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2,322 results for “precipitations”
North American watershed precipitation from WRF
<p>Precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model. Each file contains the following variables for the stated watershed:</p> <p>lat: latitude of each grid point, in degrees north</p> <p>lon: longitude of each grid point, in degrees north</p> <p>time: day of simulation</p> <p>RAINC: accumulated total cumulus precipitation, in mm</p> <p>RAINNC: accumulated total grid scale precipitation, in mm</p> <p>SNOWNC: accumulated total grid scale snow and ice, in mm</p> <p>watershed mask: matrix where 1 indicates the grid point is inside the watershed, 0 is outside</p> <p>watershed mountain mask: matrix where 1 indicates the grid point is within the mountainous portion of the watershed, 0 is outside</p>
Supplementary Dataset for "Most Earth-surface calcites precipitate out of isotopic equilibrium", by M. Daëron, R.N. Drysdale, M. Peral, D. Huyghe, D. Blamart, T.B. Coplen, F. Lartaud, and G. Zanchetta
<p>This repository contains the raw data files and Python code used to generate all figures and equations included in the article "Most Earth-surface calcites precipitate out of isotopic equilibrium", by Daëron et al. (https://doi.org/10.1038/s41467-019-08336-5).</p>
Dataset - Assessing the Added Value of the Intermediate Complexity Atmospheric Research Model (ICAR) for Precipitation in Complex Topography
<p>Abstract. The coarse grid spacing of global circulation models necessitates the application of downscaling techniques to investigate the local impact of a changing global climate. Difficulties arise for data sparse regions in complex topography which are computationally demanding for dynamic downscaling and often not suitable for statistical downscaling due to the lack of high quality observational data. The Intermediate Complexity Atmospheric Research Model (ICAR) is a physics-based model that can be applied without relying on measurements for training and is computationally more efficient than dynamic downscaling models. This study presents the first in-depth evaluation of multi-year precipitation time series generated with ICAR on a 4 × 4 km<sup>2</sup> grid for the South Island of New Zealand for an eleven-year period, ranging from 2007 until 2017. It focuses on complex topography and evaluates ICAR at 16 weather stations, eleven of which are situated in the Southern Alps between 700 m MSL and 2150 m MSL. ICAR is assessed with standard skill scores and the effect of model top elevation, topography, season, atmospheric background state and synoptic weather patterns on these scores are investigated. The results show a strong dependence of ICAR skill on the choice of the model top elevation, with the highest scores obtained for 4 km above topography. Furthermore, ICAR is found to provide added value over its ERA-Interim reanalysis forcing data set for alpine weather stations, improving mean squared errors (MSE) by up to 53 % and 30 % on median. It performs similarly during all seasons with an MSE minimum during winter, while flow linearity and atmospheric stability were found to increase skill scores. ICAR scores are highest during weather patterns associated with flow perpendicular to the Southern Alps and lowest for flow parallel to the alpine range. While measured precipitation is underestimated by ICAR, these results show the skill of ICAR in a real-world application, and may be improved upon by further observational calibration or bias correction techniques.<br> Based on these findings ICAR shows the potential to generate downscaled fields for long term impact studies in data sparse regions with complex topography.</p>
Arctic Ocean freshwater dynamics: transient response to increasing river runoff and precipitation [dataset]
<p>This dataset contains the underlying data for the manuscript Brown et al., Arctic Ocean freshwater dynamics: transient response to increasing river runoff and precipitation, submitted to JGR-Oceans</p> <p>-----------------------------------<br> Descriptors in the filenames, shown below as *, correspond to the various simulations, as follows:</p> <p>Simulations forced with JRA-25 reanalysis data:<br> AR: Unperturbed control simulation<br> B1: Simulation involving a step change in river runoff of -30% <br> C1: Simulation involving a step change in river runoff of +30% <br> B7: Simulation involving a step change in precipitation of -30% <br> C8: Simulation involving a step change in precipitation of +30% </p> <p>Simulations forced with the CORE-II climatology:<br> AR_CORE: Unperturbed control simulation <br> P-30_CORE: Simulation involving a step change in precipitation of -30% <br> P+30_CORE: Simulation involving a step change in precipitation of +30% </p> <p>-----------------------------------<br> The files named as fwvolume_Sref35_*.nc contain the variables:</p> <p>freshwater: horizontally-integrated liquid freshwater volume in m^3<br> freshwater_pos: as before, but including only positive values in the integration<br> h_f: basin-mean freshwater height in m<br> h_f_pos: as before, but including only positive values in the integration</p> <p>The reference salinity used in each case is 35.</p> <p>-----------------------------------</p> <p>The files named as freshwater_h_f_*.nc contain horizontally-gridded, depth-integrated liquid freshwater heights in m. The integration is made to a depth of 276.68m.</p> <p>h_f_34p8: freshwater height, using a reference salinity of 34.8 <br> h_f_34p8_pos: as before, but including only positive values in the integration<br> h_f_35: freshwater height, using a reference salinity of 35 <br> h_f_35_pos: as before, but including only positive values in the integration</p> <p>The grid for these files is grid.nc</p> <p>-----------------------------------</p> <p>The files named as fluxes_ed3_*.nc contain strait-integrated volume fluxes (in m^3 s^-1) into and out of the Arctic domain: </p> <p>f: liquid volume flux<br> fw: freshwater, with variables numbered 1 using a reference salinity of 34.8 and those numbered 2 using a reference salinity of 35<br> ice: sea ice volume flux</p> <p>The first dimension, ngate, of the variables indicates the strait:</p> <p>1) Fram Strait<br> 2) Barents Sea Opening<br> 3) Bering Strait<br> 4) Amundsen Gulf<br> 5) McClure Strait<br> 6) Canadian Arctic Archipelago<br> 7) Nares Strait</p> <p>-----------------------------------</p> <p>Sea ice volumes (in m^3) for the JRA-25 forced simulations are contained in the files named area_int_*.nc with variable name "ivol"</p> <p>-----------------------------------</p> <p>Basin-integrated liquid freshwater volumes for a further series of JRA-forced runoff perturbation experiments are contained within the file FWLvol_AO_Sref35_runoff.mat:</p> <p>R_10: a step increase in runoff of 10%<br> R_60: a step increase in runoff of 60%<br> R_100: a step increase in runoff of 100%</p> <p>The reference salinity is 35 and depth of integration 276.68m</p>
Effects of Energetic Electron and Proton Precipitations on Thermospheric Nitric Oxide Cooling during shock-led Interplanetary Coronal Mass Ejections
<p>Satellite measurements have revealed significant enhancement of 5.3-µm nitric oxide (NO) emission during shock-led interplanetary coronal mass ejections (ICMEs). Great discrepancies in modeled neutral density occur during these events, and may be attributed to the abnormally high NO cooling. Meanwhile, the relative significance of protons, soft electrons, and keV-electrons to NO emission is yet to be well determined. The goal of this study is to identify the contribution of electron and proton precipitations to the thermospheric NO cooling by using the Defense Meteorological Satellite Program (DMSP) data. The observed energetic electrons and protons (0.1–30.2 keV) during 36 shock-led ICME events in 2002–2010 are binned into geomagnetic grids to provide statistical distributions of the particle precipitation for polar regions. The distributions are incorporated into the Global Ionosphere-Thermosphere Model. The results show that electrons play a dominant role to NO cooling, but protons are also important and contribute to up to a quarter of NO cooling by electrons and ions combined. NO cooling enhancement during the events is proportional to the level of energy flux and is dominated by the electrons in the energy band of 1.4–3.1 keV. Both total electron content (TEC) and NO cooling enhance at the source regions, but they have different lifetime and correlation with the particle precipitations. Generally, NO cooling and TEC enhancements have a positive correlation with the precipitating energy. Cross correlation shows that particle precipitations have more direct and instantaneous impact on TEC while it takes longer for the atmosphere to heat up for cooling to proceed.</p>
Data for Understanding the moisture variance in precipitating shallow cumulus convection
<p>Data accompanying the paper titled: Understanding the moisture variance in precipitating shallow cumulus convection .</p> <p> </p> <p>This repository contains configuration files and output statistics for three idealized simulations run with LES model MicroHH (van Heerwaarden et al., 2017). </p> <p><br> The experimental setup of all cases is based on the Rain in Cumulus over the Ocean (RICO) field experiment as used in the GEWEX Cloud System Study (GCSS) RICO model inter comparison study (Rauber et al., 2007; vanZanten et al, 2011). The experiment, defined as the standard case (referenced as STD), uses the GCSS configuration, while the second experiment, termed as the moist case (referenced as MST), is initialized with an increased moisture content in the cloud layer and above of about 2 g/kg. Both of these experiments cover a domain size of 50 x 50 x 6 km with an isotropic grid spacing of 25 m. The control simulation (referenced as CTRL) is a non-precipitating reference simulation. The setup for the control case is identical to the setup of the standard case, except for a smaller domain size of 8 x 8 x 6 km.</p> <p> </p> <p> </p> <p>The files are organized in the following way: </p> <p>Each case has its own folder (CTRL, MST, and STD). </p> <p>Each such folder contains a 'setup' sub-folder with the configuration files for the microHH simulation: an '.ini' file and a '.py' file that generates initial conditions and forcings. </p> <p>Output statistics are located in the 'statistics_output' sub-folders. They contain '.nc' files with statistics from 30 h to 35 h of the simulation. Conditional spatial averaging was performed in order to understand the significance of the convective regions. Files valid for cloud active regions are marked with '_CA'. Files valid for non active regions are marked with '_NA' (Bastak Duran, Geleyn, Vana, Schmidli, & Brozkova, 2018). Files without '_CA' or '_NA' are valid for the whole domain.</p>
Raindrop size distribution and precipitation over Peruvian central Andes (12.0°S)
<p>The raindrop size distribution and precipitation over Peruvian central Andes (12.0°S) database consist of raindrop size distribution, raindrop fall velocity and precipitation data measured in the Huancayo Observatory of Peru.<br> Two Parsivel 2 and two tipping bucket rain gauge are used in this database which are operating together since 2018.</p> <p>The repository ULR:<br> https://scah.igp.gob.pe/sites/datos/PP_prods/</p> <p>There are two types of files, one NetCDF for the precipitation totals and<br> another NetCDF which contains each Parsivel2 raw data.</p> <p>The precipitation totals files are located in the subfolder:<br> https://scah.igp.gob.pe/sites/datos/PP_prods/PP_dia/</p> <p>And the Parsivel2 raw data are located in the sub folder:<br> https://scah.igp.gob.pe/sites/datos/PP_prods/PSV_nc/</p> <p>This repository is part of the Atmospheric Microphysics And Radiation Laboratory (LAMAR),<br> Huancayo Observatory (12.0S, 75.3W, 3113 m ASL) (https://scah.igp.gob.pe/laboratorios/lamar)</p>
Data for Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options
<p>This dataset accompanies the submitted paper in the Earth and Space Science:Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options.</p>
Early Mars EBM data for "CLIMATE SIMULATIONS OF EARLY MARS WITH ESTIMATED PRECIPITATION, RUNOFF, AND EROSION RATES"
<p>These are all the data and plots we used for the publication, CLIMATE SIMULATIONS OF EARLY MARS WITH ESTIMATED PRECIPITATION, 2 RUNOFF, AND EROSION RATES.</p>
X-ray Signatures of Lightning-induced Electron Precipitation
<p>Figures, data, and code used in my paper describing X-ray signatures of lightning-induced electron precipitation (LEP)</p>
Supporting Information for "The polarity of IMF By strongly modulates particle precipitation during high-speed streams"
<p>This data file contains start times of 485 high-speed stream events, used in the manuscript (for Geophysical Research Letters) "The polarity of IMF By strongly modulates particle precipitation during high-speed streams". </p>
Linkage between projected warm season precipitation systems and thermodynamic and microphysical changes over eastern China
<p>Data used in the manuscript "Linkage between projected warm season precipitation systems and thermodynamic and microphysical changes over eastern China" which was submitted to Journal of Geophysical Research: Atmospheres. </p>
Temperature, precipitation and drought code at 1 km resolution over Italy from start of 2017 to end of 2022 (6 years)
<p>These files are products from an investigation related to understanding seasonal vertical ground movement through InSAR and how it is correlated to drought and temperature. </p> <p><strong>All the raster stacks have one band per day from 1 January 2017 to 1 January 2023 for a total of 2192 bands</strong>. All raster are in geographic coordinate systems with a WGS84 datum and aligned to an extent of 6.6116°, 18.51428°, 35.48345°, 47.09867° with a cell ground sampling distance of 0.008983153° degrees.</p> <p> (i) PrecipitationCHIRP.tif - daily precipitation in Italy, from the CHIRP dataset.</p> <p> (ii) correctedMODIS_LST_2018_2024.tif - daily air temperature interpolated values from MODIS corrected from LST to air temperature using a simple correction factor (see https://www.mdpi.com/2072-4292/16/22/4123) </p> <p>(iii) droughtCodeMODIS_CHIRP_2018_2024.tif - calculated drought code (see paper).</p> <p>(iv) italyEGMS_DCcor.gpkg - all Measurement Points with absolute value of Spearman correlation above 0.7 for Italy.</p> <p>An interactive webgis is available at <a href="https://www.cirgeo.unipd.it/appVM/EGMS/" target="_blank" rel="noopener">https://www.cirgeo.unipd.it/appVM/EGMS/</a> (not always active, please email authors if not accessible). This portal allows the user to click each measurment point that was detected as having significant correlation with drought codes, and see the time-series of the vertical movement and of the detrended drought code and temperature and precipitation (see preview image).</p>
Precipitation trend analysis for different Mediterranean countries
<p>Independent files with detailed numeric analysis of precipitation evolution and trends for different countries in the Mediterranean region.</p>
Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"
<h1>Code and Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"</h1> <p>This dataset accompanies the research paper titled <strong>"On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"</strong>, currently under review for the AGU Journal GRL. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes">Github Repository</a>.</p> <p>The code can also be found as a ".zip" file: *On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes-main. </p> <h2>Aims</h2> <p>Our study focuses on two important gaps in the literature regarding the extrapolation of empirical downscaling algorithms. First, we examine how well relationships learned from a historical period extrapolate to future unobserved climates. We compare two widely used algorithms, a GAN and a deterministic CNN baseline, that use a similar architecture (i.e. convolutional layers) trained in a model-as-truth framework to downscale daily precipitation over New Zealand. We evaluate their accuracy in capturing climate change signals in mean and extreme precipitation. Second, we explore whether training on future vs. only historical periods combined with different-sized training datasets can improve extrapolation skill. </p> <h2>Geographic Focus</h2> <p>Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).</p> <p> </p> <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> <p>The training data used in this study (for our RCM emulator) spans the historical period and future period (SSP370) of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables. </p> <ul> <li> <p><strong>Resolution:</strong> The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation. </p> </li> <li> <p><strong>Period Coverage:</strong></p> <ul> <li>Training Data: 1960-2100 (Depending on Experiment, see Table 1 for list of experiment configurations)</li> <li>Validation Data: 1985-2014 + 2070-2099 (to compute the climate change signal)</li> </ul> </li> <li> <p><strong>Models:</strong></p> <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM, CNRM-CM6-1, AWI-MR-1 </li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> <p><strong>Training Data:</strong></p> <ul> <li>Target/Ground Truth (Y): <code>target_ACCESS-CM2_hist_ssp370_pr.nc</code></li> <li>Predictor (X): <code>predictor_ACCESS-CM2_hist_ssp370.nc</code></li> </ul> </li> <li> <p><strong>Evaluation Data:<br></strong>All other GCMs can be accessed in one single file, predictor and target variables have the dimensions (time, lat, lon, GCM).</p> <ul> <li>Target/Ground Truth (Y): <code>Other_GCMs_hist_SSP370_target_fields_pr.nc</code></li> <li>Predictor (X): <code>Other_GCMs_hist_SSP370_predictor_fields.nc</code></li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> <p><strong>Regional Climate Model</strong>, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>.</p> </li> <li> <p><strong>Predictor and Target Variables:</strong> Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository.</p> </li> <li> <p><strong>Training Framework:</strong> Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a></p> </li> </ul> <table> <tbody> <tr> <td> <p><strong>Algorithm</strong></p> </td> <td> <p><strong>Training Data</strong></p> </td> <td> <p><strong>Period</strong></p> </td> </tr> <tr> <td> <p>Deterministic Baseline</p> </td> <td> <p>Historical</p> </td> <td> <p>1960-2014 (~21,000 days)</p> </td> </tr> <tr> <td> <p>Deterministic Baseline</p> </td> <td> <p>Future (SSP370)</p> </td> <td> <p>2044-2099 (~21,000 days)</p> </td> </tr> <tr> <td> <p>Deterministic Baseline</p> </td> <td> <p>Historical and Future (SSP370)</p> </td> <td> <p>1960-2099 (~51,000 days)</p> </td> </tr> <tr> <td> <p>Residual GAN</p> </td> <td> <p>Historical</p> </td> <td> <p>1960-2014</p> </td> </tr> <tr> <td> <p>Residual GAN</p> </td> <td> <p>Future (SSP370)</p> </td> <td> <p>2044-2099</p> </td> </tr> <tr> <td> <p>Residual GAN</p> </td> <td> <p>Historical and Future (SSP370)</p> </td> <td> <p>1960-2099</p> </td> </tr> </tbody> </table> <p><strong>Table 1:</strong> The six RCM emulator experiments performed in this study.</p>
Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet
<p>Supplementary data used in the article <em>Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet</em> submitted to The Cryosphere.</p> <p>Please cite the corresponding paper if you use this data. </p> <p>We supply</p> <ol> <li>the regridded CMIP6 precipitation (pr) and temperature (tas) model output for Greenland.</li> <li>local precipitation-near surface temperature sensitivities for each model in the folder <em>local_slopes_precipitation</em></li> <li>The monthly anomalies for near-surface temperature and precipitation as well as the local change in precipitation (compare to the climatology, in %) with respect to the climatology (year 1980-200) in the folder <em>2100_anomalies</em></li> <li>The CMIP6 monthly climatology from the years 1980-2000 (precipitation & near-surface temperature) </li> <li>The monthly precipitation and near-surface temperature anomalies from 2015-2100 in the folder <em>2100_85years</em></li> </ol> <p> </p> <p>License of CMIP6 model output: </p> <p>CMIP6 model data produced is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0; https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</p>
The accelerating loss of resilience in suburban woodlands can largely be attributed to changes in urban precipitation patterns
<p><span>Vegetation resilience holds significant importance for stabilizing ecosystem service functions in a changing climate. While global land surface vegetation resilience changes have been extensively studied, the impact of urbanization on the resilience of suburban woodlands remains inadequately understood.</span> <span>In this study, we utilized two Critical Slowing Down (CSD) indicators, namely lag-one autocorrelation (LOA) and variance (VA), to assess the vegetation resilience, its long-term trends, and influencing factors in suburban woodlands across 1,356 cities worldwide. The recovery rates estimated by LOA (r<sub>r1</sub>) and VA (r<sub>r2</sub>) showed close alignment in suburban woodlands with low suburban forest coverage (SFC) areas (correlation coefficient (r)=0.95). However, a notable divergence was observed in areas with high SFC (r=0.73). Suburban woodlands with high SFC typically exhibited lower recovery rate estimates, thus indicating greater vegetation resilience compared to areas with lower SFC. From 1986 to 2022, the recovery rates of suburban woodland areas in over 83% of the cities demonstrated a significant upward trend, with an average of 3.23×10<sup>−3</sup> yr<sup>−1</sup> for both r<sub>r1</sub> and r<sub>r2</sub>, signifying a widespread decline in vegetation resilience. The accelerating pace of urbanization led to higher rising rates of r<sub>r1</sub> and r<sub>r2</sub> during 2010-2022 (5.11×10<sup>−3</sup> yr<sup>−1</sup>) compared to 1986-1999 (0.49×10<sup>−3</sup> yr<sup>−1</sup>). The notable decrease in resilience of forestland was primarily attributed to reduced precipitation in urban suburbs, which can be explained by urbanization-induced heat island and building barrier effects, causing a shift of precipitation center from urban suburbs to central cities. In summary, this study revealed that urbanization diminishes the vegetation resilience of urban suburban woodlands by altering urban precipitation patterns. These findings underscore the necessity of augmenting water availability in urban suburbs to restore resilience in these woodlands, thereby enhancing their ecosystem service value.</span></p>
The accelerating loss of resilience in suburban woodlands can largely be attributed to changes in urban precipitation patterns
<p><span>Vegetation resilience holds significant importance for stabilizing ecosystem service functions in a changing climate. While global land surface vegetation resilience changes have been extensively studied, the impact of urbanization on the resilience of suburban woodlands remains inadequately understood.</span> <span>In this study, we utilized two Critical Slowing Down (CSD) indicators, namely lag-one autocorrelation (LOA) and variance (VA), to assess the vegetation resilience, its long-term trends, and influencing factors in suburban woodlands across 1,356 cities worldwide. The recovery rates estimated by LOA (r<sub>r1</sub>) and VA (r<sub>r2</sub>) showed close alignment in suburban woodlands with low suburban forest coverage (SFC) areas (correlation coefficient (r)=0.95). However, a notable divergence was observed in areas with high SFC (r=0.73). Suburban woodlands with high SFC typically exhibited lower recovery rate estimates, thus indicating greater vegetation resilience compared to areas with lower SFC. From 1986 to 2022, the recovery rates of suburban woodland areas in over 83% of the cities demonstrated a significant upward trend, with an average of 3.23×10<sup>−3</sup> yr<sup>−1</sup> for both r<sub>r1</sub> and r<sub>r2</sub>, signifying a widespread decline in vegetation resilience. The accelerating pace of urbanization led to higher rising rates of r<sub>r1</sub> and r<sub>r2</sub> during 2010-2022 (5.11×10<sup>−3</sup> yr<sup>−1</sup>) compared to 1986-1999 (0.49×10<sup>−3</sup> yr<sup>−1</sup>). The notable decrease in resilience of forestland was primarily attributed to reduced precipitation in urban suburbs, which can be explained by urbanization-induced heat island and building barrier effects, causing a shift of precipitation center from urban suburbs to central cities. In summary, this study revealed that urbanization diminishes the vegetation resilience of urban suburban woodlands by altering urban precipitation patterns. These findings underscore the necessity of augmenting water availability in urban suburbs to restore resilience in these woodlands, thereby enhancing their ecosystem service value.</span></p>
Data for the publication "Evaluation of climatic impacts of ice nucleating particles through precipitation process with a GCM"
<p>These data are a set of annual-mean values for 5yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments of precipitation (i.e., diagnostic and prognostic). The outputs include diagnostics from the satellite simulator COSP2.<br>The data are used in the manuscript entitled "Evaluation of climatic impacts of ice nucleating particles through precipitation process with a GCM". All data used in this study are available from the corresponding author upon request.</p>
Enhanced_Precipitation_of_energetic_protons_due_to_Uranus_asymmetric_magnetic_field
<p>The dataset pertaining to Figures 1-4 in the academic paper titled: "Enhanced Precipitation of energetic protons due to Uranus asymmetric magnetic field" by Matthew Acevski and Adam Masters</p>
ScienceDex guides
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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.