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67 results for “extreme precipitation”
The HEC method processing code and output for constrained precipitation extremes reveal unequal future socioeconomic exposure
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Data from: Does previous exposure to extreme precipitation regimes result in acclimated grassland communities?
<p>This dataset is related to a journal paper published in Science of The Total Environment <a href="https://doi.org/10.1016/j.scitotenv.2022.156368">https://doi.org/10.1016/j.scitotenv.2022.156368</a>.</p>
Data supplementing article "Dramatic estuarine response to a hurricane with extreme precipitation: Hurricane Harvey in Galveston Bay"
<p>This is data set used to plotting the figure that describes the relationship between surface landward current at buoy station g06010 and section-averaged landward current at the entrance of Galveston Bay. </p> <p> </p>
Extreme precipitation event over Henan in July 2021
<p>HYSPLIT-generated trajectories associated with extreme precipitation evnets over Henan in July 2021 and August 1975.</p>
Total column moisture and vertical velocity for two cases of extreme precipitation
<p>Total column water (filled) and vertical velocity at 500 hPa (red—descending motions, blue—ascending) for 1st case (8-9 July 1994) and 2nd case (6-7 July 2001)<strong>.</strong></p>
Date of extreme precipitating events over Burkina Faso
<p>The present ASCII file provides the dates of the daily extreme precipitating events (EPEs) for each of the 15 1°x1° pixels covering Burkina Faso as detected by Sanogo et al. (2022). The original raingauge dataset was provided by the Burkinabe National Meteorological Agency (Agence Nationale de la Météorologie). It consists in 142 raingauge stations covering most of Burkina Faso and providing daily rainfall accumulation over the period 1995–2016. Raingauge data were aggregated in 1°x1° pixels, and only pixels with at least five stations were considered. EPEs are detected for each of these pixels as the days when rainfall exceeds the 99th all-day percentile computed over the 1995-2016 period (Note Sanogo et al. 2022 used the period 2001-2013 for consistency with other datasets). See Sanogo et al. 2022 for details.</p> <p>The present dates serve as a basis for the composite analysis in Peyrillé et al. (2023).</p> <p>The ASCII file is organised as follows: one line per event, with latitude (degrees north), longitude (degrees east), year, month and day separated by spaces. Latitude and longitude refer to the center of each 1°x1° pixel.</p> <p>Peyrillé, P., R. Roehrig, and S. Sanogo, 2023: Tropical Waves are key drivers of Extreme Precipitation Events in the Central Sahel. Submitted to Geophysical Research Letters.</p> <p>Sanogo, S., P. Peyrilé, R. Roehrig, F. Guichard, F., and O. Ouedraogo, 2022: Extreme precipitating events in satellite and rain gauge products over the Sahel. Journal of Climate, 35(6), 1915– 1938. <a href="https://doi.org/10.1175/JCLI-D-21-0390.1">https://doi.org/10.1175/JCLI-D-21-0390.1</a></p>
Changes of the annual and seasonal extreme precipitations over Southeastern Europe - source datasets
<p>Source files: ERA5 land - highest one day precipitation amount (RX1) for seasons December-January-Februrary (DFJ), March-April-May (MAM), June, July, August (JJA), September-October-November (SON) Mann Kendall trends and p-values calculated from 1961 to 2020 in TXT, Microsoft Excell (XLSX) and ESRI Shapefile format.</p>
Data from: Guidelines and considerations for designing field experiments simulating precipitation extremes in forest ecosystems
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Data from: Extreme precipitation variability, forage quality and large herbivore diet selection in arid environments
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Increased frequency of extreme precipitation events in the North Atlantic during the PETM: Observations and theory
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Impact of horizontal resolution and model time step on European precipitation extremes in the OpenIFS 43r3 atmosphere model
<p>Python scripts used for GMD paper</p>
Impact of horizontal resolution and model time step on European precipitation extremes in the OpenIFS 43r3 atmosphere model
<p>Python scripts used for GMD paper</p>
Influence of precipitation accumulation and extremes on leptospirosis cases in Colombia
<p>Environmental variables like temperature and precipitation pose a direct influence on waterborne and vector-borne diseases like leptospirosis, malaria, dengue, among others (Kifle, M. M., et. al, 2019; Andre-Fontaine, G., Aviat, F., & Thorin, C., 2015; Mantilla, G., Oliveros, H., & Barnston, A. G., 2009). Therefore, the climatic forcing over those variables is key to understand the contagion dynamics. Leptospirosis is a zoonotic disease caused by bacteria of the genus Leptospira that affects humans, domestic animals, and wildlife. In Colombia, 85% of leptospirosis cases are concentrated in 10 of its 32 departments (Arias-Monsalve, Salas-Botero, & Donalisio, 2021). Methods: Annual cycle of leptospirosis cases at national and departmental level were computed and compared with changes in number of cases occurred during the different phases of El Niño Southern Oscillation-ENSO (Neutral, El Niño and La Niña (Arias-Monsalve & Builes, 2019). Additionally, for municipalities with the highest number of cases, the leptospirosis Relative Risk based on the incidences computed for the days exceeding thresholds of accumulated rainfall at 7, 14, and 21 lags was estimated. Results: Regarding the influence of extreme rainfall events as the ones presented during El Niño and La Niña phases of ENSO, we found that at the national level, the monthly number of cases raised a 25% during La Niña and decreased by 17% during El Niño. At the departmental level, we detected an increase of cases in both phases of ENSO, depending on the location in the country. With respect to the accumulation of rainfall that occurs during the different seasons in the country, we found a statistically significant association between excess rainfall and leptospirosis relative risk higher than 1 at different lags for cities in the Orinoco, Caribbean, Pacific, and Andes regions. Conclusions: In Colombia, there is a relationship between leptospirosis and accumulated rainfall as well as with the excess and lack of rainfall related to ENSO. The contrasting results from each spatial and temporal scale, reinforce that leptospirosis is a multidimensional disease with high complex interactions among its determinants.</p>
Appendix of "Extreme Precipitation Formation in the south Siberia and Mongolia: Wave Propagation Patterns and zonal Temperature Gradient Changes"
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Globally observed annual extreme daily and persistent precipitation relative totals
<p>To provide the most comprehensive analysis of observed global extreme daily and persistent precipitation, we use high-quality daily precipitation data from a number of different sources. Co-authors from fifteen countries contributed daily data, most of which until now were not available for global precipitation studies. The compilation of global daily precipitation data includes the GHCND dataset (https://www.ncdc.noaa.gov/ghcn-daily-description), the ECA&D dataset (https://www.ecad.eu/), the USHCN dataset (http://cdiac.ess-dive.lbl.gov/ftp/ushcn_daily/), and the dataset for Canada (http://climate.weather.gc.ca/), raw data provided by authors from Argentina, Australia, Benin, Brazil, China, India, Japan, Korea, Mongolia, New Zealand, Pakistan, South Africa, Saudi Arabia, Spain, and Russia. In total 12151 stations were collated. After quality control and homogeneity test, 6125 high-quality stations with long-term (data are available at least for 45 years) daily precipitation for the period 1961-2010 are remained. </p> <p>The 95<sup>th</sup> percentile of daily and persistent precipitation series on wet days (≥ 1 mm) is used to identify daily and persistent extremes, respectively. The base period for percentile calculation is 1961-2010. Considering regional precipitation characteristics, the ‘relative total’ used here is not the simple precipitation amount, but a relative measure (%) associated with the local threshold of ‘extremity’ (i.e. the 95<sup>th</sup> percentile) and the total extreme precipitation amount. The relative total of extreme precipitation is defined as the mean precipitation amount exceeding the threshold divided by the corresponding threshold. This dataset contains the annual extreme precipitation relative totals for the 6125 stations.</p>
DATA for Land Surface Model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v.3.9 model over North America
<p>Code and daily temperature and precipitation outputs used to estimate climate extreme indices in García-García et al. 2020.</p> <p> </p> <p>García-García A., Cuesta-Valero F.J., Beltrami H., González-Rouco J.F., García-Bustamante E., and Finnis J. "Land Surface Model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v.3.9 model over North America" submitted to Geocientific Model Development. 2020. </p>
Data and code for Synoptic mechanisms of large-scale extreme precipitation events over southeastern China (Version 1.1.0)
<p>All data generated for the article <em>Synoptic mechanisms of large-scale extreme precipitation events over South China</em> and the code used to reproduce the article analysis and figures.</p>
The dataset of article "Analysis of Uncertainties and Associated Convective Processes in Simulations of Extreme Precipitation over Cities with a Regional Earth System Model: A Case Study"
<p>The "out<i>EXP02.nc" and "out</i>EXP11.rar" are two examples of 11 simulations' output file in the article, in each of the file, 9 variables (horizontal wind u, horizontal wind v, vertical speed, height, temperature, pressure, precipitation, longitude and latitude) from outputs of simulation are included, the time interval is 2 hours.</p><p>The MERRA-2 dataset provides the initial and boundary conditions of chemical fields in simulations.</p><p>The era5 dataset provides the meteorological initial and boundary conditions in simulations.</p><p> </p>
Extreme precipitation increases the productivity of a desert ephemeral plant community in Central Asia, but there is no slope position effect
<p>Extreme precipitation increases the productivity of a desert ephemeral plant community in Central Asia, but there is no slope position effect</p>
High Mountain Asia TRMM-derived 3B42 Extreme Precipitation Indices V001
This data set features seven standard annual mean extreme precipitation indices: Rx1day, Rx5day, CWD, R10mm, R20mm, R95pTOT, and R99pTOT. They were selected on the basis of potential relevance to landslide activity from the 27 indices established by the joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI). The seven indices were calculated from 3B42 version 7 daily satellite precipitation estimates.
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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.