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2,322 results for “precipitations”
The CH-IRP data set: fortnightly data of δ2H and δ18O in streamflow and precipitation in Switzerland
<p>The data set contains δ<sup>2</sup>H and δ<sup>18</sup>O from fortnightly grab samples in streamflow and corresponding monthly precipitation derived from interpolation for 23 Swiss hydrological catchments.</p> <p>δ<sup>2</sup>H and δ<sup>18</sup>O in streamflow are provided as one ASCII file for each station. Additionally to these time series each of the files contains the Deuterium excess, the streamflow conditions preceding the sampling as well as the z-scores indicating if a sample might be a statistical outlier, assuming the data are normally distributed. All files contain further information for each sample whether double measurement was performed in the lab comments indicating for instance special sampling conditions or storage-related issues that could alter the isotopic composition due to fractionation.</p> <p>For each data file for streamflow data there is a corresponding ASCII file for catchment precipitation. These contain the interpolated δ<sup>2</sup>H and δ<sup>18</sup>O in precipitation for the catchment as well as the source data that were used to derive the interpolated values.</p> <p>Associated data that can be useful for applications are provided. This is mean areal precipitation and temperature (ASCII files) as well as the topographic catchment boundaries (shape files).</p>
Precipitating Solar Wind Hydrogen at Mars: Improved Calculations of the Backscatter and Albedo with MAVEN Observations
<p>These files contain the derived data products used in the paper, including the penetrating and backscatter energy spectra and directional fluxes. See Readme.txt for a description of the data that is stored in each file.</p>
Antwerp precipitation, open water streams and sewer system sensor data
<p>This csv dataset includes historical data for the period 2018-2020 from multiple sensors deployed in Antwerp that can help city services to have a clear view on the actual precipitation in different regions, the water level of different water flows as well as the water flows in the sewer system of the city. This data was used in CUTLER (visualized in Antwerp’s dashboard) to assist in the impact modelling of garden streets.</p> <p>The data set contains:</p> <p>- 6 water level sensors: lora.0004A30B00202D0C, lora.0004A30B00204B8B, lora.0004A30B00200BFE, lora.0004A30B0021F1D4, lora.0004A30B002041F6, lora.0004A30B001FC6DF</p> <p>- 4 pluvio meters: lora.0004A30B002025F5, lora.0004A30B00201DCC, lora.0004A30B001FF6F7, lora.0004A30B001FA140<br> <br> - 3 sewer level meters: lora.0004A30B001FD07B, lora.0004A30B0020112D, lora.0004A30B001F9B4B</p>
Visualization of Dissolution-Precipitation Processes in Lithium-Sulfur Batteries: Supporting Data
<ul> <li>Contours_1.gif: 0 mA/g - pristine state</li> <li>Contours_2.gif: 30 mA/g</li> <li>Contours_3.gif: 80 mA/g</li> <li>Contours_4.gif: 130 mA/g</li> <li>Contours_5.gif: 180 mA/g</li> <li>Contours_6.gif: 230 mA/g</li> <li>Contours_7.gif: 330 mA/g - no remaining solid sulphur</li> </ul>
Seasonal Precipitation and Temperature Data in Canberra, Australia
<p>This dataset contains the precipitation, mean maximum temperature and mean minimum temperature data used in the study Application of Machine Learning to Attribution and Prediction of Seasonal Precipitation and Temperature Trends in Canberra, Australia. This data was originally from the Australian Bureau of Meteorology Climate Data Online (http://www.bom.gov.au/climate/data/index.shtml), but has been updated to have missing values (1% of data) filled using a moving average centred on the year for which the data is missing. <br> <br> Below is the abstract for the paper.</p> <p>Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn precipitation. However, annual precipitation remains stable as summer precipitation increased and the other seasons show no trend. Further, mean temperature increases in all seasons. These results suggest that Canberra is increasingly vulnerable to drought. Wavelet analysis suggests that the El-Niño Southern Oscillation (ENSO) influences precipitation and temperature in Canberra, although its impact on precipitation has decreased since the 2000s. Linear regression (LR) and support vector regression (SVR) were applied to attribute climate drivers of annual precipitation and mean maximum temperature (TMax). Important attributes of precipitation include ENSO, the southern annular mode (SAM), Indian Ocean Dipole (DMI) and Tasman Sea SST anomalies. Drivers of TMax included DMI and global warming attributes. The SVR models achieved high correlations of 0.737 and 0.531 on prediction of precipitation and TMax, respectively, outperforming the LR models which obtained correlations of 0.516 and 0.415 for prediction of precipitation and TMax on the testing data. This highlights the importance of continued research utilising machine learning methods for prediction of atmospheric variables and weather pattens on multiple time scales.</p>
Precipitation Efficiency
<p>This dataset contains processed precipitation efficiency data derived from six global storm-resolving models. The calculation of the precipitation efficiency index follows the methodology outlined in Li et al. (2002a)</p>
Datasets for the article "Moisture source controls on water isotopes in Antarctic precipitation - insights from water tracers in ECHAM6-wiso"
<p>This is the dataset used for the manuscript Qinggang Gao, Louise C Sime, Alison J Mclaren, et al. Moisture source controls on water isotopes in Antarctic precipitation -insights from innovative water tracers in ECHAM6-wiso. <em>ESS Open Archive .</em> December 10, 2024. DOI: 10.22541/essoar.173386109.93218804/v1.</p> <p>The corresponding code used for the manuscript can be found at https://github.com/l975421700/a_basic_analysis.</p>
StageIV-IRC – A High-resolution Dataset of Extreme Orographic Quantitative Precipitation Estimates (QPE) Constrained to Water Budget Closure for Historical Floods in the Appalachian Mountains
<h2>Quantitative Flood Estimation (QFE) in complex terrain remains a grand challenge in operational hydrology due to the lack of accurate high-resolution Quantitative Precipitation Estimates (QPE) at spatial and temporal resolutions needed to capture the variability of orographic precipitation, and where radar-based QPE are available there are significant biases due to the geometry and constraints of radar operations. Here, we present a high-resolution (i.e. 250m, 5minute-hourly) QPE dataset for the most extreme (flood-producing) events from 2008 to 2024 for 26 gauged basins (in total 215 events) in the Appalachian mountains constrained to meet basin-scale water budget closure through inverse rainfall-runoff modeling to correct the Next Generation Weather Radar (NEXRAD) Stage IV analysis (4km resolution, hourly) using a fully-distributed uncalibrated hydrological model that leverages recent advances in hydrologic modeling in mountainous regions (e.g. improved river routing and initial soil moisture estimation) (Liao and Barros, 2024a and 2024b). The corrected Stage IV analysis is referred to as StageIV-IRC (Inverse Rainfall Correction). Previously, a subset of this dataset informed the construction of a generalized QPE error model (Liao and Barros, 2023), supporting the development of water budget closure constrained QPE and providing physics insights into orographic QPE uncertainties for various radar-based products at high resolution in complex terrain. The unique advantage of the StageIV-IRC QPE is that it achieves water budget closure at the storm-flood event scale within observational uncertainty of streamflow observations, that is the golden standard in hydrological modeling. The QPE dataset is publicly available at: <a href="https://doi.org/10.5281/zenodo.14028867">https://doi.org/10.5281/zenodo.14028867</a></h2> <p><strong> </strong></p>
Great Lakes Coordinated Monthly Precipitation Data
<p>Coordinated Monthly Precipitation Data Information (1900-2023)</p> <p>This dataset consists of the coordinated amount of monthly precipitation that has fallen over the basin areas of the different Great Lakes. The data are presented in both mm and inches. Most of the processing is done by the US Army Corps of Engineers Detroit Office using models developed by that agency and the Great Lakes Environmental Research Lab (GLERL) of NOAA. The data is then verified by Environment and Climate Change Canada.</p> <p>Different methods used to calculate monthly precipitation for certain periods in record period. For more information on data sources reference this paper: *<a href="http://www.glerl.noaa.gov/pubs/fulltext/2015/20150006.pdf">https://www.glerl.noaa.gov/pubs/fulltext/2015/20150006.pdf</a></p> <p>An advancement made in most recent period, beginning in 1948. Data from 1948 to present calculated through AHPS model with GLERL DTP method. GLERL DTP method uses a group of meteorological stations surrounding the Great Lakes basin to calculate precipitation. In a recent study, it was found that some of the stations in the group were reporting erroneous data in recent years. Therefore, station list updated.</p> <p>Also, AHPS model replaced with GLSHFS model. GLSHFS uses same method for calculating precipitation. With the model change to GLSHFS and the method's (GLERL DTP) improved station list, precipitation data updated back to 1948. </p> <p><strong>Period Current Method** Previous Method**</strong> </p> <p>1900-1930 USACE-AWD USACE-AWD</p> <p>1931-1947 GLERL-MTP GLERL-MTP</p> <p>1948-2017 GLERL-DTP (GLSHFS) GLERL-DTP (AHPS)</p> <p>2018-YYYY GLERL-DTP (GLSHFS) </p> <p>*Citation:</p> <p>Hunter, T. S., Clites, A. H., Campbell, K. B., & Gronewold, A. D. (2015). Development and application of a North American Great Lakes hydrometeorological database—Part I: Precipitation, evaporation, runoff, and air temperature. Journal of Great Lakes Research, 41(1), 65-77.</p> <p>**Acronyms</p> <p>AHPS: Advanced Hydrologic Prediction System</p> <p>AWD: Areally Weighted District</p> <p>DTP: Daily Thiessen Program</p> <p>GLERL: Great Lakes Environmental Research Laboratory</p> <p>GLSHFS: Great Lakes Seasonal Hydrological Forecasting System</p> <p>MTP: Monthly Thiessen Polygon</p> <p>USACE: United States Army Corps of Engineers</p>
Processed ERA5, IMERG and TRMM PR/GPM DPR precipitation data for Nicolas & Boos - "Understanding the spatiotemporal variability of tropical orographic rainfall using convective plume buoyancy."
<p>The dataset contains processed data from large datasets that are freely available online. <br>All data cover the period 01/2001 - 12/2020. The file names describe the months & region that each file contains. Variable codes for ERA5 data (all files starting in e5.) are:</p><p> - 228_246_100u : 100m u-wind<br> - 228_247_100v : 100m v-wind<br> - qL : 900-600hPa averaged specific humidity<br> - thetaeb : surface - 900hPa averaged equivalent potential temperature<br> - thetaeL : 900-600hPa averaged equivalent potential temperature<br> - thetaeLstar : 900-600hPa averaged saturation equivalent potential temperature<br> - tL : 900-600hPa averaged temperature<br> - uBL : surface - 900hPa averaged u wind<br> - vBL : surface - 900hPa averaged v wind<br> - 128_034_sstk : sea surface temperature<br> - 162_071_viwve : eastward component of vertically integrated water vapor transport<br> - 162_072_viwvn : northward component of vertically integrated water vapor transport</p><p> </p>
Dataset to "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " by Zmijewski, Dziekan & Pawlowska
<p>The archive contains datasets, run scripts, time series and plotting scripts used when preparing the paper: P. Zmijewski, P. Dziekan and H. Pawlowska "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " submitted to Geoscientific Model Development in March 2023.</p>
Hydrological controls of slope response to precipitation - Code and Data
<p>This repository contains the dataset and codes used in the study of sloping soil response to precipitation through machine learning analysis. The dataset includes synthetic data of precipitation, soil moisture, and groundwater level mimicking field observations conducted in a experimental field. The codes include scripts for data preprocessing, analysis, and visualization. Here you will find: The dataset used to build a random forest (RF) model (01_RF_dataset.csv), the script for building the model (01_RF_model.py) using the sciki-learn library in Python (<a href="https://scikit-learn.org/stable/index.html">https://scikit-learn.org/stable/index.html</a>), the dataset for the cluster analysis (SyntheticData.mat) and the script for the analysis using the k-means clustering technique implemented in Matlab (<a href="https://it.mathworks.com/help/stats/kmeans.html">https://it.mathworks.com/help/stats/kmeans.html</a>).</p><p>The data and the codes in the present repository are part of the research entitled "Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data", published in Hydrology and Earth System Sciences - HESS journal. More details can be found for now in the paper preprint: Roman Quintero DC, Marino P, Santonastaso GF, Greco R (2023). Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data. EGUsphere: 1-41. DOI: 10.5194/EGUSPHERE-2022-1078</p>
A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset
<p>Tropical cyclone precipitation (TCP) is a key diagnostic in the context of atmospheric science, hazard, risk and flood research. This dataset provides estimates of TCP from global datasets. The various TCP metrics reported were estimated through the analysis of the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation product and the International Best Track Archive for Climate Stewardship (IBTrACS) version 4. There are two main files that comprise the dataset. The main dataset file includes information on the mean and maximum TCP found within 500 km of each storm centre as well as the rainfall area and radius of maximum rain. The second file includes the estimates of azimuthally averaged precipitation using a 10 km bin spacing which is useful for analyses of the storm-scale structure of precipitation.</p>
Precipitation-temporal-clustering-and-Italian-landslides
<p>This repository contains results about the spatial and temporal distribution of temporal clustering of precipitation and analysis of landslides triggers based on movement types over Italy.</p>
Video Supplement for "Understanding the dependence of mean precipitation on convective treatment and horizontal resolution in tropical aquachannel experiments"
<p>A time series of snapshots of precipitable water (shading) and rainfall rate (contour) in the tropical aquachannel simulations. </p>
4DMED precipitation product: 1 km merged precipitation from CPC, IMERG-LR and SM2RAIN-ASCAT for mediterranean basin
<p>The high resolution satellite precipitation product is based on the integration of multiple precipitation and rainfall datasets to generate a high spatial (1 km) and temporal (daily) resolution precipitation product over the Mediterranean area. The following precipitation and rainfall datasets are downscaled and merged together: GPM-Late run, CPC, SM2RAIN-ASCAT. All these products are originally at coarse spatial resolution (>10 km) and have been downscaled to 1 km spatial resolution using CHELSA (1 km) climatology. The three products are then merged with a triple collocation technique. In the areas covered by snow only GPM-Late run and CPC are merged together, again using the triple collocation to obtain the relative weights (third product: ERA5 Land precipitation). The product has been developed in the framework of the 4D-MED Hydrology project. The product is available in the period 2015-2022. </p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The work is supported by the European Space Agency (ESA) through the 4D-MED Hydrology project (grant no. ESA 4000136272/21/I-EF)</p>
Data for: Drivers of wood decay in tropical ecosystems: Termites vs. microbes along spatial, temporal and experimental precipitation gradients
<ol> <li>Models estimating decomposition rates of dead wood across space and time are mainly based on studies carried out in temperate zones where microbes are dominant drivers of decomposition. However, most dead wood biomass is found in tropical ecosystems, where termites are also important wood consumers. Given the dependence of microbial decomposition on moisture with termite decomposition thought to be more resilient to dry conditions, the relative importance of these decomposition agents is expected to shift along gradients in precipitation that affect wood moisture.</li> <li>Here, we investigated the relative roles of microbes and termites in wood decomposition across precipitation gradients in space, time and with a simulated drought experiment in tropical Australia. We deployed mesh bags with non-native pine wood blocks, allowing termite access to half the bags. Bags were collected every six months (end of wet and dry seasons) over a four-year period across 5 sites along a rainfall gradient (ranging from savanna to wet sclerophyll to rainforest) and within a simulated drought experiment at the wettest site. We expected microbial decomposition to proceed faster in wet conditions with greater relative influence of termites in dry conditions.</li> <li>Consistent with expectations, microbial-mediated wood decomposition was slowest in dry savanna sites, dry seasons, and simulated drought conditions. Wood blocks discovered by termites decomposed 16% to 36% faster than blocks undiscovered by termites regardless of precipitation levels. Concurrently, termites were 10 times more likely to discover wood in dry savanna compared with wet rainforest sites, compensating for slow microbial decomposition in savannas. For wood discovered by termites, seasonality and drought did not significantly affect decomposition rates.</li> <li>Taken together, we found that spatial and seasonal variation in precipitation are important in shaping wood decomposition rates as driven by termites and microbes, although these different gradients do not equally impact decomposition agents. As we better understand how climate change will affect precipitation regimes across the tropics, our results can improve predictions of how wood decomposition agents will shift with potential for altering carbon fluxes.</li> </ol>
Data for "Leveraging a Disdrometer Network to Develop a Probabilistic Precipitation Phase Model in Eastern Canada"
<p><a name="_Toc157072893"></a><strong>Abstract</strong>. This study presents a probabilistic model that partitions the precipitation phase based on hourly measurements from a network of radar-based disdrometers in eastern Canada. The network consists of 27 meteorological stations located in a boreal climate for the years 2020-2023. Precipitation phase observations showed a 2-m air temperature interval between 0-4°C where probabilities of occurrence of solid, liquid, or mixed precipitation significantly overlapped. Single-phase precipitation was also found to occur more frequently than mixed-phase precipitation. Probabilistic phase-guided partitioning (PGP) models of increasing complexity using random forest algorithms were developed. The PGP models classified the precipitation phase and partitioned the precipitation accordingly into solid and liquid amounts. PGP_basic is based on 2-m air temperature and site elevation, while PGP_hydromet integrates relative humidity. PGP_full includes all the above data plus atmospheric reanalysis data. The PGP models were compared to benchmark precipitation phase partitioning methods. These included a single temperature threshold model set at 1.5°C, a linear transition model with dual temperature thresholds of –0.38 and 5°C, and a psychrometric balance model. Among the benchmark models, the single temperature threshold had the best classification performance due to a low count of mixed-phase events. The other benchmark models tended to over-predict mixed-phase precipitation in order to decrease partitioning error. All PGP models showed significant phase classification improvement by reproducing the observed overlapping precipitation phases based on 2-m air temperature. In terms of partitioning error, PGP_full had the lowest RMSE and the least variability in performance. The RMSE of the single temperature threshold model was the highest and showed the greatest performance variability. The improvement of mixed-phase prediction remains a challenge. This study establishes a basis for integrating automated phase observations into a hydrometeorological observation network and developing probabilistic precipitation phase models.</p>
Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming
<p>Dataset used to analyize role of climate variability</p>
Data for the publication "Radiative effects of precipitation on the global energy budget and Arctic amplification"
<p>This dataset includes a set of 15yr simulations using the MIROC6 global aerosol-climate model with 1) diagnostic precipitation, 2) prognostic precipitation without radiative effect of precipitation, and 3) prognostic precipitation with radiative effect of precipitation.</p> <p>The data are used in the manuscript entitled "Radiative effects of precipitation on the global energy budget and Arctic amplification".</p>
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International Brain Laboratory public data
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OpenNeuro
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