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2,322 results for “precipitations”
Data for "Global Precipitation Correction Across a Range of Climates Using CycleGAN"
<p># Data for "Global Precipitation Correction Across a Range of Climates Using CycleGAN"</p> <p>This repository contains the data used in the paper "Global Precipitation Correction Across a Range of Climates Using CycleGAN" by J. McGibbon et al. (2023, *in review*).</p> <p>`train_val` contains the model outputs for the training and validation sets used in the paper. Model spinup data is not included. The C384 runs are stored as a single series along a concatenated time axes. All C384 data has been coarsened to C384 resolution.</p> <p>`ramping_data` contains the model outputs from the 4-year, 3-month ramping simulation, including spinup data.</p> <p>`predicted` is included for convenience, and contains the model outputs from the "best" model, as was used to create figures shown in the paper. This model output was created from the CycleGAN using the validation dataset (stored in `train_val`) as input, and years 2 and 3 of the ramping simulation in `ramping_data` (following the 3-month spinup period). Ramping predictions are stored as separate datasets for the C48 (backwards) prediction and the C384 prediction. Validation data is stored alongside its respective target data in a single netCDF file, labelled "processed-agg". These filenames are intentionally left unchanged so that they correspond with the names of files used in the `process_combined_aggregate.py` script in `projects/cyclegan` of the code DOI for this paper, which was used to create its figures.</p>
Data for A multi-phase biogeochemical model for mitigating earthquake-induced liquefaction via microbially induced desaturation and calcium carbonate precipitation
<p>This data accompanies the paper published in Biogeosciences, which can be found at https://doi.org/10.5194/egusphere-2022-1419.</p>
Simulated diagenesis of the iron-silica precipitates in banded iron formations: Data Repository
<p>This XRD dataset derives from experiments we performed bubbling 49 ppm O2 into simulated Archean seawater and then aging the produced precipitates at 25 degrees C, 80C, 150C, and 220C. Precipitate slurries were extracted from experimental samples and pipetted as 20 µL subsamples into Cole-Parmer Kapton tubes to keep anoxic during XRD analysis. Samples were sent to McMaster Analytical X-Ray Diffraction Facility (MAX) for XRD analysis using a Bruker D8 DISCOVER cobalt source tube (Co-XRD) with a DAVINCI.DESIGN diffractometer. More details on methods in associated article. Resultant XRD measurements of our samples yielded patterns showing increasing crystallinity with temperature. The bubbled experiment aged for 40 days at 25 °C produced a large and diffuse diffraction peak corresponding to the Kapton tube but no other sharp diffraction peaks, suggesting an amorphous to minimally crystalline product. A broad peak in the 25 °C precipitate, that persisted through the higher-temperature aging treatments, may correspond to ferrihydrite. The bubbled experiment aged at 80C contained diffraction peaks consistent with a serpentine group silicate and a spinel group oxide (like magnetite). After the 150 °C treatment, samples showed sharper peaks consistent with a serpentine group silicate and spinel group oxide. After 220 °C aging, the precipitates showed a continued narrowing of the diffraction peaks for a spinel group oxide, reflecting an increase in crystal size and/or crystallinity, but smaller and less sharp serpentine group peaks. </p>
The daily gridded precipitation observations (0.5° × 0.5°) in central and eastern China (1961-2021)
<p>The daily observed gridded precipitation data were obtained from the China Meteorological Data Service Center (<a href="http://www.nmic.cn/en">http://www.nmic.cn/en</a>) on 04-08-2021 but are currently not available at this website. These data have 128 columns and 72 rows with longitude and latitude starting from 72°E and 18°N, respectively. Spatial resolution is 0.5°×0.5°.</p> <p>Here, we provide these data during 1961-2021 in two study regions: central China (111°-116°E, 32°-37°N) and eastern China (118°-123°E, 28°-34°N). To make it easier for everyone to reuse the data, we select these data with adding two degrees in each direction of two regions. The selected data have 19 columns and 19 rows (21 columns and 19 rows) in central (eastern) China with the grid center's longitude and latitude ranging from (109°-118°E, 30°-39°N) to (116°-125°E, 26°-36°N). </p>
Spatiotemporally independent heavy precipitation events for the state of Hesse (Germany)
<p>This data set contains a collection of spatiotemporally independent convective precipitation objects for the German state of Hessen. The data set was generated on the basis of the <em>RADKLIM‑YW Version 2017.002 (</em>https://doi.org/10.5676/DWD/RADKLIM_YW_V2017.002) radar precipitation data of the German Weather Service (DWD). It is grouped into precipitation duration stages of 15, 30, 45, 60, 75 and 90 minutes as well as a spatial aggregation of 9 and 25 grid cells. This results in 12 separate event lists. </p>
Output of the Land Surface Model ORCHIDEE over river catchments in Europe, run with GSWP3 and synthetic forcings where the precipitation is modified
<p># Description of the data file</p> <p>This dataset contains the main outputs used for the results of the article: "Budyko framework based analysis of the effect of climate change on watershed evaporation efficiency and its impact on discharge over Europe", by Julie Collignan, Jan Polcher, Sophie Bastin, Pere Quintana-Segui, accepted by the journal *Water Resources Research*.</p> <p>This study uses the outputs of a land surface model (LSM), forced with differentan atmospheric datasets from 1901 to 2010. The atmospheric dataset are based on GSWP3 (Hyungjun, K. (2017), doi: 10.20783/DIAS.501) and was modified to create synthetic forcings with different precipitation characteristics (annual average, intra-annual distribution). The LSM was run with all synthetic forcings, and the outputs (precipitation, evapotranspiration, potential evapotranspiration, discharge) were integrated at the level of each river basin which are sampled by gauging stationss. These outputs are used to fit a parametric equation of the Budyko framework to decompose the partial trends in discharge and the relative weight of the different climatic components.</p> <p># Details of the variables and attribute of the file</p> <p>This study was led over 2196 river basins over Europe. </p> <p>For each catchment used in the study, it gathers:<br> - name of the station at the outlet *name*<br> - name of the river associated *rivers*<br> - lat/lon of the station *Localisation*<br> - upstream area of the catchment *upstream*<br> - Observed discharge (data not used in the article) *DisObs*<br> These data come from three different sources:<br> * *Global Runoff Data Centre (GRDC)*, https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html ;<br> * *Ministere de lenergie* (February 2021), https://www.hydro.eaufrance.fr/" ;<br> * *Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico*, 2020</p> <p>Each catchment was projected on the grid of the LSM ORCHIDEE (*IPSL, https://orchidee.ipsl.fr/*) during the construction of its river routing system. More details are given in the associated article.</p> <p>The results for four different run of the LSM are included in this dataset. This dataset gathers for each run:<br> * Precipitation *P*<br> * Evapotranspiration *E*<br> * Potential evapotranspiration *PET*<br> * Discharge *DisMod*</p> <p>The different synthetic forcings are:<br> - the reference forcing with the un-modified atmospheric dataset: *the Global Soil Wetness Project Phase 3 (GSWP3)*, Hyungjun, K. (2017), doi: 10.20783/DIAS.501<br> - *f2000*: A forcing where all 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year. Therefore, all components of $P$ (average and intra-annual variations) are set constant.<br> - *cstmean*: A forcing for which we keep the relative intra-annual distribution of $P$ of each year, but where the average $P$ of each year is set constant. The 3h values of $P$ are scaled so the hydrological year average is set to the one of the year 2000 (September 1999 to September 2000).<br> - *cstintravar*: A forcing for which we keep the annual average of $P$ for each year, but where the relative intra-annual distribution of $P$ is set constant. The 3h values of $P$ are set to the values of the year 2000 (September 1999 to September 2000) for each year and then scaled over each hydrological year so the yearly average is set to the one of the corresponding years in the reference forcing.<br> \end{itemize}</p> <p>## ncdump -h Filename.nc</p> <p>```<br> dimensions:<br> basins = 2196 ;<br> years = UNLIMITED ; // (110 currently)<br> loc = 2 ;<br> lenstr = 57 ;<br> variables:<br> short years(years) ;<br> years:long_name = "Years" ;<br> years:units = "year" ;<br> char names(basins, lenstr) ;<br> names:long_name = "Name of the station at the catchment output" ;<br> char rivers(basins, lenstr) ;<br> rivers:long_name = "Name of the river where the station is positioned" ;<br> float upstream(basins) ;<br> upstream:long_name = "Upstream area of the catchment" ;<br> upstream:units = "km^2" ;<br> float Localisation(basins, loc) ;<br> Localisation:long_name = "Position of each catchment: (Lon, Lat)" ;<br> Localisation:units = "degrees_east, degrees_north" ;<br> float DisObs(years, basins) ;<br> DisObs:long_name = "Discharge observation at the outlet of the catchment" ;<br> DisObs:units = "m3/s" ;<br> float E_ref(years, basins) ;<br> E_ref:long_name = "Modeled average annual evaporation with forcing ref" ;<br> E_ref:units = "m3/s" ;<br> float P_ref(years, basins) ;<br> P_ref:long_name = "Average annual precipitation for forcing ref" ;<br> P_ref:units = "m3/s" ;<br> float PET_ref(years, basins) ;<br> PET_ref:long_name = "Modeled average annual potential evaporation with forcing ref" ;<br> PET_ref:units = "m3/s" ;<br> float DisMod_ref(years, basins) ;<br> DisMod_ref:long_name = "Modeled Discharge with forcing ref" ;<br> DisMod_ref:units = "m3/s" ;<br> float E_f2000(years, basins) ;<br> E_f2000:long_name = "Modeled average annual evaporation with forcing f2000" ;<br> E_f2000:units = "m3/s" ;<br> float P_f2000(years, basins) ;<br> P_f2000:long_name = "Average annual precipitation for forcing f2000" ;<br> P_f2000:units = "m3/s" ;<br> float PET_f2000(years, basins) ;<br> PET_f2000:long_name = "Modeled average annual potential evaporation with forcing f2000" ;<br> PET_f2000:units = "m3/s" ;<br> float DisMod_f2000(years, basins) ;<br> DisMod_f2000:long_name = "Modeled Discharge with forcing f2000" ;<br> DisMod_f2000:units = "m3/s" ;<br> float E_cstmean(years, basins) ;<br> E_cstmean:long_name = "Modeled average annual evaporation with forcing cstmean" ;<br> E_cstmean:units = "m3/s" ;<br> float P_cstmean(years, basins) ;<br> P_cstmean:long_name = "Average annual precipitation for forcing cstmean" ;<br> P_cstmean:units = "m3/s" ;<br> float PET_cstmean(years, basins) ;<br> PET_cstmean:long_name = "Modeled average annual potential evaporation with forcing cstmean" ;<br> PET_cstmean:units = "m3/s" ;<br> float DisMod_cstmean(years, basins) ;<br> DisMod_cstmean:long_name = "Modeled Discharge with forcing cstmean" ;<br> DisMod_cstmean:units = "m3/s" ;<br> float E_cstintravar(years, basins) ;<br> E_cstintravar:long_name = "Modeled average annual evaporation with forcing cstintravar" ;<br> E_cstintravar:units = "m3/s" ;<br> float P_cstintravar(years, basins) ;<br> P_cstintravar:long_name = "Average annual precipitation for forcing cstintravar" ;<br> P_cstintravar:units = "m3/s" ;<br> float PET_cstintravar(years, basins) ;<br> PET_cstintravar:long_name = "Modeled average annual potential evaporation with forcing cstintravar" ;<br> PET_cstintravar:units = "m3/s" ;<br> float DisMod_cstintravar(years, basins) ;<br> DisMod_cstintravar:long_name = "Modeled Discharge with forcing cstintravar" ;<br> DisMod_cstintravar:units = "m3/s" ;</p> <p>// global attributes:<br> :author = "Julie Collignan, julie.collignan@lmd.ipsl.fr" ;<br> :model = "ORCHIDEE, IPSL, https://orchidee.ipsl.fr/" ;<br> :source_stations1 = "Global Runoff Data Centre (GRDC), https://www.bafg.de/GRDC/EN/02_srvcs/21_tmsrs/riverdischarge_node.html" ;<br> :source_stations2 = "Ministere de lenergie (February 2021), https://www.hydro.eaufrance.fr/" ;<br> :source_stations3 = "Geoportal of Spain Ministerio (Ministerio de Agricultura, pesca y alimentacion, Ministerio para la transicion ecologica y el reto demografico, 2020" ;<br> :atmospheric_dataset = "GSWP3, Hyungjun, K. (2017), doi: 10.20783/DIAS.501" ;<br> :date = "28/07/2023";<br> }<br> ```</p>
Data for: Differences in mucilage properties and stomatal sensitivity of locally adapted Zea mays in relation with precipitation seasonality and vapour pressure deficit regime of their native environment
<p>Dataset PlantDirect published manuscript:</p> <p>Berauer <em>et al.</em> (2023) - Differences in mucilage properties and stomatal sensitivity of locally adapted Zea mays in relation with precipitation seasonality and vapour pressure deficit regime of their native environment</p> <p>Article DOI: <em><strong>10.1002/pld3.519</strong></em></p> <p> </p> <p>All data is provided within one excel file. Please, pay attention to the provided ReadMe sheet for information on the dataset.</p>
VNMPF-LIS: Validation Network Multiplatform Precipitation Feature (VNMPF) Dataset with International Space Station Lightning Imaging Sensor (ISS LIS) Data
<p>The Multiplatform Precipitation Feature (MPF) database combines ground- and space-based precipitation observations and retrievals from the Global Precipitation Measurement (GPM) mission Validation Network (VN) with space-based lightning measurements from the Lightning Imaging Sensor on board the International Space Station (ISS LIS). The data are synthesized in a thunderstorm-like, feature-based framework that encapsulates the microphysical, kinematic, and electrical properties of the observed storm.<br> <br> A VNMPF includes:</p> <p>- Radar information, GPM orbit, and ISS orbit <br> - Time/date information<br> - Geographical information<br> - Radar reflectivity characteristics<br> - Lightning energetic and identification information (where there is lightning)<br> - 3-dimensional wind information (where radars in dual-Doppler configuration are available)<br> <br> Version 1: 2017-2020</p> <p>Version 2: 2017-2022, updated VN winds </p>
Maps of solar wind plasma precipitation onto Mercury's surface: a geographical perspective
<p>Data Archive to accompany: "Maps of solar wind plasma precipitation onto Mercury’s surface: a geographical perspective." Federico Lavorenti, Elizabeth A. Jensen, Sae Aizawa, Francesco Califano, Mario D’Amore, Deborah Domingue, Pierre Henri, Simon Lindsay, Jim M. Raines, and Daniel Wolf Savin. Submitted 2023 May to Planetary Science Journal.</p> <p>The files contained in this archive comprise the values shown in Figures 3 & 5.</p>
The Role of Operating Conditions in the Precipitation of Magnesium Hydroxide Hexagonal Platelets Using NaOH Solutions
<p>Magnesium hydroxide, Mg(OH)<sub>2</sub>, is an inorganic compound extensively employed in several industrial sectors. Nowadays, it is mostly produced from magnesium-rich minerals. Nevertheless, magnesium-rich solutions, such as natural and industrial brines, could prove to be a great treasure. In this work, synthetic magnesium chloride and sodium hydroxide, NaOH, solutions were used to recover Mg(OH)<sub>2</sub> by reactive crystallization. A detailed experimental campaign was conducted aiming at producing grown Mg(OH)<sub>2</sub> hexagonal platelets. Experiments were carried out in a stirred tank crystallizer operated in single- and double-feed configurations. In the single-feed configuration, globular and nano-flakes primary particles were obtained, as always reported in the literature when NaOH is used as a precipitant. However, these products are not complying with flame retardant applications that require large hexagonal Mg(OH)<sub>2</sub> platelets. This work suggests an effective precipitation strategy to favour crystal growth while, at the same time, limiting nucleation mechanism. The double-feed configuration allowed the synthesis of grown Mg(OH)<sub>2</sub> hexagonal platelets. The influence of reactants flow rates, reactants concentrations and reaction temperature was analyzed. SEM pictures were also taken to investigate the morphology of Mg(OH)<sub>2</sub> crystals. The proposed precipitation strategy paves the road to satisfy flame retardant market requirements.</p>
Variations in the Intensity and Spatial Extent of Tropical Cyclone Precipitation
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Data for: Drivers of wood decay in tropical ecosystems: Termites vs. microbes along spatial, temporal and experimental precipitation gradients
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Data from: Composition of a chemical signalling trait varies with phylogeny and precipitation across an Australian lizard radiation
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Dynamically downscaled 15-minute U.S. West Coast precipitation (Part 2/3)
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Moss species and precipitation mediate experimental warming stimulation of growing season N2 fixation in subarctic tundra
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Data for: Emigration and survival correlate with different precipitation metrics throughout a grassland songbird's annual cycle
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Deconstructing precipitation variability: Rainfall event size and timing uniquely alter ecosystem dynamics (Data)
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Dynamically downscaled 15-minute U.S. West Coast precipitation (Part 1/3)
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Dynamically downscaled 15-minute U.S. West Coast precipitation (Part 3/3)
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Tahoe rain or snow precipitation phase observations
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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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.