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33 results for “precipitation event”
EMIC-driven precipitation events observed by ELFIN, using proton precipitation as a proxy for EMIC waves
<p>This dataset lists the time intervals of the precipitation events observed by ELFIN nearby proton precipitation (a proxy for EMIC waves). This dataset is analyzed in the paper "Electron Precipitation Observed by ELFIN Using Proton Precipitation as a Proxy for Electromagnetic Ion Cyclotron (EMIC) Waves", currently under review in Geophysical Research Letters.</p>
Data for 'Rethinking variability in bedrock rivers: sensitivity of hillslope sediment supply to precipitation events modulates bedrock incision during floods'
<p>This is the repository for model code, sample data, and plotting scripts for the OTTER model for Python.</p> <p>The file OTTERPy.py contains the actual model code</p> <p>The file OTTERPlottingFuncs.py contains a few scripts for plotting model results.</p> <p>The four sample data files in the repository are results from two long (1.5million model year) runs at different rock uplift rates which took several days to run on the IU Bloomington Quartz supercomputing cluster. This data should be viewed and plotted using the OTTERPlottingFuncs.py script. See that code for information on how to select which model to plot.</p> <p> </p> <pre><code>Glossary of variables - this is pretty close to exhaustive, there are a probably a couple of throwaway variables not listed here. a: Sternberg's law multiplier Ah: drainage area from Hack's Law (m^2) beta: fraction of sediment transported as bedload from 0 to 1 D: grain size at each node (m) dA: change in drainage area from node to node (m^2) depth_thresh: sediment depth at which bedrock erosion can occur Do: initial grain size Do: grain size at headwaters (m) dQs: change in sediment supply from node to node dt: time step (years) dx: space step (m) dz_b: bedrock erosion at each node dz_s: change in sed depth dz_b_t: bedrock erosion saved through time dz_b_store: bedrock erosion stored dz_s_store: sed depth change stored eQ: exponent for scaling Qw from Ah eroded_sedsup: sediment created by bedrock erosion eW: exponent for scaling W from Qw F: fractional bedrock exposure g: gravitational acceleration (M/s^2) H: water depth Hc: Hack's Law constant He: Hack's law Exponent initial_sedsup: Sediment supply in first 10k yr (equal to uplift) kf: bedrock erodibility kQ: constant for scaling Qw from Ah kv: parameter controlling discharge variability - Crave & Davy, 2001. Lower kv is more variable kw: lateral bedrock erosion constant kWid: constant for scaling W from Qw last_z: elevation at channel mouth lmbda: sediment porosity n: manning's roughness coefficient onlyonce: changes from 0 to 1 to make sure the uplift pulse only happens one time onoff: turns sediment on and off entirely onoffsedvar: turns stochastic sediment on and off Qs: sediment supply along the length of the river Qs_down: downstream sediment flux at each node Qt: sediment transport capacity Qw: water discharge Qwo: initial water discharge QwScale: array of scalar multipliers for Qw, from the chosen distribution R: effective density of submerged sediment rho_w: density of water (kg/m^3) rho_s: density of sediment (kg/m^3) savesteps: at what time should we save outputs? sed_depth: depth of seidment at each node (m) SedStore: storing snapshots of sediment thickness through time slope: river gradient (negative usually) SlopeStore: storing snapshots of channel slope through time tarray: array of model time tau_b: basal shear stress tau_c: critical shields criterion for initiation of sediment motion time: model duration (years) topo: channel elevation (bedrock + sediment) upinc: size up uplift pulse, as a multiplier of initial uplift rate upinctime: when should the pulse happen? (years) uplift: uplift field across the entire domain uprate: initial uplift rate, if using constant uplift across domain W: channel width WidthStore: storing snapshots of channel width through time Wo: initial channel width yr2sec: number of seconds in a year z: bedrock channel elevation ZStore: storing snapshots of bedrock elevation through time</code></pre>
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>
Data of event list of cold and hot patches for manuscript "The Dependence of Cold and Hot Patches on Local Plasma Transport and Particle Precipitation in Northern Hemisphere Winter"
<p>A database of 4,634 cold patches (high density and low electron temperature) and 4,700 hot patches (high density and high electron temperature) from Defense Meteorological Satellite Program F16 in 2005-2018 winter months.</p>
Dataset for the study "Deep Learning-Based Precipitation Simulation for Different Types of Weather Events in Eastern China"
<p>This dataset contains the data and codes used in the study "Deep Learning-Based Precipitation Simulation for Different Types of Weather Events in Eastern China". The resources will be updated continously.</p>
On the importance of using event-specific wave pitch angle diffusion rates in modeling diffuse electron precipitation
<p>These are simulation results from running RAM-SCB and GLOW models. Two simulations were conducted. One uses diffusion coefficients derived based on statistical wave properties ("stat"), and the other uses coefficients from event-specific simultaneous wave properties ("event"). Data include the precipitating electron flux, wave diffusion coefficients, and height profiles of ionospheric electron density.</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>
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>
Probability of a dust emission event during the period that the soil active layer remains wet after a precipitation event
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Increased frequency of extreme precipitation events in the North Atlantic during the PETM: Observations and theory
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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>
Response of hydrological processes to event- and annual-scale precipitation extremes in the critical zone of the hillside surface
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Surface precipitation type based on new decision algorithm about 131 precipitation events in 5 ICE-POP 2018 sites
<p>The documents are decision results (+simple event description) of surface precipitation type based on new decision algorithm about 131 precipitation events in 5 ICE-POP 2018 sites.</p> <p>Simple event description include PARSIVEL V-D scatterplots and rawinsonde profiles.</p> <p>Please see Description_plot.pdf.</p> <p>and other pdf files show figures relating events belong to each types (RA[Rain], SN[Snow], RASN).</p>
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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.