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2,322 results for “precipitations”
2017-2018 Oahu Precipitation Isotopes and Ions
<p>Precipitation collected around Oahu, Hawaii from 2017-2018 across 20 stations. Data includes station locations, precision of measurements, ion concentrations, and stable isotopes.</p>
Data for the publication "Reconciling compensating errors between precipitation constraints and the energy budget in a climate model"
<p>These data are a set of 6yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments (diagnostic and prognostic) of precipitation under the present-day (PD, aerosol emission at the year 2000) and preindustrial (PI, aerosol emission at the year 1850) conditions.<br> The data are used in the manuscript entitled "Reconciling compensating errors between<br> precipitation constraints and the energy budget in a climate model".</p>
High resolution deformation data from the surface of a Nickel-based superalloy: Coarse precipitates
<p>High resolution digital image correlation (HRDIC) and electron backscattered diffraction (EBSD) data provided that quantifies the deformation on the surface of Nickel-based superalloy with coarse gamma prime precipitates (250 nm diameter) after 2% strain in tension.</p>
High-resolution climate model output for selected extreme precipitation events in Cyprus
<p>This dataset consists of high-resolution model output for selected past and future extreme precipitation events for Cyprus. It was generated in the framework of the BINGO Research Project (http://www.projectbingo.eu/) . BINGO has received funding from the European Union’s Horizon 2020 Research and Innovation programme, under Grant Agreement number 641739. More details about the dataset and the design of the simulations in:</p> <p>G. Zittis, A. Bruggeman, C. Camera, P. Hadjinicolaou, J. Lelieveld,<br> The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean,<br> Atmospheric Research, Volume 191, 2017, Pages 20-33, https://www.sciencedirect.com/science/article/pii/S0169809516307153</p>
High resolution deformation data from the surface of a Nickel-based superalloy: Fine precipitates
<p>High resolution digital image correlation (HRDIC) and electron backscattered diffraction (EBSD) data provided that quantifies the deformation on the surface of Nickel-based superalloy with fine gamma prime precipitates (70 nm diameter) after 2% strain in tension.</p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅱ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmin in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmin(Tmin>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
Dataset of trend-preserving bias-corrected daily temperature, precipitation and wind from NEX-GDDP and CMIP5 in the Qinghai-Tibet Plateau——Part Ⅰ
<p>A bias-corrected dataset containing daily meteorological data of the Qinghai-Tibet Plateau has been generated, by using a trend-preserving bias-correction, the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) approach together with a high-quality gridded meteorological dataset based on ground observation (CN05.1). The data set contains daily bias-corrected values of maximum/minimum near-surface air temperature, precipitation and mean near-surface wind speed from 15 models from the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5) and their downscaled high-resolution dataset (NEX-GDDP) in the Qinghai-Tibet Plateau (QTP) during 1986-2095. This dataset can provide important reference for the study on future climate change and its impacts in the Qinghai-Tibet Plateau region.</p> <p><strong>Note: For Tmax in historical periods, the values larger than 2606 refer to no data. Set them to NaN before using, for example (Matlab): Tmax(Tmax>2606)=nan;</strong></p> <p>More details about this dataset can be found in the article: S. Chen, T. Ye, W. Liu, A. Wang and P. Shi. Evaluation and bias correction of the historical and future near-surface climate forcing in NEX-GDDP and CMIP5 over the Qinghai-Tibet plateau[J], Plateau Meteorology (in Chinese), 2020, DOI: 10.7522/j.issn.1000-0534. 2020. 00019.</p>
Data for journal article: "Automated precipitation monitoring with the Thies disdrometer: Biases and ways for improvement"
<p>This dataset contains data used for the journal article" Automated precipitation monitoring with the Thies disdrometer: Biases and ways for improvement" submitted to Atmospheric Measurement Techniques.</p>
Analysis Of Monthly And Daily Annual Extreme Precipitation For Vadodara
<p>Processed data for analysis</p> <p>1 Monthly One day Extreme Rainfall IMD</p> <p>2 Station wise Average Annual Rainfall SWDC</p> <p>3 Station wise Number of Extreme Events (P95) SWDC</p> <p>4 Urban Rural Moving Average Rainfall Ratio SWDC</p> <p>5 Generalized extreme value distribution (IMD)</p>
FYRE Climate: Precipitation
<p><strong>FYRE Climate</strong> (French hYdrometeorological REanalysis Climate dataset) is a 25-member ensemble of 142-year high-resolution reanalysis of precipitation and temperature over France, from 1 January 1871 to 29 December 2012. FYRE Climate results from the assimilation of historical daily station observations of temperature and precipitation into the SCOPE Climate reconstructions (Caillouet et al., 2019) through a Kalman filter ensemble approach (Devers et al., 2020). FYRE Climate provides an ensemble of 25 equally-plausible spatially-coherent gridded bivariate time series. Data are available at a daily time step on a 8 km grid over France as 25 files in NetCDF format. Values cover grid cells located only within metropolitan France national borders (including Corsica). The FYRE Climate dataset is fully described by Devers et al. (2021).</p>
Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Land Precipitation Simulation with the NCAR CESM1.2
<p>The dataset is the output of CESM that is used in the paper "Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Land Precipitation Simulation with the NCAR CESM1.2" submitted to <em>Geophysical Research Letters</em>.</p>
Data and scripts for: Intercomparison of atmospheric datasets and PBL schemes for precipitation downscaling over a coastal mountain valley of northern British Columbia, Canada
<p>anl_6MYJdiv and anl_6MYNNdiv contains pairwise normalizations of dataset outputs (NAM/ERA5, NAM/NARR and ERA5/NARR) of total rainfall in 2017 for simulations with the MYJ and MYNN3 PBL schemes, that can be plotted by fig3_4.ncl. anl_MYJMYNN_div contains MYJ/MYNN3 spatial contours for each of ERA5, NAM -ANL and NARR outputs. anl_snow_MYJ contains MYJ output for total snow in 2017 by the ERA5, NAM-ANL and NARR datasets, for which values at discrete locations can be retrieved with yr2017snow.ncl, daily_ppt.ncl is script to extract modeled daily precipitation (dly_MYJ and dly_MYNN) from the various locations. Fig_ppt_monthly.R is the plotting script for observed and modeled precipitation time series from hydro31pt1pk.txt. nullwrf is array holder for plotting with ncl scripts. rivs_coasts.shp is shape file that is used in the spatial plots. </p>
Spectral induced polarization of calcite precipitation: 2D experimental pore scale observation
<p>This data will be available after publication.</p>
Atom probe characterisation of segregation driven Cu and Mn–Ni–Si co-precipitation in neutron irradiated T91 tempered-martensitic steel - data
<p>Data for 'Atom probe characterisation of segregation driven Cu and Mn–Ni–Si co-precipitation in neutron irradiated T91 tempered-martensitic steel' paper (<a href="https://doi.org/10.1016/j.mtla.2020.100946">https://doi.org/10.1016/j.mtla.2020.100946</a>) </p>
1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_timeseries)
<p>ChinaClim_timeseries is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
The biotic interactions hypothesis partially explains bird species turnover along a lowland Neotropical precipitation gradient
<p><span><span><span><span><span><span><span><span><span><span><span><b>Aim</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>We evaluated the influence of climate in determining bird communities along precipitation gradients. We argue that mechanisms responsible for community turnover along precipitation gradients are similar to mechanisms operating along temperature and latitudinal gradients. We test the hypothesis that environmental conditions affect community composition in dry forests, whereas biotic interactions affect community composition in wet forests.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Location</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Low-elevation forests along a precipitation gradient in Colombia where precipitation ranges from 700 – 4000 mm annually but neither temperature or elevation change.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Time period</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Present day</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Major Taxa Studied</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Tropical Forest Birds.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>We performed 291 bird counts in nine study areas across the ~3000 mm range of variation in precipitation. In each locality we obtained climatic characteristics, and a phylogenetic, morphological and physiological proxy data set to test predictions about the evolutionary relationships and distribution of traits in each community. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Bird communities changed abruptly along the precipitation gradient and could be divided into dry and wet forest communities. Analyses of phylogenetic relationships, trait space, and observations at nests suggested that environmental filtering is more important in dry forest, especially for breeding. In contrast, we found little evidence that competition was more important in wet forest. Nest predation or competition for nest space, however, may be more critical in wetter forests.</span></span></span></span></span></span></span></span></span></span></span></p> <p class="author"><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>The two distinct bird communities we documented suggest that lowland precipitation gradients, where temperature is constant can be as important as temperature gradients in generating high beta diversity. We conclude that the breeding process in bird communities might be crucial for determining community assembly along environmental gradients. Given that recent population declines in tropical birds have been attributed to changes in precipitation, by understanding the mechanisms underlying community assembly along precipitation gradients our study may improve our ability to understand those declines and predict the effects of climate change on neotropical avifauna.</span></span></span></span></span></span></span></span></span></span></span></p>
Data for quantitative precipitation estimation with Rulefit
<p>This is the data that I used for quantitative precipitation estimation with Rulefit. rain represents the rain data, band 07-10 is the Himawari 8 satellite data. radar is the combined reflectivity data. Three kinds of data were preprocessed into the array with the shape of 301*279, and extracted the region of interest(east china) with python package(Salem). Then, these array are flattened and concatenated into pandas dataframe. nan value was dropped by dropna. I saved the data in the format of csv. As the review of manuscript progresses, I will continue to provide new versions and give new doi here.</p>
MFS-M-00157 Snow precipitation measured manually (Tretyakov rain gauge), raised bog (ridge-hollow complex), 2010-present
<p>A raine gauge or Russian hydro-meteorological network standard (Tretyakov' construction) was installed as part of manual meteorological station in Mukhrino research polygon in 2010. The bucket installed at 2m height and protected by wind shield. Since 2014 only winter measurements were collected to supplement rain precipitation measurements measured in summer by automatic rain gauges (see also MFS-M-00154, MFS-M-00307, MFS-M-00308 for summer measurements in the same location).</p>
MFS-M-00308 Rain precipitation measured in raised bog (ridge-hollow complex), HOBO rain gauge RG3-M, 2017-present
<p>A raine gauge HOBO RG3-M was installed at the Mukhrino field station reseach polygon in the raised bog, treed bog community (ryam) in 2017, installation on the ground level (to prevent wind turbulence), measurement frequency - by event. This series is duplicated by MFS-M-00154 and MFS-M-00307 to cover variability and in case of breakdown. See also MFS-M-157 for winter (snow) precipitation at the same site measured manually.</p>
MFS-M-00154 Rain precipitation measured in raised bog (ridge-hollow compleх), HOBO rain gauge RG3-M, 2008-2019
<p>A raine gauge HOBO RG3-M was installed at the Mukhrino field station reseach polygon in the raised bog (ridge-hollow complex) in 2008, installation on the ground level (to prevent wind turbulence), measurement frequency - by event. This series was stopped by 2019 and replaced by another in close proximity (50 m), see MFS-M-00308 for precipitation after 2019.</p>
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OpenNeuro
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