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 0.9.0
Dataset results
2,322 results for “precipitations”
Ring Width Index (RWI) and Standard Precipitation Index (SPI) data for Atmospheric River reconstruction
<p>These are the two datasets used for reconstructing atmospheric river activity along the U.S. west coast (under review at the Journal of Geophysical Research: Atmosphere). The first file rwi.mat contains the ring-width-index for the chronologies used to develop the second dataset (SPI_recon.nc). This file is used as the covariate to develop the AR reconstructions (with Poisson regression) and goes back to 1400 CE. </p>
Data for Simulating the seeder-feeder impacts on cloud ice and precipitation over the Alps
<p>The ice phase impacts many important cloud properties and the lifetime of the clouds. Ice particles that sediment into a lower cloud from an upper cloud (external seeder-feeder process) or into the mixed-phase region of a deep cloud from cirrus levels (internal seeder-feeder) can amplify cloud glaciation and enhance surface precipitation. Recently, numerical weather prediction modeling studies have aimed at representing the ice crystal number concentration in mixed-phase clouds more accurately by including secondary ice formation processes. The increase in the ice crystal number concentration can impact the number of ice particles that sediment into the lower cloud and alter its composition and precipitation formation. <br> In the Swiss Alps, the orography permits the formation of orographic clouds, making it ideal for studying the occurrence of multi-layered clouds and the seeder-feeder process. We present results from a case study on May 18, 2016, showing the occurrence frequency of multi-layered clouds and the seeder-feeder process. We included ice-graupel breakup to enhance the ice crystal number concentration and investigate the precipitation formation processes.<br> 47.6\% of all observed clouds were categorized as multi-layered, in which the external seeder-feeder process occurred in 10.3\% of these clouds which is similar to what was found in several studies. In between cloud layers, 58.4\% of the ice particle mass was lost due to sublimation or melting. The external seeder-feeder process was found to be more important, with regard to the impact on precipitation, than the internal seeder-feeder process in this case study. In the case where the external seeder-feeder process was blocked, the average surface precipitation and riming rate over the domain were both reduced by 8.5\% and 3.9\%, respectively. When ice-graupel collisions were allowed, further large reductions were seen in the liquid water fraction and riming rate. Blocking of the internal seeder-feeder process enhanced the liquid water fraction of 6\% compared to a reduction of 5.8\% in the cloud condensate and, therefore, pointing towards the deamplification in cloud glaciation and a reduction in surface precipitation.</p>
Precipitation data for paper submitted to WRR
<p>This table includes precipitation indices calculated for regional flood statistical modelling.</p>
ARISE-SAI_1.5: UKESM1 Extreme Temperature and Precipitation Indices
<p>The UKESM1 ARISE experiment explores the impacts of geoengineering via the injection of sulphur dioxide (SO2) into the stratosphere. The injections occur at four different latitudes: 15 degrees N & S and 30 degrees N & S, at an altitude of approximately 20 km.</p> <p>The simulations are based on the medium-emissions CMIP6 scenario ssp245 and cover the years 2035 to 2070. They form a 5-member ensemble with initial conditions taken from the corresponding five members of the ssp245 simulations upon which they were based. Data for the UKESM1 ARISE-SAI-1.5 simulations is available at https://data.ceda.ac.uk/badc/deposited2022/arise and the data for the UKESM1 SSP2-4.5 simulations is available on the Earth System Grid Federation database.</p> <p>The files included here contain processed annual daily extremes of surface temperature (TREFHT) and total precipitation (PRECT) from the ARISE-SAI-1.5 simulations and companion SSP245 simulations. Indices are those recommended by the WCRP Expert Team on Climate Change Detection Indices, Zhang et al. 2011). Methods to calculate the indices are also described in Tye et al. (2022).</p> <p><strong>Precipitation Indices</strong></p> <p>PRCPTOT, SDII, RX1D, RX5D, R10mm, R20mm, CDD, CWD, P95TOT, P99TOT</p> <p><strong>Temperature Indices</strong></p> <p>TNN, TNX, FD, TR, TN90, TN10, TXX, TXN, ID, SU, TX90, TX10</p>
Divergent responses of grassland productivity and plant diversity to intra-annual precipitation variability across climate regions: A global synthesis
<p><span>Global warming intensifies the hydrological cycle and may result in changes in the frequency and intensity of precipitation events. Although the effects of changes in precipitation amount and inter-annual precipitation variability on terrestrial plant productivity and carbon sequestration have been well studied, how intra-annual precipitation variability affects terrestrial ecosystem function remains unclear. </span><span>Here, we synthesized field manipulative experiments from 71 publications to quantify the effects of intra-annual precipitation variability increases (IPVI) on community biomass and plant diversity in grasslands worldwide. </span><span>At the global scale, we found that IPVI generally increased grassland community aboveground biomass (AGB) by 6%, and decreased grass biomass and soil ammonium nitrogen by 12% and 31%, respectively. IPVI stimulated AGB, belowground biomass, and plant species richness in arid regions, but not changed them in humid regions. Changes in AGB under IPVI were related to changes in the biomass of plant functional groups, species richness, and soil moisture. Structural equation modelling demonstrated that that climate conditions (mean annual temperature and mean annual precipitation) and background soil properties (soil sand content and soil organic carbon content) jointly regulated grassland AGB responses to IPVI across climate types.</span></p> <p><span>Synthesis: Overall, our study shows that grassland productivity and diversity may increase under IPVI in arid climates, and that humid grasslands may be highly resistant to the effects of IPVI. These findings have important implications for understanding ecosystem carbon cycling under global precipitation change scenarios.</span></p>
KScale SE Asia LAM precipitation data (2.2 km resolution)
<p>2.2 km resolution SE Asia LAM precipitation data produced as part of the K-Scale project at the UK Met Office. The model is initialised at 20171230T0600Z and run for a 12 day integration period (T+0 to T+288).</p>
An Integrated Precipitation Product for the Arid and Semi-arid Regions of China
<p>We integrate various reanalysis, meteorological observation, and satellite precipitation datasets, including CRU (Climate Research Unit), MERRA2 (Modern-Eta Retrospective Analysis for Research and Applications version2), CMAP (Climate Prediction Center (CPC) integrated analysis of monthly precipitation), GPCP (Global Precipitation Climatology Project) and GPM (Global Precipitation Measurement), to create a new monthly precipitation product with a spatial resolution approximately 0.2° × 0.2° (latitude × longitude) during the period from 1980 to 2019 in ASRC. The new product was validated with observations at different temporal scales (e.g., monthly to yearly variability and trends). The results show that the new product improves the accuracy of precipitation with the lower bias of -1.1 mm month<sup>-1</sup>. The annual trend of integrated precipitation is 0.09 mm/year which is consistent with the trend of observation. </p>
POES/MetOp Daily-Averaged Precipitating Electron Fluxes
<p>Daily-averaged precipitating electron fluxes as a function of energy and L-shell from the MPE data set by Pettit et al., 2021.</p>
A Standardized Precipitation Index (SPI) Climatology for the Continental U.S.
<p>The standardized precipitation index SPI (McKee et al. 1993) was designed to standardize precipitation timeseries across an observational record in order to compute precipitation anomalies in both time and space. The SPI is a widely used drought index that represents the number of standard deviations that observed precipitation deviates from the climatological average precipitation measured at the same location and time period. We calculated the SPI using monthly 4 kilometer resolution precipitation data from the Parameter-elevation with independent slopes model (PRISM; Daly et al. 1994) for the conterminous United States. Monthly PRISM data from 1948-2022 were aggregated to warm season (May-September) and annual (calendar year) totals. SPI anomalies were calculated for each seasonal time window relative to the 1948-2022 timeperiod.</p> <p>Each timeseries of annual precipitation totals was fit to a Gamma probability distribution based on the L-moments of the data. We computed the cumulative distribution function (CDF) associated with the observations using the parameters from the aforementioned Gamma distribution. The CDF values were then evaluated within an inverse Gaussian function with a mean of zero and a standard deviation of one to obtain the final SPI value</p> <p>Coordinate Reference System (CRS) = EPSG:4326</p> <p>McKee, T.B., N.J. Doesken and J. Kleist, 1993: The relationship of drought frequency and duration to time scale. In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California,17–22 January 1993. Boston, American Meteorological Society, 179–184.</p> <p>Daly, C., R.P. Neilson, and D.L. Phillips. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140-158.</p>
A Deep Learning Approach to Quantitative Precipitation Estimation Mainly Using Weather Radar Data: Taking the Plum Rain Season in Taiwan as an Example
<p>The following partial dataset is provided by the authors for the Quantitative Precipitation Estimation method presented in the paper.</p> <p>wissdom_out_Taiwan_mosaic_202105010000.bin is wind data, the number means year, data, and time.</p> <p>CAPPI_COMP_DR_202005100000.bin is radar data.</p> <p>qpepre_202005010000-202005010100_1_h.txt is rain data.</p> <p> </p> <p> </p>
Precipitation nowcasting verification dataset
<p>Verification files for the Verif program. See https://github.com/WFRT/verif for details.</p>
Precipitation short-range forecasting verification dataset
<p>Verification files for the Verif program. See https://github.com/WFRT/verif for details.</p>
Precipitation, rather than temperature drives coordination of multidimensional root traits with ectomycorrhizal fungi in alpine coniferous forests
<ol> <li><span>The interactions between roots and mycorrhizal fungi are critical for our understanding of the multidimensional root economics space. Our knowledge on their relationships comes mainly from arbuscular mycorrhizal (AM) plants, and less is known about how roots are coordinated with ectomycorrhizal (ECM) fungal communities, especially in ECM-dominated alpine forests that are highly sensitive to climate change worldwide.</span></li> <li> <span>Here, we investigated the coordination between roots and ECM fungi and their drivers by measuring </span><span>multiple</span><span> root traits, ECM fungal </span><span>composition and environmental factors of 47 coniferous populations across the alpine coniferous forests </span><span>on the Tibetan Plateau.</span> </li> <li> <span>Our results reveal two independent fine-root trait dimensions, i.e., root foraging dimension and root uptake dimension, which are represented by</span> <span>root diameter-specific root length, root tissue density-root N concentration. Importantly, the hyphal exploration-type-based ECM foraging correlated significantly with both root foraging and root uptake dimension. Further, in the low-temperature plateau, it is precipitation-induced changes in soil moisture, soil nutrients and pH that drive the proportion of </span><span>longer-</span><span>distance hyphal exploration types to increase with </span><span>higher root </span><span>foraging </span><span>by higher </span><span>specific root length, and </span><span>to </span><span>decrease with </span><span>higher </span><span>uptake </span><span>by higher </span><span>root N concentration.</span> </li> <li> <span><em>Synthesis</em>.</span><span> The coordination of multidimensional root traits with ECM fungi differs greatly from the well-recognized pattern in AM plants that mycorrhizal fungi connect predominantly with root foraging and that roots and mycorrhizal fungi are temperature sensitive. These findings provide a new insight for our holistic understanding of how roots and mycorrhizal fungi vary collaboratively and hence driving plant community assembly and responses to the changing climate.</span> </li> </ol>
Data for Western U.S. Lightning-Fire-Precipitation paper
<p>This repository contains supporting data for the manuscript titled "<strong>Lightning-ignited wildfires in the western United States: ignition precipitation and associated environmental conditions</strong> " by Kalashnikov et al. (2023). Please contact me with any questions at dmitri.kalashnikov@wsu.edu. -Dmitri</p>
Stable isotopes in precipitation (δ18O and δD) from Yadong and Pali, central Himalayas
<p><strong>Metadata</strong></p> <p>The isotopic compositions of daily precipitation were measured at two stations, Yadong and Pali, in the central Himalayas. The precision for δ<sup>18</sup>O and δD is ±0.1‰ and ±0.4‰, respectively. The meteorological data, including the collection of precipitation amounts and surface air temperature, was obtained by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences, China.</p> <p><strong>Stations</strong></p> <table> <tbody> <tr> <td><strong>Station</strong></td> <td><strong>Latitude</strong></td> <td><strong>Longitude</strong></td> <td><strong>Altitude (m.a.s.l.)</strong></td> <td><strong>Sampling period</strong></td> <td><strong>Samples (n)</strong></td> </tr> <tr> <td>Yadong</td> <td>27° 29’ 40” N</td> <td>88° 55’ 01” E</td> <td>2945</td> <td>2014-03-13 - 2015-07-18</td> <td>125</td> </tr> <tr> <td>Pali</td> <td>27° 43’ 16” N</td> <td>89° 09’ 08” E</td> <td>4300</td> <td>2014-03-18 - 2015-07-23</td> <td>130</td> </tr> </tbody> </table> <p><strong>Variable definitions</strong></p> <table> <tbody> <tr> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Equipment</strong></td> <td><strong>Additional description</strong></td> </tr> <tr> <td>Date time</td> <td>−</td> <td>−</td> <td>−</td> </tr> <tr> <td>Air temperature</td> <td>Celsius (°C)</td> <td>−</td> <td>−</td> </tr> <tr> <td>Precipitation</td> <td>mm</td> <td>−</td> <td>−</td> </tr> <tr> <td>Water vapour isotopic ratio (δ<sup>18</sup>O)</td> <td>per mil</td> <td>Picarro-2130i Liquid Water Isotope Analyzer</td> <td>Normalized to V-SMOW</td> </tr> <tr> <td>Water vapour isotopic ratio (δD)</td> <td>per mil</td> <td>Picarro-2130i Liquid Water Isotope Analyzer</td> <td>Normalized to V-SMOW</td> </tr> <tr> <td>Deuterium excess (d-excess)</td> <td>per mil</td> <td> </td> <td>d-excess=δD-8*δ<sup>18</sup>O</td> </tr> </tbody> </table> <p><strong>Reference paper</strong></p> <p>Axelsson, J., Gao, J., Eckhardt, S., Cassiani, M., Chen, D. & Zhang, Q. (2023). A precipitation isotopic response in 2014-2015 to moisture transport changes in the central Himalayas. <em>Journal of Geophysical Research: Atmospheres</em></p>
Data for "Multiple Equilibria and Soil Moisture-Precipitation Feedbacks in Idealized Convection-Permitting Simulations with an Open Hydrological Cycle"
<p>Code, simulation input files, and simulation output data supporting “Multiple Equilibria and Soil Moisture-Precipitation Feedbacks in Idealized Convection-Permitting Simulations with an Open Hydrological Cycle”, under review at JAMES. Enclosed README files provide detailed descriptions of the archive contents.</p>
Data for 'Rethinking variability in bedrock rivers: sensitivity of hillslope sediment supply to precipitation events modulates bedrock incision during floods'
<p>This is the repository for model code, sample data, and plotting scripts for the OTTER model for Python.</p> <p>The file OTTERPy.py contains the actual model code</p> <p>The file OTTERPlottingFuncs.py contains a few scripts for plotting model results.</p> <p>The four sample data files in the repository are results from two long (1.5million model year) runs at different rock uplift rates which took several days to run on the IU Bloomington Quartz supercomputing cluster. This data should be viewed and plotted using the OTTERPlottingFuncs.py script. See that code for information on how to select which model to plot.</p> <p> </p> <pre><code>Glossary of variables - this is pretty close to exhaustive, there are a probably a couple of throwaway variables not listed here. a: Sternberg's law multiplier Ah: drainage area from Hack's Law (m^2) beta: fraction of sediment transported as bedload from 0 to 1 D: grain size at each node (m) dA: change in drainage area from node to node (m^2) depth_thresh: sediment depth at which bedrock erosion can occur Do: initial grain size Do: grain size at headwaters (m) dQs: change in sediment supply from node to node dt: time step (years) dx: space step (m) dz_b: bedrock erosion at each node dz_s: change in sed depth dz_b_t: bedrock erosion saved through time dz_b_store: bedrock erosion stored dz_s_store: sed depth change stored eQ: exponent for scaling Qw from Ah eroded_sedsup: sediment created by bedrock erosion eW: exponent for scaling W from Qw F: fractional bedrock exposure g: gravitational acceleration (M/s^2) H: water depth Hc: Hack's Law constant He: Hack's law Exponent initial_sedsup: Sediment supply in first 10k yr (equal to uplift) kf: bedrock erodibility kQ: constant for scaling Qw from Ah kv: parameter controlling discharge variability - Crave & Davy, 2001. Lower kv is more variable kw: lateral bedrock erosion constant kWid: constant for scaling W from Qw last_z: elevation at channel mouth lmbda: sediment porosity n: manning's roughness coefficient onlyonce: changes from 0 to 1 to make sure the uplift pulse only happens one time onoff: turns sediment on and off entirely onoffsedvar: turns stochastic sediment on and off Qs: sediment supply along the length of the river Qs_down: downstream sediment flux at each node Qt: sediment transport capacity Qw: water discharge Qwo: initial water discharge QwScale: array of scalar multipliers for Qw, from the chosen distribution R: effective density of submerged sediment rho_w: density of water (kg/m^3) rho_s: density of sediment (kg/m^3) savesteps: at what time should we save outputs? sed_depth: depth of seidment at each node (m) SedStore: storing snapshots of sediment thickness through time slope: river gradient (negative usually) SlopeStore: storing snapshots of channel slope through time tarray: array of model time tau_b: basal shear stress tau_c: critical shields criterion for initiation of sediment motion time: model duration (years) topo: channel elevation (bedrock + sediment) upinc: size up uplift pulse, as a multiplier of initial uplift rate upinctime: when should the pulse happen? (years) uplift: uplift field across the entire domain uprate: initial uplift rate, if using constant uplift across domain W: channel width WidthStore: storing snapshots of channel width through time Wo: initial channel width yr2sec: number of seconds in a year z: bedrock channel elevation ZStore: storing snapshots of bedrock elevation through time</code></pre>
Response and resilience of karst subterranean estuary communities to precipitation impacts
<p>Data and code used in the analyses for the article "Response and resilience of karst subterranean estuary communities to precipitation impacts" published in the journal "Ecology and Evolution" in 2023. See Metadata file for the description on each file.</p>
UKCP18 RCM precipitation and temperature bias corrected using non-parametric quantile mapping method
<p>The UKCP18 RCM PPE (Met Office Hadley Centre, 2018) projections of precipitation and daily average temperature were bias adjusted using a non-parametric quantile mapping method based on empirical quantiles (Boe et al, 2007, Gudmundsson et al, 2012). The datasets cover the period from December 1980 to November 2080 and are intended for use in climate change impact assessments, where the bias correction helps reduce biases in multiple statistics while <span>maintaining projected climatic changes</span>.</p> <p>-------------------------------------------------</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. CEDA, <em>8 March 2022</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Boe, J.; Terray, L.; Habets, F. & Martin, E. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. International Journal of Climatology, 2007, 27, 1643-1655, doi: 10.1002/joc.1602.</p> <p>Gudmundsson, L.; Bremnes, J. B.; Haugen, J. E. & Engen-Skaugen, T. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrology and Earth System Sciences, 2012, 16, 3383-3390, doi:10.5194/hess-16-3383-2012.</p> <p><strong>Paper Citation:</strong><br>We kindly ask users of this dataset to cite the paper that describes the dataset. The paper is published and can be accessed via the following link: <a href="https://doi.org/10.5194/essd-17-2113-2025" target="_new" rel="noopener">https://doi.org/10.5194/essd-17-2113-2025</a>.</p> <p><br>Please reference the paper as:<br>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025.</p>
North American Monsoon Extreme Precipitation Supporting Datasets
<p>This dataset contains 1) shapefiles for the North American Monsoon domain and its subdomain derived from the global CPC precipitation dataset, and 2) moisture surges along the Gulf of California identified with the vertical-integrated vapor transportation from the ERA5 reanalysis dataset. 3) supporting figures for the NAM EPE analysis. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.