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11 results for “Extreme wildfire”
Data from: Solar energy resource availability under extreme and historical wildfire smoke conditions
<p>The data in this repository are used to generate the figures in the article "Solar energy resource availability under extreme and historical wildfire smoke conditions" by Corwin et al. (accepted 2024) in <em>Nature Communications</em>. Data are the final processessed and merged datasets sourced from the following publicly available data products:</p> <ul> <li>National Renewable Energy Laboratory’s (NREL) National Solar Radiation Database (NSRDB) (<a href="https://nsrdb.nrel.gov/)">https://nsrdb.nrel.gov/)</a>. <ul> <li>Bulk download in July 2023 via AWS: <a href="https://registry.opendata.aws/nrel-pds-nsrdb/">https://registry.opendata.aws/nrel-pds-nsrdb/</a></li> <li>Variables: modeled irradiance (clear-sky and all-sky direct normal (DNI) and global horizontal (GHI) irradiance, aerosol optical depth, and cloud optical depth</li> </ul> </li> <li>National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product. <ul> <li>Access: <a href="https://www.ospo.noaa.gov/Products/land/hms.html#maps">https://www.ospo.noaa.gov/Products/land/hms.html#maps</a></li> <li>Variables: smoke plume locations</li> </ul> </li> <li>National Aeronautics and Space Administration's (NASA) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product (MCD19A2 MODIS/Terra + Aqua land aerosol optical depth daily L2G Global 1km SIN Grid V006). <ul> <li>Access: <a href="https://lpdaac.usgs.gov/products/mcd19a2v006/">https://lpdaac.usgs.gov/products/mcd19a2v006/</a></li> <li>Variables: aerosol optical depth and cloud mask</li> </ul> </li> <li>NASA's Clouds and the Earth’s Radiant Energy System (CERES) cloud data product (SYN1deg-1Hour Edition 4.1) <ul> <li>Access: <a href="https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp">https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp</a></li> <li>Variables: cloud optical depth</li> </ul> </li> </ul> <p>A detailed description of the data processing methods used to produce the final merged data are available in the article by Corwin et al. </p> <p>Associated code scripts are located in the linked code repository.</p>
FLAME Deliverable 8 - Extreme Wildfires Dataset (D3.1)
<p>This dataset comprises extreme wildfire growth events that occurred in Greece during the 2002 - 2020 period. The data are provided in GeoPackage (.gpkg) format. The accompanying report documents information concerning the methods used for deriving the dataset. </p>
Extreme wildfire events analysis for dry pyrocloud hypothesis
<p>Dataset for EWE to test the dry pyrocloud hypothesis. The files are Excel files from 182 extreme wildfires (EWE_globla.xlsx). From those fires, we extract extreme fire spread events to use in the research when we can reconstruct the vertical profile from the ERA5 ECMWF reanalysis data (fires_verticalprofile.xlsx) and the accurate rate of spread from observations (fire_spread_events.xlsx).</p> <p>The dataset contains two videos ilustratingthe concept of dry pyrocloud</p> <p>The dataset contains a variable explanatory document ('Table of variables on the dataset. docx')</p> <p>The dataset contains a README file to guide the use of code contained in the Demo ZIP</p> <p>The demo ZIP contains the phyton codes to obtain the fire-spread-events variables and a DEMO fire to test the codes. The fire is the Santa Coloma wildfire from 24 and 25 of July 2021 in Catalonia, SPAIN.</p> <p> </p>
Dataset associated with Senf et al. (2023): "How the extreme 2019-2020 Australian wildfire affected global circulation and adjustments"
<p>This contains data aggregates derived from global ECHAM-HAM simulation for the study for effects due to the extreme Australian wildfire event 2019/2020. This data build the basis for analysis and figures in Senf et al. (2023) submitted to ACP.</p> <p> </p> <p>Simulation Data are</p> <ul> <li>available for freely running ensembles (36 member) and nudged simulations</li> <li>conducted for fire emissions artificially scaled with factors 0, 1, 2, 3, 5.</li> <li>stored for Jan - Mar 2020</li> </ul>
Drivers of extreme wildfire years in the 1965–2019 fire regime of the Tłı̨chǫ First Nation territory, Canada
<p>Datasets, metadata and Rscript used to describe 1965-2019 wildfire regime and extreme wildfire years in central NWT.</p> <p> </p>
Canada's extreme wildfires dominate the decline in global land carbon sinks in 2023
<p>Terrestrial land carbon sinks are strongly influenced by climate extremes, and 2023 is the warmest year on record, accompanied by an El Niño event, extreme wildfires, and extreme precipitation and drought, but their impact on global land sinks in 2023 remains unclear. Here, we used the Global Carbon Assimilation System, version 2, to estimate recent global land sinks by assimilating the OCO-2 ACOS v11.1 XCO2 retrievals. We estimate the global land sink to be -1.63 ± 0.52 PgC/yr in 2023. Compared to 2017-2022, it decreases by 0.59 PgC/yr, in which net ecosystem exchange decreases by only 0.14 PgC/yr, but wildfire emissions increase significantly by 0.45 PgC/yr, mainly in Canada. Our findings suggest that extreme wildfires are an important threat to land sinks under global warming.</p>
In-Plume Profiling Methodology: Ensuring Firefighter Safety During Pyroconvective Extreme Wildfire Events
<p>dataset for the paper: <span>In-Plume Profiling Methodology: Ensuring Firefighter Safety During Pyroconvective Extreme Wildfire Events</span></p> <p> </p> <p><span>It includes the radiosonde flight files used in the methodology</span></p> <p><span>It includes the videos of radiosondes launching as complementary materials</span></p>
Canada’s extreme wildfires dominate the decline in global land carbon sinks in 2023
Open the record for dataset details and reuse information.
Wildfire and extreme rainfall reduce soil carbon and nitrogen pools in a semiarid shrubland ecosystem
Open the record for dataset details and reuse information.
AirNow and Low Cost Sensor PM2.5 During Extreme Wildfires in Sonoma County, 2020
<p>This data collection includes AirNow measurements of PM2.5 from the Sebastopol station as well as PurpleAir measurements of PM2.5 within Sonoma County, California, USA. Data is archived for the manuscript "Air Quality Monitoring and the Safety of Farmworkers in Wildfire Mandatory Evacuation Zones". Code used to process and analyze data can be found on GitHub: https://github.com/rrbuchholz/pm25_atmosphere_analysis_2023</p>
Code and data for: Ladder fuels, not canopy volumes, consistently associated with forest wildfire severity even in extreme topographic-weather conditions
<p>Code and associated metrics for Hakkenberg et.al. 2024. Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions</p> <p>Contact chrishakkenberg@gmail.com for all questions and requests</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
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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.