Data for paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts"
<p>The forecasts and observation datasets are used in the paper "Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts". https://doi.org/10.1016/j.jhydrol.2021.127301</p> <p>The forecast data is a subset of the "ensemble for machine learning dataset (ENS4ML)" from ECMWF. </p> <p>The Python codes are stored in Github: https://github.com/wentao-bnu/LeNet_CSG_Precip</p>
Seasonal hindcast of temperature and precipitation at a local scale by using TeWA approach
<p><strong>Methodology</strong></p> <p>Data set of simulated time-series of temperature and precipitation for the 1982-2020 period. Our statistical seasonal prediction model have two main components: a) the ocean-atmosphere coupling represented by correlations between surface variables with delayed teleconnections and b) the self-predictability of the residual anomalies by trends or cycles (quasi-oscillations).</p> <p>The approach has three stages approach with two main predictor components, as mentioned above. The first two stages consist of separate predictions, one per each component, and the third stage is a combination of both predictions (Fig. 2): Teleconnection-based approach (Redolat et al. 2019, 2020) and a self-predictability by using Wavelet-ARIMA models (Conejo et al. 2005; Joo and Kim 2015). Therefore, the total method is a Teleconnection+Wavelet+ ARIMA (TeWA) approach.</p> <p><strong>References</strong></p> <p>Conejo, A.J., M.A. Plazas, R. Espinola, A.B. Molina, 2005: Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans. Power Syst., 20, 1035-1042, https://doi.org/10.1109/TPWRS.2005.846054.</p> <p>Joo, T., S. Kim, 2015: Time series forecasting based on wavelet filtering. Expert Syst. Appl. 42, 3868-3874. https://doi.org/10.1016/j.eswa.2015.01.026</p> <p>Redolat, D., R. Monjo, C. Paradinas, J. Pórtoles, E. Gaitán, C. Prado-López, and J. Ribalaygua, 2020: Local decadal prediction according to statistical/dynamical approaches. Int. J. Climatol., 40: 5671–5687. https://doi.org/10.1002/joc.6543.</p> <p>Redolat, D.; R. Monjo, J.A. Lopez-Bustins, and J. Martin-Vide, 2019: Upper-Level Mediterranean Oscillation index and seasonal variability of rainfall and temperature. Theor. Appl. Climatol., 135: 1059–1077. https://doi.org/10.1007/s00704-018-2424-6.</p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2020 in China
<p>In this dataset, the MODIS vegetation index and land surface temperature products are processed into NDVI and LST monthly time series with a spatial resolution of 1 km, and the final precipitation data of GPM IMERG are downscaled, unified at a spatial resolution of 1 km. And after a standardization process, using the spatial distance model, a remote sensing drought monitoring dataset in China from 2001 to 2020 was produced based on the Temperature Vegetation Precipitation Dryness Index. For the specific construction process of this data, please refer to https://linkinghub.elsevier.com/retrieve/pii/S0034425720303278</p>
2010_2023_ERA5_Precipitation_Daily_Dekadal_Monthly_Annual_5k_ER
<p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre of Medium Range Weather Forecasting for 2010 - 2023.</p> <p>Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting . for 2010 - 2023 . The original data is at 0.25 degree resolution and was downloaded and scaled by ERA extraction algroithms. The daily data have been aggregated into dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <p>This dataset was windowed for E4warning project to Europe and North Africa. </p>
Parameters for PITRI Precipitation Temporal Disaggregation over continental US, Mexico, and southern Canada, 1981-2013
<p>This dataset contains parameter values for the Precipitation Isosceles Triangle (PITRI) precipitation disaggregation method (Bohn et al., 2019) over the CONUS+Mexico domain (southern Canada, the continental US, and Mexico; 14.65 - 53° N latitude, 65-125° W longitude), at 1/16° (6 km) spatial resolution. There are two parameters: "dur" (mean event duration [minutes]) and "t_pk" (mean time of peak precipitation intensity [minutes from beginning of day]). In each land grid cell, each parameter has 12 climatological mean monthly values for the period 1981-2013.</p> <p>This dataset contains 2 NetCDF-format files:</p> <ul> <li>domain.CONUS_MX.L2015.nc - this contains parameters over the entire CONUS+Mexico domain, using the land mask of the Livneh et al. (2015) daily meteorology dataset.</li> <li>domain.USMX.L2015.nc - this contains the same parameters, but clipped to exclude Canada (to be consistent with datasets that cover only that part of the domain).</li> </ul> <p>These files are structured as input "domain" files for 2 applications:</p> <ul> <li>MetSim meteorology simulator (https://github.com/UW-Hydro/MetSim/releases/tag/2.0.0_alpha; Bennett et al., 2018). The PITRI algorithm has been implemented as an option in MetSim. To use this algorithm within MetSim, set the "prec_type" option to "triangle" or "mix" in the configuration file. The "mix" option is a blend of the "uniform" (previous) method and the "triangle" method that fixes biases in snow accumulation rates yielded by the "triangle" method in some climates. "mix" uses the "uniform" method on days for which minimum daily temperature falls below 0 C, and "triangle" method on all other days.</li> <li>Variable Infiltration Capacity (VIC) model, release 5.0 and later (Liang et al., 1994; Hamman et al., 2018; https://github.com/UW-Hydro/VIC). VIC does not use the PITRI parameters, but does use the other variables such as mask, elevation, area, etc. For VIC to use the output of MetSim (disaggregated meteorological fields) as input, VIC needs to use the same domain file as was used in MetSim.</li> </ul> <p>Algorithm details can be found in the following paper, which should be cited if you use this dataset:</p> <p>Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019: A deterministic approach for approximating the diurnal cycle of precipitation for use in large-scale hydrological modeling, Journal of Hydrometeorology 20(2), 297-317, doi: 10.1175/JHM-D-18-0203.1.</p>
Concentration of daily precipitation in the contiguous United States
<p>The contiguous US exhibits a wide variety of precipitation regimes, first, because of the wide range of latitudes and <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/altitude">altitudes</a>. The physiographic units with a basic meridional configuration contribute to the differentiation between east and west in the country while generating some large <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/continental-interior">interior continental</a> spaces. The frequency distribution of daily precipitation amounts almost anywhere conforms to a negative exponential distribution, reflecting the fact that there are many small daily totals and few large ones. Positive exponential curves, which plot the cumulative percentages of days with precipitation against the cumulative percentage of the rainfall amounts that they contribute, can be evaluated through the Concentration Index. The Concentration Index has been applied to the contiguous United States using a gridded climate dataset of daily precipitation data, at a resolution of 0.25°, provided by CPC/NOAA/OAR/Earth System Research Laboratory, for the period between 1956 and 2006. At the same time, other rainfall indices and variables such as the annual coefficient of variation, seasonal rainfall regimes and the probabilities of a day with precipitation have been presented with a view to explaining spatial CI patterns. The spatial distribution of the CI in the contiguous United States is geographically consistent, reflecting the principal physiographic and climatic units of the country. Likewise, linear correlations have been established between the CI and geographical factors such as latitude, <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/longitude">longitude</a> and altitude. In the latter case the Pearson <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/correlation-coefficient">correlation coefficient</a> (r) between this factor and the CI is −0.51 (<em>p</em>-value < 0.001). For annual probability of days with precipitation and the CI there is also a significant and negative correlation, <em>r</em> = −0.25 (<em>p</em>-value < 0.001).</p> <p> </p> <p>Fig. 8. Concentration Index values (1956–2006).</p> <p>File: ci_raster_USA.tif (geoTIFF)</p> <p>NOTE: After the publication of the research article we calculate the Concentration Index with the <a href="http://www.prism.oregonstate.edu/">PRISM</a> climate data set, which has a higher resolution with 4km (PRISM Climate Group, Oregon State University). Nevertheless, the temporal coverage is limited to the period from 1981 to 2017.</p> <p>File: CI_PRISM_USA.tif (geoTIFF)</p> <p> </p> <p>Fig. 4. Seasonal rainfall regimes (1956–2006) (P, spring, S, summer, A, autumn, W, winter)</p> <p>File: 1) pulvio_regimes_raster_USA.tif (geoTIFF); 2) pulvio_regimes_id.csv (clasification for regimes)</p> <pre>Map projection details:</pre> <p>EPSG:2163; proj4: "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"</p>
EURO-CORDEX Precipitation Intensity-Duration-Frequency Curves
<p>This dataset provides preliminary precipitation intensity-duration-frequency (IDF) values calculated from the <a href="https://cds.climate.copernicus.eu/datasets/projections-cordex-domains-single-levels">CMIP5 based EURO-CORDEX regional climate model simulations</a>. Values for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece are provided for a historical and future period for the RCP 2.6, 4.5 and RCP 8.5 atmospheric greenhouse gas concentration scenarios for a total of 123 simulations.</p> <p>Technical Info</p> <p>The return period values are computed for exceedance periods of 2, 5, 10, 25, 50, 100, 200, and 500-years and durations of 3, 6, 12, and 24-hours using the generalized extreme value (GEV) distribution with the <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.genextreme.html">scipy.stats.genextreme </a>function from the Python SciPy library. Computations are performed from the output of a combination of 123 regional climate models (RCMs), global climate models (GCMs), climate change scenarios, and ensemble members. The historical data cover the period 1976 through 2005 and the future 2041 through 2070 for the RCP 2.6, 4.5 and 8.5 atmospheric greenhouse gas concentration scenarios. Included in the analysis, there are 39 unique RCM-GCM combinations with outputs from 48 historical, 15 RCP 2.6, 13 RCP 4.5, and 47 RCP 8.5 simulations.</p> <p>Data description: This dataset contains EURO CORDEX return period estimates computed for exceedance periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-years and 3-, 6-, 12-, and 24-hour durations using the generalized extreme value (GEV) distribution. The data includes values based on outputs from a total of 123 simulation combinations, including 39 RCM–GCM configurations, three climate change scenario combinations for a historical period (1976-2005) and future period (2041-2070), and three ensemble members.</p> <p>Format:</p> <p>The format of this dataset is organized in one ZIP files: IDF_{Cityname}.zip. The zip file contains 48 csv files for each RCM-GCM combination and ensemble member that include IDFs for each available RCP scenario.</p>
Remote Sensing Drought Monitoring Dataset based Temperature Vegetation Precipitation Dryness Index (TVPDI) from 2001 to 2021 in China (v2.0)
<p>The Enhanced Vegetation Index (EVI), Land Surface Temperature (LST) and Precipitation (P) were used as new data sources based on the spatial distance model to construct an optimized multi-source remote sensing dryness index named Temperature-Vegetation-Precipitation Dryness Index based on the shortcomings of the TVPDIorigin (i.e., TVPDI<sub>o</sub>) data source. The TVPDI<sub>n</sub> of the long time series was also compared and analyzed with the classical drought index - Standardized Precipitation Evapotranspiration Index (SPEI-3) on a 3-month scale, different drought response level products of Solar-Induced Chlorophyll Fluorescence (SIF), soil moisture (SM) from ESA CCI (European Space Agency's Climate Change Initiative), and total crop yield, then the sensitivity and validity of the TVPDI<sub>n</sub> for wetness and dryness monitoring were synthesized and validated. On this basis, here are the results of the verification:</p> <p>(1) Compared with the original data source TVPDI<sub>o</sub> using the new multi-source remote sensing data source of precipitation and vegetation index to construct TVPDI<sub>n</sub>, the overall correlation between the two and SPEI-3 was good, with a maximum of 0.57 and 0.56, respectively (p< 0.1), but the overall TVPDI<sub>n</sub> constructed in this study had a better fit compared to the original data source TVPDI<sub>o</sub> and was more sensitive to the monitoring of dry and wet conditions.</p> <p>(2) According to the comparison of TVPDI<sub>n</sub> with ESA CCI sm, TVPDI<sub>n</sub> showed a high correlation of more than 0.9 with soil water content, which proved that TVPDI<sub>n</sub> was highly consistent with soil moisture; compared with SIF, 54.5% of the regional correlation coefficients were greater than 0.8 (p< 0.01), and spatially, the correlation results were better in the northwest than in the east, indicating that the response of TVPDI<sub>n</sub> to vegetation productivity is more agile in regions with continental climate such as the northwest. The results of correlation with grain yield comparison showed that good positive correlations were presented with TVPDI<sub>n</sub> in Liaodong Peninsula, northern North China Plain, and most of Qilian Mountains, southern edge of Qinling Mountains, middle and lower reaches of Yangtze River, and South China, indicating that TVPDI<sub>n</sub> has a high consistency in the changes of agricultural grain production in the above mentioned regions, and also proving the index in monitoring agricultural aridity and guiding agricultural production The good performance of the index in monitoring agricultural aridity and guiding agricultural production.</p> <p> This dataset is version 2.0, and covers all of China's territory, but the temperature-vegetation- precipitation dryness index of the open water surface are often set to a null value. Note:The data format is "TIF", the spatial resolution is "1 km", the time resolution is "1 month" and dimensionless. The pixel value is the NTVPDI value, and the closer the pixel value is to 0, the drier it is, and the larger the data, the wetter the land surface. The practical utility of this dataset is to compare the degree of dryness and wetness of China's land, to monitor short-term and medium-term droughts, and to substitute model parameters related to soil moisture. This is of great value to the impartial formulation of China's environmental and economic policies, regular monitoring and evaluation of drought and flood conditions. This product will be freely available to all users worldwide and will be continuously improved to suit new goals and needs.</p>
Silica dissolution and precipitation kinetics in hot geothermal conditions
<p>This dataset report quartz dissolution kinetics as obtained from packed column experiments at different flow rates. Variables were temperature, pressure and NaCl content as incicated in the table. Silica values are reported as mg/L of Si as measured by ICP-OES. Also included in the table is a column describing how data series were treated to extract steady-state values for each flow rate (cf. the report to which the current dataset is related). The column "solubility used" states the solubility used to calculate dissolution (k<sub>+</sub>) and precipitation (k<sub>-</sub>) rate constants along with a column "source" which briefly indicates how this value was obtained. Further details are given in the report.</p> <p>Factors used to get from the raw data to the reported rate constants are also given. Not included in the table, but common for all data points are a quartz BET surface are of 0.6922 m<sup>2</sup>/g, 10 g quartz and a quartz activity assumed to be 1.</p> <p>Note that this dataset contain several measurement points that are not representative. These include points close do equilibrium where kinetic information cannot be reliably obtained and points where it is suspected that a temperature drop during sampling may have caused erroneous results (the Si content actually represents a somewhat lower temperature that was not measured). The reader is referred to the full report for details.</p>
Simulations of Miocene Antarctic ice-sheet variability under increased precipitation and sub-shelf melt, using the ice-sheet model IMAU-ICE
<p>To demonstrate the viability of a precipitation regime change leading to a fundamentally different volume-to-area ratio of the Antarctic ice sheet, we deploy the 3D thermodynamical ice sheet/shelf model IMAU-ICE v1.1.1. In the standard set-up (<a href="https://doi.org/10.5194/cp-2023-12">Stap et al., 2021a</a>, <a href="https://doi.pangaea.de/10.1594/PANGAEA.939114">2021b</a>), climate forcing follows from pre-run warm and cold snapshot climate simulations. The applied climate forcing is transiently calculated based on the prescribed CO<sub>2</sub> concentration and the modelled ice sheet size, through a matrix interpolation method. Equilibrium experiments are performed at various CO<sub>2</sub> levels between preindustrial and 3x preindustrial CO<sub>2</sub> values, with insolation at present-day levels and initiated from an ice-free Miocene Antarctic topography (dataset <a href="https://doi.pangaea.de/10.1594/PANGAEA.923109">Hochmuth et al., 2020</a>). Here, we perform additional sensitivity experiments, in which we apply a fixed precipitation increase and extreme sub-shelf melt rates. The precipitation anomaly is calculated as 25% of the warm snapshot precipitation fields, sub-shelf melt rates are set to 400 m/yr.</p> <p> </p>
2010_2024_ERA5_Precipitation_Rainfall_FourierProcessed_1k_ER
<p>This is a set of images produced by Temporal Fourier Analysis (TFA) of ERA5 data:</p> <p>ERA5: Total Precipitation </p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2010 to 2024. This version is an update to the previous one (2010 to 2022)</p> <p> </p> <p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting for 2010 - 2024.</p> <p>Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium-Range Weather Forecasting . The original data is at a 0.25-degree resolution and was downscaled by ERA extraction algorithms, then downloaded at a 1 km resolution. The daily data have been aggregated into dekadal, monthly, and annual datasets to match the outputs produced by NASA from the MODIS imagery temperature and vegetation Index datasets. The resolution was also chosen to match these MODIS datasets.</p> <h4>Process:</h4> <p>Image values were extracted from ERA5 (Total precipitation) 1 km imagery from 2010 to 2024. Each parameter extract dataset was then processed by a Temporal Fourier Processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the mean, minimum, and maximum of the time series, and errors measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>) <br>Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility. Then, sea pixels were masked with a VIIRS land/sea layer in arcmap. The E4Warning study region was a subset of global images. </p> <p> </p> <p>This new ERA5 Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p>Projection + EPSG code:</p> <p>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716</p> <h4>File names:</h4> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 04 refers to the year timeline of 2010-2024.<br><br>The next two characters identify the channel:<br>20 Monthly Total Precipitation<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>ERA5 A0, A1, A2, A3, Min, Max, Vr Reflectance values monthly total precipitation in mm<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p>
Data Source: Synergistic effects of precipitation and groundwater extraction on freshwater wetland inundation
Wetlands provide essential ecosystem services, including nutrient cycling, flood protection, and biodiversity support, that are sensitive to changes in wetland hydrology. Wetland hydrological inputs come from precipitation, groundwater discharge, and surface run-off. Changes to these inputs via climate variation, groundwater extraction, and land development may alter the timing and magnitude of wetland inundation. Data were compiled for 152 wetlands in west-central Florida over 14 years to investigate the response of wetland inundation to the interactive effects of precipitation, groundwater extraction, surrounding land development, basin geomorphology, and wetland vegetation class. Further methods are defined in the Methods section of the journal article associated with this dataset (Synergistic effects of precipitation and groundwater extraction on freshwater wetland inundation, published in the Journal of Environmental Management, 2023).
Precipitation and dry deposition chemistry concentrations and fluxes, Andrews Experimental Forest, 1969 to present
Collection and analyses of precipitation chemistry were initiated in 1969 at the low-elevation Primary Met site, and in 1973 at a mid-elevation Hi-15 site. Rain collection samples accumulate from one week to three weeks in bulk and NADP type collectors and then are transported to Cooperative Chemical Analytical Laboratory (CCAL) for analysis. Analytes include nitrogen, phosphorus, carbon, and cations and anions as well as pH, conductivity, alkalinity and particulate sediment. Concentration and volume of precipitation are combined for inflow. Dry deposition chemistry concentrations began in 1989 and are analyzed 2-4 times per year at one site. The original objectives were to evaluate precipitation chemistry inputs versus chemistry outputs in streamflow from forested watersheds. The study has evolved into a general monitoring effort for precipitation chemistry that is among the least contaminated of any within the USA. This study is conducted in conjunction with Andrews streamflow chemistry (CF002) and the U.S. National Atmospheric Deposition Program (NADP).
Dryland soil mycobiome response to long-term precipitation variability at the Jornada Basin LTER site, 2013-2019
This dataset contains data and code for the paper "Dryland soil mycobiome response to long-term precipitation variability depends on host type" published in Journal of Ecology in 2022. Data were collected at the Jornada Basin LTER site in southern New Mexico, USA. Soil samples were sent to the University of Georgia, for DNA extraction and sequencing, performed at the Georgia Genomics and Bioinformatics Core in Athens, GA. Sequencing data are archived at NCBI under project identifier PRJNA884111.
Perennial grass tiller and stolon counts in plots with experimentally altered precipitation variability at the Jornada Basin LTER site, 2012-2014
This dataset contains perennial grass tiller and stolon counts collected starting in 2012 for a long-term precipitation variability manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. The study was designed to assess the effect of interannual variability in precipitation on average aboveground net primary productivity (ANPP) in Chihuahuan Desert grasslands. The study began in 2009, has five annual precipitation treatments, and contains 50 plots (10 per treatment). This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs to 2.5 x 2.5 meter plots in a desert grassland. There are high, low, and ambient (control) precipitation variability treatments. Ambient plots receive natural precipitation each year, while variability treatments alternate between 20% and 180% (high variability), or 50% and 150% (low variability) of ambient precipitation each year. Perennial grass tiller and stolon counts were made annually in each plot from 2012-2014. This is an ongoing study and the dataset will be updated as needed.
Perennial grass tiller and stolon density in plots with experimentally altered precipitation and nutrient inputs at the Jornada Basin LTER site, 2012-2014
This dataset contains perennial grass tiller and stolon counts collected starting in 2012 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Tillers and stolons of perennial grasses were counted in each plot in 2012, 2013 and 2014. This is an ongoing study and the dataset will be updated as needed.
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.