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2,322 results for “precipitations”
Asymmetric responses of abundance and diversity of N-cycling genes to altered precipitation in arid grasslands
<p>Precipitation changes exert a fundamental effect on the nitrogen (N) cycle in water-limited grasslands. Soil microbes are essential drivers of N cycle, and the rates and their stabilities of interrelated N-cycling processes are reflected by the abundance and diversity of N-cycling genes. Yet, little is known about how altered precipitation affects the genes involved in the entire N-cycling pathways.</p> <p>By combining a 6-year precipitation manipulation experiment (-30%, ambient, +30%, +50%) with metagenomic sequencing, we investigated the responses of N-cycling gene abundance and diversity to altered precipitation at two soil depths (0-10 and 30-50 cm).</p> <p>We found that increased precipitation enhanced the abundance of numerous key genes, leading to an acceleration of N turnover, but decreased the diversity of ammonium assimilation genes. Decreased precipitation did not reduce abundance or diversity of N-cycling genes. Most N-cycling genes showed generally consistent responses to altered precipitation in the topsoil (0-10 cm) and subsoil (30-50 cm), albeit with clear distinctions in both abundance and diversity by soil depth. These precipitation-specific responses and depth-dependent variabilities of functional genes were attributed to the distinct taxonomic composition of each N-cycling gene. Further, we quantified gross N transformation rates and found that they were well predicted by the abundance of most N-cycling genes (e.g., genes involved in ammonium assimilation and nitrification).</p> <p>Our study sheds new light on the soil N cycle under precipitation alterations from the perspective of individual gene abundance and diversity, and shows that future increases in precipitation could accelerate soil N turnover in arid and semi-arid lands. </p>
Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics
<p><strong>Dataset for "Data-driven empirical conductance relations during auroral precipitation using incoherent scatter radar and all sky imagers" JGR-Space Physics.</strong></p> <p>Processed ACF level data can be provided by contacting me.</p> <p><strong>DOI of the publication:</strong></p> <p><strong>README file:</strong></p> <p>PFISRInversions_ASI_v1.1_Fang_01042023_v08162023.h5</p> <p>HallConductance: Altitude integrated Hall Conductance from 85-150 km altitude, [ntime], mho<br> PedersenConductance: Altitude integrated Pedersen Conductance from 85-150 km, [ntime], mho<br> EnergyFlux: Energy flux after integrating the differential number flux, [ntime], W/m^2<br> AverageEnergy: Average Energy after integrating the differential number flux, [ntime], eV<br> Measured_ElectronDensity: measured electron density from PFISR, [ntime, naltitude], #/m^3<br> Modeled_ElectronDensity: modeled electron density produced by the MEM version, [ntime,naltitude], #/m^3<br> UnixTime: time in seconds since 1970-01-01 00:00:00 UT, [ntime], seconds<br> NumberFlux: differential number flux, [ntime, nenergy], #/m^2 s^-1 eV^-1<br> EnergyGrid: energy grid spanning 1 keV - 100 keV in 25 steps, [nenergy], eV<br> ASIStatus: auroral identification number, [ntime], no units<br> 1: Discrete aurora<br> 2: Diffuse aurora<br> 3: Pulsating aurora<br> 8: Unidentified aurora<br> -1: data that was flagged as unsuitable:<br> either TEC was too low (no auroral E-region possibly associated with red aurora),<br> the modeled electron density was not consistent at all with the observed electron density (bad fit)</p> <p>ASIYEAR-Final_date.xlsx<br> This is an excel file that contains the original auroral image identification near the zenith direction<br> We used all sky imager videos located: http://optics.gi.alaska.edu/realtime/data/MPEG/PKR_DASC_256/<br> These files were converted into python pickle files and used internally for the rest of the investigation.</p> <p>The columns corresponds to days, and the rows correspond to time in UT as decimal hours:<br> The ASI Key is the following:<br> # 1 Discrete<br> # 2 Diffuse<br> # 3 Pulsating<br> # 4 Cloudy/Clear<br> # 5 Moon<br> # 6 Possible faint Aurora with moon out<br> # 7 Substorm Breakup<br> # 8 Cloudy with aurora (can't make out type)</p>
Baixada Santista 2020 event analysis - Attribution and Synopsis of Landslide Impacts from Precipitation
<p>Dataset used for the Baixada Santista 2020 event analysis during the Attribution and Synopsis of Landslide Impacts from Precipitation (ASLIP) Workshop as part of the Climate Science for Service Partnership Brazil. </p> <p>Notes:</p> <p>impact_data: data from land cover change (retrieved from https://brasil.mapbiomas.org/), socioeconomic impacts (https://s2id.mi.gov.br/) and susceptibility to landslides (https://www.cprm.gov.br/)</p> <p>df_rxmax_climat: data from HadGEM3 (15 members) and CPC for the climatological period of study</p> <p>df_rxmax_event: data from HadGEM3 (525 members) and CPC for the event occurred in March 2020</p> <p>pluviometer_analysis_historical: data processing scripts and precipitation observational data</p> <p>soilMoistureCPC_data: mean integrated soil moisture data from SMAP and precipitation data from CPC for the studied area</p>
Precipitation reconstructions for Paris based on the observations by Louis Morin, 1665–1713 CE
<pre>This dataset includes Louis Morin's daily rainfall records from 1665--1713. More details can be found in the file.</pre>
Dataset for: The role of hydrides and precipitates on the strain localisation behaviour in a zirconium alloy
<p><strong>This is the dataset used in the following publication: </strong></p> <p> </p> <p>R. Thomas, D. Lunt, M.D. Atkinson, J. Quinta da Fonseca, M. Preuss, P. Honniball, P. Frankel, The role of hydrides and precipitates on the strain localisation behaviour in a zirconium alloy, Acta Materialia, 2023, 119327, ISSN 1359-6454, https://doi.org/10.1016/j.actamat.2023.119327.</p> <p><strong>Contained in this dataset are:</strong></p> <p>A Jupyter notebook which uses the open-source DefDAP Python package (https://github.com/MechMicroMan/DefDAP) to open enclosed HRDIC, EBSD and image data for hydrides Zircaloy-4 deformed to ~3% strain, along the rolling direction.</p> <p>Please use the 'master' version of DefDAP: <a href="https://github.com/MechMicroMan/DefDAP/tree/51074e158b0131c69358ddf7eee319e41cf582ca">https://github.com/MechMicroMan/DefDAP/tree/51074e158b0131c69358ddf7eee319e41cf582ca</a></p> <p><strong>Publication abstract:</strong></p> <p>In service, zirconium alloys undergo aqueous corrosion and hydrogen is absorbed, which can lead to the formation of hydrides. Hydrides, as well as the precipitates in zirconium alloys, affect the mechanical performance, though their contribution to plasticity is not yet understood. This study uses a combination of high-resolution digital image correlation and electron backscatter diffraction to quantify the strain partitioning between the different phases. Following uniaxial tensile deformation, it was found that the average strain within the δ-ZrH and Zr(Fe,Cr)2 was lower than in the α-Zr. Shear bands in the α-Zr matrix were observed to interact with small and medium sized hydride in multiple ways, such as terminating at the interface, cracking and shear around the interface or causing plastic slip in the hydride. Large hydrides showed a particularly detrimental effect on deformation behaviour, with large strain localisations and cracking observed at interfaces, which are typically precursors to failure. In contrast, the Zr(Fe,Cr)2precipitates remained undeformed and forced the expected metal-matrix lattice rotation. The importance of these observations in context of fuel cladding integrity is discussed.</p>
Dataset for "Scanning precession electron diffraction data analysis approaches for phase mapping of precipitates in aluminium alloys"
<p>Data needed to reproduce the results in "Scanning precession electron diffraction data analysis approaches for phase mapping of precipitates in aluminium alloys" published in Ultramicroscopy. The codes and notebooks can be found at 10.5281/zenodo.8321258.</p>
2010_2024_ERA5_Precipitation_Rainfall_FourierProcessed_1k_WG
<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 whole world<br>This series of ERA5 data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2010 to 2024. </p> <p> </p> <p>Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting for 2010 - 2022. Abstract: Precipitation from the ERA5 reanalysis archive supplied by the European Centre for Medium Range Weather Forecasting. The original data is at 0.25 degree resolution and was downscaled by ERA extraction algorithms to 1km resolution, then downloaded. The daily data have been aggregated to 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) at 1 km resolution 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. Sea pixels were masked with a VIIRS land/sea layer. </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 -180.0000000000000000,-90.0000000000000000 : 179.9999999999998295,89.9999999999999147</p> <h4>File names:</h4> <p><br>The wg at the start of each file name indicates that the image covers the whole world in the E4warning 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>
High-spatial-resolution monthly precipitation dataset over China during 1901–2017
<p>The dataset with 0.5 arcminute (~1 km) was spatially downscaled from CRU TS v4.02 based on Delta downscaling method, including monthly precipitation from 1901.1 to 2017.12. The dataset covers the main land area of China. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China.</p> <p>Another data download site is Loess plateau Scientific Data Center (http://loess.geodata.cn/). This is a Chinese website. This website publishes the updated histrorical dataset and future downscaled monthly precipitation under multiple SSP Scenarios and GCMs, with 1 km spatial resolution.</p> <p>/*************/ The dataset is updated yearly. Now, the period of the dataset is from 1901.1 to 2020.12.</p> <p>/*************/ The future 1km dataset from 2021-2100 is published.</p> <p><br> The data provider recommended the below publication as the reference.<br> Peng Shouzhang, Ding Yongxia, Liu Wenzhao, Li Zhi. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 2019, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019.</p>
Vaped Marijuana to Attenuate Naloxone-Precipitated Withdrawal
ClinicalTrials.gov study NCT05114460. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Urinary Alkalinization on Urine Uric Acid Precipitation and Crystallization in Adults With Type 1 Diabetes
ClinicalTrials.gov study NCT02502071. IPD Sharing: Not stated. Countries: 1. Publications: 12.
Leveraging spatial patterns in precipitation forecasts using deep learning to support risk-averse flood management
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Data from: Precipitation timing and soil substrate drive phenology and fitness of Arabidopsis thaliana in a Mediterranean environment
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Variations in the Intensity and Spatial Extent of Tropical Cyclone Precipitation
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Data from: Precipitation drives the abundance and distribution of Arctia virginalis: A 40-year study
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Data from: Grazing effect on grasslands escalated by abnormal precipitations in Inner Mongolia
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Data from: Structural diversity shifts from negative to positive associations with forest productivity via basal area, stand age, and precipitation thresholds
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Effects of reduced precipitation and advanced spring phenology on intra- and interspecific competition between beech, oak and linden saplings
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Carbon and nitrogen dynamics in subsoils after 20 years of added precipitation in a Mediterranean grassland
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Crowdsourced data reveal shortcomings in precipitation phase products for rain and snow partitioning
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Precipitation regime controls bryosphere carbon cycling similarly across contrasting ecosystems
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