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67 results for “Precipitation Extremes”
Extreme Precipitation Potential and Slow-moving Extreme Precipitation Potential
<p>Extreme Precipitation Potential (EPP) and Slow-moving Extreme Precipitation Potential (SEPP) are described in Kahraman et al. paper "Quasi-stationary intense rainstorms spread across Europe under climate change".</p> <p> </p> <p>File names as "identifier+YYYY+MM+.nc".<br> </p> <p>EPP count per month for current (identifier=hvpraj) and future (identifier=hvprak) climate.</p> <p>SEPP count per month for current (identifier=hvprslowaj) and future (identifier=hvprslowak) climate.</p> <p>YYYY=simulation year</p> <p>MM=simulation month</p> <p>".nc"=netcdf extension</p>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
Elevated increase in compound extreme heat-precipitation events over China
<p>This file contains the fractions (in percentage) of the compound extreme precipitation events that are preceded by an extreme heat event in China during 1961-2017. The compound events are identified based on the CN05.1 dataset at 0.5x0.5 resolution. Please contact us with any questions or concerns (email: luo.ming@hotmail.com).</p>
ARISE-SAI_1.5 : CESM2 Extreme Precipitation and Temperature Indices
<p>Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) is a set of simulations carried out with the Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model, version 6 (CESM2(WACCM6)) that aims at simulating a plausible deployment of solar climate intervention of stratospheric aerosol injection to enable community assessment of responses of the Earth system.</p> <p>This dataset uses the first set of simulations, called ARISE-SAI-1.5, that utilized the middle-of-the-road SSP2-4.5 emission scenario, and targetted a global mean surface air temperature near 1.5°C above the pre-industrial value. ARISE-SAI-1.5 is described in Richter et al. (2022). Selected data are available at Richter & Visioni (2022a,b).</p> <p>The files contained here contain processed annual daily extremes of surface temperature (TREFHT) and total precipitation (PRECT) from the ARISE-SAI-1.5 simulations and companion SSP245 simulations. Indices are those recommended by the WCRP Expert Team on Climate Change Detection Indices, Zhang et al. 2011). Methods to calculate the indices are also described in Tye et al. (2022).</p> <p><strong>Precipitation Indices</strong></p> <p>PRCPTOT, SDII, RX1D, RX5D, R10mm, R20mm, CDD, CWD, P95TOT, P99TOT</p> <p><strong>Temperature Indices</strong></p> <p>TNN, TNX, FD, TR, TN90, TN10, TN90p, TN10p, TXX, TXN, ID, SU, TX90, TX10, TX10p, TX90p, WSDI</p> <p>Where T?10 is the number of days below an annual 10th percentile threshold and T?90 is the number of days above an annual 90th percentile threshold (i.e. around 30 days per year).</p> <p>T?10p as defined by ETCCDI is the frequency of days below the rolling 5-day average climatological day of year 10th percentile. This threshold is also used for the cold spell duration index (CSDI), or consecutive days that are cool for the season.</p> <p>T?90p as defined by ETCCDI is the frequency of days above the rolling 5-day average climatological day of year 90th percentile. This threshold is also used for the warm spell duration index (WSDI), or consecutive days that are warm for the season.</p>
Data for publication "Statistical characteristics of extreme daily precipitation during 1501 BCE - 1849 CE in the Community Earth System Model".
<p>Here, the data used in Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). "Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model" in <em>Climate of the Past </em>(<a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-6</a>) are provided.</p> <p>Two simulations covering the period 1501 BCE - 2008 CE are performed with CESM 1.2.2: the orbital-only and the full-forcing simulations. The full-forcing transient simulation includes the new long record of volcanic eruptions (<a href="https://doi.org/10.1594/PANGAEA.928646">https://doi.org/10.1594/PANGAEA.928646</a>) that covers the last 3500 years. The output from the simulations is used to examine the long-term variability and characteristics of daily extreme precipitation during 1501BCE-1849 CE.</p> <p>The following files are provided:</p> <ul> <li> <strong>CESM122.transient.PRECT.anom.above99th.1501BCE-1849CE_I and II</strong>: Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the full forcing simulation. The file is split into two parts, with the first file containing the first 50% of extremes (I) and the second file containing the rest 50% (II).</li> <li><strong>CESM122.orbital.PRECT.anom.above99th.1501BCE-1849CE I and II:</strong> Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the orbital-only simulation.</li> <li> <strong>CESM122.transient.variables.mon.1979-2008CE:</strong> monthly precipitation, temperature, and geopotential height at 500 hPa for 1979-2008CE from the full-forcing simulation.</li> <li><strong>CESM122.trans.variable_names.years:</strong> Monthly variables from the full-forcing simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE. The variables are solar insolation (SOLIN), clear-sky net surface shortwave radiation (FSNSC), geopotential height at 500hPa (Z500), and surface temperature (TS).</li> <li><strong>CESM122.orbital.variable_names.years:</strong> Monthly variables from the orbital-only simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE.</li> <li><strong>CESM122.*.log-likelihood-GPDmodel-ExtForcing</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for external forcings.</li> <li> <strong>CESM122.*.log-likelihood-GPDmodel-ModesVar</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for modes of variability.</li> <li><strong>Evolk_EVA_distribution_1501BCE-2015CE</strong>: Distribution of volcanic aerosol for CAM5, produced based on Kim et al. (2021).</li> </ul> <p>If you use this dataset, please cite:</p> <p><em>Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model. Climate of the Past Discussions, 1-38. <a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-61</a></em></p>
Data files for figures in "Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming" by Bird et al. 2022
<p>The data files for figures in <em>Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming</em> by Bird, Bodeker and Clem. The data files are provided either as self-describing netCDF files, or .csv files with column descriptors.</p>
Extreme precipitation records in Antarctica [Dataset]
<p>This is the dataset associated to the research 'Extreme precipitation records in Antarctica' published in <em>International Journal of Climatology</em>.</p> <p>This repository contains:</p> <ul> <li>Precipitation extremes for each <em>model</em> at every grid point for a duration of <em>xxx</em> days. Files named: <ul> <li>[<em>model</em>]_PCP_max_[<em>xxx</em>]d.csv <ul> <li>Dimensions for ERA5: [lons, lats]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: mm</li> </ul> </li> </ul> </li> <li>Dimensions to plot the precipitation extremes: lons (longitudes), lats (latitudes) and duration. Files named: <ul> <li>[<em>model</em>]_PCP_max_lats.csv <ul> <li>Dimensions for ERA5: [lats]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: degrees</li> </ul> </li> <li>[<em>model</em>]_PCP_max_lons.csv <ul> <li>Dimensions for ERA5: [lons]</li> <li>Dimensions for RACMO2: [grid_x, grid_y]</li> <li>Units: degrees</li> </ul> </li> <li>[<em>model</em>]_PCP_max_duration.csv <ul> <li>Dimensions: [time]</li> <li>Units: days</li> </ul> </li> </ul> </li> <li>World Precipitation Records from 1 day. File named: <ul> <li>Max_WR_from1day.csv (first row duration [days]; second row precipitation [mm])</li> </ul> </li> </ul> <p> </p> <p><strong>How to cite</strong></p> <p>If you use this dataset, please cite the accompanying paper as:</p> <p>González-Herrero, S.,Vasallo, F., Bech, J., Gorodetskaya, I., Elvira, B., & Justel, A. (2023). Extreme precipitation records in Antarctica.International Journal of Climatology, 43(7), 3125–3138. <a href="https://doi.org/10.1002/joc.8020">https://doi.org/10.1002/joc.8020</a></p> <p> </p> <p><strong>Complementary code</strong></p> <p>You can find the jupyter notebooks to complement the research in: <a href="https://github.com/sergigonzalezh/Extreme_PCP_Scaling_Antarctica">https://doi.org/10.1002/joc.8020</a></p> <p> </p> <p><strong>Contact</strong></p> <p>If you have any question, please contact with Sergi at <a href="mailto:sergi.gonzalez@slf.ch">sergi.gonzalez@slf.ch</a></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>
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>
Spatial patterns of extreme precipitation and their changes under ~2 °C global warming: A large-ensemble study of the western US: Data Release
<p>This dataset supports the analysis in Rupp et al. (2022). The dataset consists of 17,223 data files containing the water year (WY) maximum of the daily-averaged precipitation rate simulated with the HadRM3p regional climate model configured for the western United States. Each file contains the WY maxima across the model domain for a single WY, single model parameterization, and single set of initial conditions. Please refer to Hawkins et al. (2019) and Rupp et al. (2022) for a description of how the climate model data were generated.</p>
Supporting data for ``Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land'"
<p>Here we have the processed data used in the preprint ``'Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land''</p> <p>The README.md file includes explanations about the data in the repository.</p>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The flood day products were derived from GRACE Terrestrial Water Storage and Extreme Precipitation. We used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81% of the flood events in the DFO database, 87% of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62% among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60°S—60°N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.</p>
Risk Assessment of Extreme Precipitation on to Low- and Medium-Voltage Electrical Infrastructure Under the Influence of Climate Change
Open the record for dataset details and reuse information.
Wind and Precipitation Extremes in Great Britain (1979-2019) to apply the methodology for Spatiotemporal Identification of Compound Hazards
<p>The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which was released in 2019 by ECMWF and benefits from the latest improvements in the field (Hersbach et al., 2020). ERA5 data (ECMWF, 2020) is available 1979 to present (we use up to September 2019), with a spatial resolution of 0.25deg x 0.25deg and an hourly temporal resolution. The data resolves the atmosphere using 137 levels from the surface up to a height of 80 km (ECMWF, 2020). ERA5 data are generated with a short forecast of 18 h twice a day (06:00 and 18:00 UTC) and assimilated with observed data (ECMWF, 2020). more information about ERA5 can be found <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">here</a>.</p> <p>The two following variables are extracted from the product:</p> <ul> <li> <p>Extreme precipitation (p): accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth’s in one hour (mm). This value is averaged over a grid cell.</p> </li> <li> <p>Extreme wind (w): hourly maximum wind gust at a height of 10 m above the surface of the Earth (m s-1). The WMO (2021) defines a wind gust as the maximum of the wind averaged over 3 s intervals. As this duration is shorter than a model time step, this value is deduced from other parameters such as surface stress, surface friction, wind shear and stability. This value is averaged over a grid cell.</p> </li> </ul> <p>Importation of the raw data</p> <p>Input data is divided into 4 files for each variables representing 4 periods:</p> <ol> <li>1979-1986</li> <li>1987-1997</li> <li>1998-2008</li> <li>2009-2019</li> </ol> <pre>library(ncdf4) filer=c(paste0(getwd(),"/data/in/raindat_7986.nc"), paste0(getwd(),"/data/in/raindat_8797.nc"), paste0(getwd(),"/data/in/raindat_9808.nc"), paste0(getwd(),"/data/in/raindat_0919.nc")) filew=c(paste0(getwd(),"/data/in/windat_7986.nc"), paste0(getwd(),"/data/in/windat_8797.nc"), paste0(getwd(),"/data/in/windat_9808.nc"), paste0(getwd(),"/data/in/windat_0919.nc")) Startdate=as.POSIXct("1979-01-01 10:00:00") Enddate=as.POSIXct("1986-12-31 23:00:00") # ncr = nc_open(filer) # ncw = nc_open(filew)</pre> <p>Intermediary data</p> <p>Intermediary data are stored in the “data/interdat” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "allraininclusters1.Rdata" "allraininclusters2.Rdata" ## [3] "allraininclusters3.Rdata" "allraininclusters4.Rdata" ## [5] "extremEventsWind.Rdata" "interclustRain.Rdata" ## [7] "interclustWind.Rdata" "metaclustRain.Rdata" ## [9] "metaclustWind.Rdata" "Rain_99_AllP.Rdata" ## [11] "rainP1.Rdata" "rainP2.Rdata" ## [13] "rainP3.Rdata" "rainP4.Rdata" ## [15] "rawclustRain.Rdata" "rawclustWind.Rdata" ## [17] "timeP1.Rdata" "timeP2.Rdata" ## [19] "timeP3.Rdata" "timeP4.Rdata" ## [21] "windP1.Rdata" "windP2.Rdata" ## [23] "windP3.Rdata" "windP4.Rdata" ## [25] "Wnd_99_AllP.Rdata" </code></pre> <ul> <li> <p>allraininclustersX: [data.frame] files are used to assess more accurately the accumulated precipitation during events by collecting precipitations from timesteps in which precipitation is above and below the threshold for every grid cell and the whole duration of the cluster.</p> </li> <li> <p>99_allp: [matrix] value of extreme precipitation and extreme wind gust threshold over the whole domain (one value per grid cell)</p> </li> <li> <p>interclust: [list] files contain a list of data from wind and precipitation clusters divided in the 4 periods aggregated over space and clusters (1 value per grid cell per cluster). These files are uses to create the files “RainEv_ldat” and “Windev_ldat”.</p> </li> <li> <p>metaclust: [list] files contain a list of metadata from wind and precipitation clusters divided in the 4 periods . These files are uses to create the files “RainEv_meta” and “Windev_meta”.</p> </li> <li> <p>rainPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing precipitation data for the period X.</p> </li> <li> <p>rawclust: [list] files contain a list of data.frame from wind and precipitation clusters divided in the 4 periods. These files are uses to create the files “RainEv_hdat” and “Windev_hdat”.</p> </li> <li> <p>timePX: [vector] contain vectors of time for the 4 periods.</p> </li> <li> <p>windPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing wind gust data for the period X.</p> </li> </ul> <p>Output data</p> <p>Output data contains metadata and raw data of single and compound hazard clusters are stored in the “data/out” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "compoundclusters.csv" "CompoundRW_79-19.v3x.Rdata" ## [3] "extremEvents_Rain.Rdata" "extremEvents_Wind.Rdata" ## [5] "Rain_stfprint.Rdata" "rainclusters.csv" ## [7] "RainEv_hdat_1979-2019.Rdata" "RainEv_ldat_1979-2019.Rdata" ## [9] "Rainev_ldatp_1979-2019.Rdata" "RainEv_meta_1979-2019.Rdata" ## [11] "RainEv_metap_1979-2019.Rdata" "Wind_stfprint.Rdata" ## [13] "windcluster.csv" "WindEv_hdat_1979-2019.Rdata" ## [15] "WindEv_ldat_1979-2019.Rdata" "WindEv_meta_1979-2019.Rdata" </code></pre> <ul> <li> <p>CompoundRW: [data.frame] contains metadata for the compound hazard clusters identified</p> </li> <li> <p>_hdat: [data.frame] hourly data of precipitation and wind gust clusters.</p> </li> <li> <p>_ldat: [data.frame] aggregated data over space and clusters (1 value per grid cell per cluster) for wind gust and precipitation clusters.Rain_ldatp contains aggregated values including non-extreme timesteps. Created from allraininclustersX.</p> </li> <li> <p>_meta:[data.frame] metadata for wind gust and precipitation clusters</p> </li> <li> <p>stfprint: [data.frame] files containing duration*footprint of each hazard clusters during all clusters</p> </li> <li> <p>sptdf: [data.frame] data.frame containing spatial, temporal, cluster and intensity information</p> </li> </ul> <p>Codes assiciated to the method are availaible here: https://github.com/Alowis/SI-CH</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>
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>
Climate characteristics and trends of extreme daily precipitation events associated with cold fronts in the metropolitan region of São Paulo, Brazil
<p>Data used in the paper "Climate characteristics and trends of extreme daily precipitation events associated with cold fronts in the metropolitan region of São Paulo, Brazil" from Theoretical and Applied Climatology</p>
Projection of hourly extreme precipitation over Eastern China
<p>Data used in the manuscript "<strong>Projection of hourly extreme precipitation over Eastern China</strong>" which will be submitted to Journal of Geophysical Research: Atmospheres.</p>
Disentangling the impact of event- and annual-scale precipitation extremes on critical-zone hydrology in semiarid loess: A case study in apple tree plantation
<p>The dataset is the basic data of the author's paper ' Disentangling the impact of event-and annual-scale precipitation extremes on critical-zone hydrology in semiarid loess - a case study in apple tree plantation '. The main content of this paper is to study the hydrological effect of extreme precipitation on the critical area of semi-arid loess. Taking apple plantation as an example, the data set includes the soil moisture and soil temperature data monitored in the field and the apple tree transpiration data. The measured data are used to calibrate and verify the model used in this paper. The water vapor flux, apple tree evapotranspiration and soil leakage data of the simulated soil profile are also included to analyze the hydrological effect of extreme precipitation on the critical area of loess.</p>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The supplementary figures contain 2380 precipitation-type flood events recorded by the DFO compared with results derived based on GRACE and MODIS. "DFO-GRACE-MODIS" folder means comparison among flood derived from DFO, GRACE and MODIS while "DFO-GRACE" folder means comparison between DFO and GRACE due to lack of MODIS-derived flood inundation. As for detailed information, please refer to the corresponding paper. </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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