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50 results for “atmospheric river”
Dataset supporting "Predictable Patterns of Seasonal Atmospheric River Variability Over North America During Winter"
<p>A dataset containing selected outputs from real time forecasts and hindcasts produced by the Seamless System for Prediction and Earth System Research (SPEAR). Variables in the dataset include atmospheric river frequency, surface temperature, 500 hPa geopotential height and precipitation. The period covers 1991 through 2023 averaged seasonally in the months December through February. Forecasts initialized in a given month "MM" are stored in subdirectories and files with the indicator "iMM," where MM varies from 03 to 12 (March through December). A pair of python scripts demonstrating average predictability time analysis are also included. This data is associated with the study "Predictable Patterns of Seasonal Atmospheric River Variability over North America during Winter," by Clark et al. </p>
ClimateNet Dataset as used in "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data"
<p>ClimateNet dataset as it was used by us for the study: "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data" (https://gmd.copernicus.org/preprints/gmd-2024-60/).</p> <p> </p> <p>For the original dataset refer to: https://portal.nersc.gov/project/ClimateNet/</p>
Dataset for JGR Atmospheres manuscript " An Observational Constraint of VOC Emissions for Air Quality Modeling Study in the Pearl River Delta Region"
<p>The file includes hourly observations of ground-level ozone (O<sub>3</sub>) and nitrogen dioxide (NO<sub>2</sub>) concentrations at 56 environmental monitoring stations in the Pearl River Delta (PRD) region in China during June 2018. The units are in μg/m<sup>3</sup>.</p>
AIRA (Atmospheric Rivers Identification Algorithm) input dataset and results
<p>This dataset contains the output of three regional climate simulations covering Europe for the period 1991-2010, used to apply Atmospheric Rivers Identification Algorithm (AIRA in Spanish), and the results of its performance. The simulations were carried out by the MAR group (www.um.es/gmar) of the University of Murcia, using the WRF-Chem model (v3.6.1). Each simulation includes different levels of aerosol interactions.</p>
Data from: Atmospheric rivers and the mass mortality of wild oysters: insight into an extreme future?
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Genesis locations of the costliest atmospheric rivers impacting the Western United States (insurance claim data)
<p>Atmospheric rivers (ARs) are responsible for the vast majority (approximately 88%) of flood damage in the Western U.S, an annual average of USD$1.1 billion. Here, using historical flood insurance data, we investigate the genesis characteristics of damaging ARs in the Western U.S. ARs exceeding USD$20 million in damage (90th percentile), are shown to travel further across the Pacific Ocean, with median genesis locations 8° to 27° further westward compared to typical ARs. Identifying regions of preferential genesis of damaging ARs elicit important implications for AR observation campaigns, highlighting distant regions not currently considered for AR reconnaissance. The genesis of damaging ARs is associated with elevated upper-level zonal wind speeds along with deeper cyclonic anomalies, most pronounced for Washington and Oregon ARs. Linking AR dynamics and lifecycle characteristics to economic damage provides an opportunity for impact-based forecasting of ARs prior to landfall, supporting efforts to mitigate extreme flood damages.</p>
Stable Isotopologues of Atmospheric Moisture at Wind River Field Station (USA)
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Genesis locations of the costliest atmospheric rivers impacting the Western United States (insurance claim data)
Open the record for dataset details and reuse information.
Sensitivity of atmospheric river vapor transport and precipitation to uniform sea-surface temperature increases
<p>This is the companion data for the manuscript of the same title, originally submitted to JGR: Atmospheres on 02/08/2020 and resubmitted on 06/30/2020. Specifically, this dataset corresponds to the resubmitted version. Note that the only change is the addition of code example to generate kernel density estimates of Hadley cell edge, subtropical jet, and eddy-driven jet positions. </p> <p><strong>modelParameters: </strong>this folder contains the scripts I ran on NERSC Cori in 2019 to initialize the CESM2.0/CAM5 model runs. </p> <ul> <li>qobs_script_newcase.sh: creates all cases, for the Baseline as well as the +xK SST runs</li> <li>docn_comp_mod.F90: original CAM5 aquaplanet SST distributions; "QOBS" is used for my "Baseline" experiments</li> <li>plusxK_docn_comp_mod.F90: modified SST distributions, for x=(2,4,6); these are simply uniform additions to the QOBS SST distributions</li> <li>Macros.make & env_mach_specific.xml: configuration files to run CESM2.0 on Cori at the time</li> <li>user_nl_cam: namelist for CAM5; specifies some model run parameters as well as output variables.</li> </ul> <p><strong>detectionParameters: </strong>this folder contains the scripts I ran on NERSC Cori in 2019 to detect tropical cyclones and atmospheric rivers. Use this code for reference purposes only (i.e., to see which parameters were used to detect ARs or TCs). It will not run as-is.</p> <p><strong>All other subfolders </strong>provide working examples of code used to generate figures (all in Jupyter notebooks) for the manuscript; folder names are descriptive. </p> <p>Unfortunately, model output was large (~12 TB). Hence, I only provide mean data, used directly to generate figures, in this repository. All model output are archived on tape at NERSC. </p> <p>For more details, refer to the manuscript, or contact me (eelliott@ucdavis.edu). </p>
Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers"
<p>Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers", whose manuscript will be submitted by 10/25/2023</p> <p><br>The dataset contains the necessary data to generate the figures in the paper with the code in the link <a href="https://doi.org/10.5281/zenodo.10958491" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10958491</a> whose Github reference is <a href="https://github.com/meteorologytoday/paperfigures-2024-AR-SST-response" target="_blank" rel="noopener">https://github.com/meteorologytoday/paperfigures-2024-AR-SST-response</a></p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.