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43 results for “air support”
Data supporting "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products"
<p>Changelog</p> <p>v2: Fixes an identified issue in FluxEngine v4.0.7 that affects the calculation of fCO2atm. Fluxes have been recalculated using FluxEngine v4.0.9.1, and the analysis regenerated. The intergrated air-sea CO2 flux (or ocean sink) has reduced by ~0.2-0.3Pg C yr-1 but uncertainties are unchanged. </p> <p>v1: Initial dataset released along with the supporting manuscript</p> <p> </p> <p>Data included in this repository supports the manuscript "A comprehensive analysis of air-sea CO<sub>2</sub> flux uncertainties constructed from surface ocean data products".</p> <p>Two files are present:</p> <ol> <li>A Python config file used to run the software developed for the analysis (Ford et al., 2024)</li> <li>A ZIP file containing the input, neural network, and output files for the analysis.</li> </ol> <p>Within the ZIP file, multiple folders are present:</p> <ol> <li>Decorrelation contains .csv files that contain the annual estimates of the decorrelation lengths for the parameters requiring these (SST, sea ice, wind, fCO<sub>2</sub> and fCO<sub>2</sub> network).</li> <li>Flux contains the individual FluxEngine output files that provide all the flux calculations, and auxillary data to the flux calculations.</li> <li>Fluxengine_input contains the input files to FluxEngine, which specifies the fCO<sub>2 (sw), </sub>xCO<sub>2 (atm)</sub> and the temperature, salinities for the skin and subskin layers.</li> <li>Inputs contains all the monthly 1 degree input data used. Many of the data used are not native monthly 1 deg, and so these are generated from the higher resolution data. These are all combined into the neural_network_input.nc file, so a single file can be distributed with all the inputs used.</li> <li>Networks contains the TensorFlow neural network (FNN) files, where each province has 10 folders (one for each ensemble).</li> <li>Plots contains output plots for debugging and final plots of uncertainties</li> <li>Scalars contains the scalars used to normalise the data before input into the neural network. These are saved as Python pickle files, as they are needed if the neural network is used on other data.</li> <li>Unc_lut contains the look up tables to generate the parameter uncertainty as described in the manuscript. These are Python pickle files.</li> <li>Validation contains a csv file with the independent test RMSD, along with Python Pickle files of the validation data.</li> </ol> <p>In the main folder, three files are present:</p> <ol> <li>Annual_flux.csv contains the annual air-sea CO<sub>2</sub> flux (or ocean sink estimate) estimated from the fCO<sub>2 (sw)</sub> fields. This also contains the annual integrated uncertainties for each component in the uncertainty flow chart in the manuscript.</li> <li>Output.nc contrains the gridded global fields of the fCO<sub>2 (sw)</sub>, the air-sea CO<sub>2</sub> flux, and the uncertainties for all the individual components. Metadata within the file should provide all the information required.</li> <li>Training.tsv contains the training/validation data alongside the input parameters for neural network training</li> </ol> <p> </p> <p>Please contact Daniel J. Ford (<a href="mailto:d.ford@exeter.ac.uk">d.ford@exeter.ac.uk</a>) if you have any questions.</p> <p><strong>Acknowledgements</strong></p> <p>This work was funded by the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Union under grant agreement no. 101083922 (OceanICU; https://ocean-icu.eu/) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p> </p> <p><strong>References</strong></p> <p>Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschutzer, P., Jersild, A., & Shutler, J. D. (2024, June 30). OceanICU Neural Network Framework with per pixel uncertainty propagation (v1.1) (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.12597803</p>
Supporting data and software for: Low-temperature open-air synthesis of PVP-coated NaYF4:Yb,Er,Mn upconversion nanoparticles with strong red emission
<p>Upconversion nanoparticles (UCNPs) have unique photonic properties that make them ideally suited for many applications. They are excited by low-energy near-infrared photons and emit at higher energy (typically visible) wavebands. However, synthesis of UCNPs requires either high pressure reaction chambers or inert atmospheres. Combined with the requirements for high-temperatures (200 to 400 °C) and long reaction times (e.g. up to 24 hours), these place barriers to entry for UCNP research, in terms of both financial barriers and knowledge/"know how". These constraints may also limit the scale of UCNP production for end-user applications.</p> <p>We adapted and further developed a method for producing UCNPs with simple laboratory equipment, i.e. a hot-plate and beakers. No pressure vessel or inert atmosphere is required. The UCNPs produced have a<span> polyvinylpyrrolidone (PVP) polymer coating, with strong red emission due to Mn<sup>2+</sup> co-doping within the UCNP crystal lattice. It was found that UCNPs of composition NaYF<sub>4</sub>:Yb,Er,Mn (Yb = 20 mol %, Er = 2 mol%, Mn = 35 mol%) maximised the red emission whilst also minimising the diameter of the UCNPs to </span> 36 ± 15 nm. These combination of optical and physical properties should make these UCNPs ideal for further development and exploitation, particularly for biological applications where red emission can penetrate over a centimetre of tissue.</p> <p>This dataset and software accompanies the manuscript <em>'Low-temperature open-air synthesis of PVP-coated NaYF<sub>4:</sub>Yb,Er,Mn upconversion nanoparticles with strong red emission</em>', which was published in Royal Society Open Science on 19th January 2022. https://doi.org/10.1098/rsos.211508</p>
OPTIMA - droplet characteristics incl. air support data - bed-grown carrots boom sprayer
<p>Spray droplet size and velocity characteristics data of 4 nozzle types at 4 air support settings, measured at 300 kPa and 50 cm below the nozzle using a PDPA laser based measuring set-up.</p> <p>Read 'Info' tab for more information.</p> <p>Data presented in Zwertvaegher et al. (2022). Boom sprayer optimizations for bed-grown carrots at different growth stages based on spray distribution and droplet characteristics. Pest Management Science. https://onlinelibrary.wiley.com/doi/10.1002/ps.6792 </p>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
Supporting data and software for: Low-temperature open-air synthesis of PVP-coated NaYF4:Yb,Er,Mn upconversion nanoparticles with strong red emission
Open the record for dataset details and reuse information.
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Kampala
<p>This paper is under review</p> <p>Abstract:</p> <p>Kampala, the political and economic capital of Uganda and one of the fastest urbanising cities in sub-Saharan Africa, is experiencing a deteriorating trend in air quality with emissions from multiple diffused local sources like transportation, domestic and outdoor cooking, and industries, and sources outside the city airshed like seasonal open fires in the region. PM2.5 (particulate matter under 2.5um size) is the key pollutant of concern in the city with monthly spatial heterogeneity of 60-100 ug/m3. Outdoor air pollution is distinctly pronounced in the global south cities and lack the necessary capacity and resources to develop integrated air quality management programmes including ambient monitoring, emissions and pollution analysis, source apportionment, and preparation of clean air action plans. This paper presents an integrated assessment of air quality in Kampala drawing from ground measurements (from a hybrid network of stations), satellite observations (from NASA’s MODIS and OMI), global reanalysis fields (from GEOS-chem and CAMS simulations), high resolution (~1km) multi-pollutant emissions inventory for the airshed, WRF-CAMx based PM2.5 pollution analysis, and a qualitative review of institutional and policy environment for air quality management in Kampala. The proposed clean air action plans aim for better air quality in the region using a combination of short-, medium-, and long-term emission control measures for all the dominate sources and institutionalize pollution tracking mechanisms (like emissions and pollution monitoring and reporting) for effective management of air pollution.</p> <p>This data archive serves as a supplemenary to the journal article and with a short description of the files below:</p> <ul> <li>File: AQ-Kampala-Analysis-Summary.pptx (Caution: large 60MB) <br>A composite presentation including the following<br> <ul> <li>Grid summaries</li> <li>Snapshots of airshed GIS files, emission activities</li> <li>Summary of meteorology from WRF simulations and historical synoptics</li> <li>Summaries of ambient monitoring data</li> <li>CAMS reanalysis summary</li> <li>Summaries of Emission inventory and WRF-CAMx modelling (annual and monthly)</li> <li>Summaries of PM2.5 Source apportionment (annual and monthly)</li> </ul> </li> <li>File: grids_kampala.rar<br>Grid file (KML and ESRI shapefiles format) for the airshed spanning 0.0N to 0.6N and 32.3E to 32.9E with a spatial resolution of 0.01deg (~1km)</li> <li>File: gis_roads_from_opensteetmaps.rar<br>ESRI shapefiles of primary roads and all roads, extracted from the openstreetmaps<br>Raw data archive @ https://download.geofabrik.de/index.html</li> <li>File: gis-scanned2021image-quarries.kml<br>KML file of quarries scanned using the imagery on Google Earth platform</li> <li>File: population_kampala_2000-2022.csv<br>Gridded population data 2000 to 2022<br>Raw data archive is from LANDSCAN - https://landscan.ornl.gov</li> <li>File: Monitoring-Kampala_USEmbassy_2017-2024.xlsx<br>Summary of monitoring data collected at the US Embassy in Kampala<br>Raw data archive is @ https://www.airnow.gov/international/us-embassies-and-consulates/#Uganda$Kampala</li> <li>File: meteo_wrf_stats.xlsm<br>Summary of output of WRF simulations for the Kampala region. Have to activiate macros to summarize the results by month and update the charts. The tool can be used to other cities also by changing the input data.</li> <li>File: meteo_precip-era5-reanalysis.csv<br>Summary of monthly precipitation date (mm/day) from ERA5 reanalysis fields<br>Raw data archive is @ https://psl.noaa.gov/data/atmoswrit/timeseries</li> <li>File: TROPOMI_EastAfrica_NO2_Maps.zip<br>Images of monthly average TROPOMI NO2 extracts covering East Africa (Uganda and Ethiopia)<br>Extracted from Google Earth Engine, using 10% cloud fraction</li> <li>File: TROPOMI_EastAfrica_CSVs.zip<br>CSV files of gridded monthly average NO2, SO2, HCHO, and Ozone columnar densities<br>Extracted from Google Earth Engine, using 10% cloud fraction<br>Read the data descriptions and applicability of the data for analysis before using (for example, negative numbers in the SO2 file). <br>NO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 <br>SO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2<br>HCHO - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO<br>O3 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3</li> <li>File: composite_emisson_factors_gains.xlsx<br>A composite library of emission factors for reference</li> <li>File: kampala_gridded_emissions_2018.rar<br>Gridded emissions inventory for Kampala - PM25, PM10, SO2, NO, NO2, and CO<br>PM25 is speciated into FPRM, BC, and OC (sum all for PM25)<br>PM10 is speciated into FPRM, CPRM, BC, OC (sum all for PM10)<br>All emissions in tons/year/grid<br>Emissions are seggragted into sectors and fuels - included in the filenames</li> <li>File: kampala_gridded_modelled_monthavgp25.csv<br>Gridded PM2.5 concentrations for 2018, from WRF-CAMx modelling system<br>Monthly averages in ug/m3</li> </ul>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Addis Ababa
<p>This paper is under review. For additional information or queries, send email to sguttikunda@urbanemissions.info</p> <p>****</p> <p>ABSTRACT: Ground measurements and satellite observations over Addis Ababa airshed show a deteriorating trend of air pollution, especially for PM2.5 (all particulate matter under 2.5um). In this paper, we present a review of available monitoring data; a model-ready multi-pollutant (PM10, PM2.5, SO2, NOx, CO, non-methane VOCs, and CO2) emissions inventory at 0.01º resolution for the designated airshed; a heatmap of PM2.5 concentrations and an estimate of source contributions constructed using WRF-CAMx chemical transport modelling system; and a discussion on proposed actions towards establishing an air quality management plan for the city. Emissions from road transport; residential and commercial cooking; resuspended dust on roads and from construction activities; residential and industrial heating; lighting; open waste burning; and other industrial activities contributed the most to ambient PM2.5 pollution. Particularly, vehicle exhaust is estimated to contribute up to 29% of total PM2.5, followed by biomass combustion in the residential and industrial sectors.</p> <p>File included in this dataset:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Report-Addis-Data-Summary.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Ambient monitoring data<br>File: AddisAbaba_AllEmbassy_Data.xlsx (summary of data till May2024)<br>File: monitoring_addisair_cleaned.xlsx (summary of sensor data)</li> <li>Gridded emissions inventory<br>File: addis_gridded_emissions.rar<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_addis_grids.rar</li> <li>TROPOMI UVAI and MODIS AOD<br>Data files extracted from google earth engine as TIF and CSV, for the designated airshed<br>File: ADDISABABA_yearly_tifs.zip</li> <li>Meteorlogical data summary for the airshed, extracted from the WRF sumulations<br>File: meteo_addis.houravg_summary.csv</li> <li>Reference reports<br>File: Report-C40-2016-Addis-Ababa-GHG-Emssions.pdf<br>File: Report-CSE-Ethiopia-Urban-AQM-Guidance.pdf<br>File: Report-WB-Ethiopia-motorization-management.pdf<br>File: Report-UNEP-Addis-AQM-Plan-Draft.pdf</li> <li>GIS files<br>File: gis_addis_grids.rar (shapefile and KML file for the airshed grid)<br>File: gis_addis_osm_roads.rar (shapefiles extracted from openstreetmaps)<br>File: pop_extracts_4selection_domain.xlsm (gridded population data, with macros can be used to extract population totals around a monitoring station)<br>File: gis_addis_multiple_layers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> </ol>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Bishkek
<p>Full paper is published here<br><a href="https://doi.org/10.3390/air2040021">https://doi.org/10.3390/air2040021</a><br>Mapping PM2.5 Sources and Emission Management Options for Bishkek, Kyrgyzstan<br><br></p> <p>****</p> <p>Harsh winters, aging infrastructure and control technologies, and the increasing demand for urbanization and modernization of amenities are major factors contributing to the deteriorating air quality in Bishkek, the capital city of Kyrgyzstan and a burgeoning economic hub in Central Asia. The heating energy needs are met via combustion of coal at the central heating plant, heat only boilers, and in-situ heating equipment and the mobility needs via combustion of diesel and petrol. Other mapped sources contributing to daily air pollution levels in Bishkek’s airshed include 30 km2 of industrial area, 16 large open combustion brick kilns, a vehicle fleet with average age more than 10 years, 7.5 km2 of quarries, and one landfill. Annual PM2.5 emission load for the airshed is approximately 5,500 tons, resulting in an annual average concentration of 48 ug/m3, which is 9-10 times higher than the World Health Organization (WHO) guideline of 5 ug/m3. Wintertime daily averages range from 200-300 ug/m3. Proposed emissions management policies for the city include shift to clean fuels like gas and electricity at the heating plants and households, restricting the secondhand vehicle imports and incentivizing newer standard vehicles, promotion of public transport system with newer buses, at least doubling of the waste collection efficiency and landfill management capacity and encouraging greening and maintaining road infrastructure to control dust emissions. PM2.5 levels from mid- to long-term implementation of these options is expected to drop by 50-70%. A long-term plan for Bishkek must include an expansion of the ambient monitoring network using a combination of reference-grade and low-cost sensors to track progress of air quality management efforts and to support information dissemination for public awareness.</p> <p>Files included here:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Bishkek_AQ_Analysis_Composite.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Meteorological data summary for the airshed, extracted from the WRF sumulations<br>File: Bishkek_Met_Summary.pptx<br>File: bishkek_meteorology_stats.xlsm (activate macros for stats and making images for final use)</li> <li>GIS files<br>File: gis_bishkek-grids-pop.rar (shapefile and KML file for the airshed grid, csv file for gridded population 20 years, and images)<br>File: gis_bishkek_roads.rar (shapefiles extracted from openstreetmaps)<br>File: gis_bishkek_adm0.rar (shapefiles of administrative boundaries)<br>File: gis_bishkek_multiplelayers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> <li>CAMx output (units is ug/m3)<br>File: bishkek_camx_pm25_monthlyavg.csv (see the grid file for mapping)</li> <li>Gridded model-ready emissions inventory<br>File: bishkek_gridded_emissions_2018.rar (see the grid file for mapping)<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_bishkek-grids-pop.rar</li> <li>Ambient monitoring data<br>File: bishkek_monitoring_claritysensor_2021.xlsx<br>File: bishkek_monitoring_us.embassy2019-2024.rar</li> </ol>
Data Supporting "Air-Sea Momentum Exchange with Explicit Wind-Wave-Current Coupling and Effects on Hurricane Structure and Impacts"
<p>This dataset produced the figures for the Air-Sea Momentum Exchange with Explicit Wind-Wave-Current Coupling and Effects on Hurricane Structure and Impacts Manusrcipt.</p>
Data to support "Reduced winter-time Clear Air Turbulence in the trans-Atlantic region under Stratospheric Aerosol Injection by Katie L Barnes, Anthony C Jones, Paul D Williams and James M Haywood, Submitted to Geophysical Research Letters, November 2024"
<p>This dataset contains all the UKESM1 climate model output needed to reproduce Figures 1-4 in the paper "Reduced winter-time Clear Air Turbulence in the trans-Atlantic region under Stratospheric Aerosol Injection by Katie L Barnes, Anthony C Jones, Paul D Williams and James M Haywood, Submitted to Geophysical Research Letters, November 2024". UKESM1 is the Met Office Earth System Model (N96 resolution) with simulations using a fully-coupled ocean-atmosphere following CMIP6 protocol.</p> <p>All files are CF-1.7 compliant and in NetCDF format with appropriate metadata. Each file contains 6 hourly data for the 30 simulated years used in the paper (50 for the pre-industrial control period).</p> <p>The following simulations are included, following ScenarioMIP and GeoMIP specifications:</p> <p>Folder | Simulation name | Time period<br>-------------------------------------------------------------<br>piControl | PiControl | 2250-2299<br>historical | Historical | 1985-2014<br>ssp245 | SSP2-4.5 | 2070-2099<br>ssp585 | SSP5-8.5 | 2070-2099<br>g6sulfur | G6sulfur | 2070-2099</p> <p>The files use CMIP-like naming conventions.</p> <p>VARIABLE_LEVEL_TIMEPERIOD_MODEL_EXPERIMENT_STARTDATE-ENDDATE_SEASON.nc</p> <p>The variables comprise:</p> <p>Short name | Description (units, if any)<br>-------------------------------------------------------------<br>ua | zonal wind (m.s-1)<br>va | meridional wind (kg.m-3)<br>ta | air temperature (K)</p> <p>All figures in the paper were produced using Python (3.11) and Iris scientific analysis software.</p> <p>All data is Crown Copyright, Met Office, and is made available under the terms of the Non-Commercial Government Licence: http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/</p> <p>Description of the UKESM1 model is provided in Sellar, A. A., et al.: UKESM1: Description and evaluation of the U.K. Earth System Model. Journal of Advances in Modeling Earth Systems, 11, 4513–4558. https://doi.org/10.1029/2019MS001739, 2019.</p> <p>Description of the ScenarioMIP specifications are provided here: O'Neill, B. C., et al.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.</p> <p>Description of G6sulfur is provided here: Kravitz, B., et al. : The Geoengineering Model Intercomparison Project Phase 6 (GeoMIP6): simulation design and preliminary results, Geosci. Model Dev., 8, 3379–3392, https://doi.org/10.5194/gmd-8-3379-2015, 2015. </p>
Supporting data for ''Nonlinearity of the cloud response postpones climate penalty of mitigating air pollution in polluted regions"
<p>Supporting data for our work on Nature Climate Change. You will find:</p> <p> - python codes for generating the plots in the manuscript.</p>
Noninvasive Respiratory Support in COVID-19 (CATCOVID-AIR)
ClinicalTrials.gov study NCT04668196. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison of Static Air Support Devices (Repose®) and Alternating-Pressure Devices in the Prevention of Pressure Ulcers
ClinicalTrials.gov study NCT03597750. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Pressure Support Ventilation Versus Continuous Positive Air Way Pressure (CPAP) Using I Gelin Adult Patients,
ClinicalTrials.gov study NCT05116839. IPD Sharing: NO. Countries: 1. Publications: 11.
AIR Support: Artificially Intelligent Robot (AIR) Support for Pediatric Asthma Education
ClinicalTrials.gov study NCT07387718. IPD Sharing: NO. Countries: 1. Publications: 1.
Detectable urbanization effect in observed surface air temperature data series in Pyongyang region, DPR Korea-Supporting Information-data
<p>This is the calculated dataset as supplemental information of a paper entitled "Detectable urbanization effect in observed surface air temperature data series in Pyongyang region, DPR Korea", which will be likely published in Geophysical Research Letters.</p> <p> </p> <p> </p> <p> </p>
AIRS/Aqua L3 Daily Support Daily Product (AIRS+AMSU+HSB) 1 degree x 1 degree V006 (AIRH3SPD) at GES DISC
The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.
AIRS/Aqua L3 Daily Support Product (AIRS-only) 1 degree x 1 degree V006 (AIRS3SPD) at GES DISC
The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.
Aqua/AIRS L3 Daily Support Product (AIRS-only) 1 degree x 1 degree V7.0 at GES DISC
The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in thebox.
AIRS/Aqua L3 8-day Support Product (AIRS-only) 1 degree X 1 degree V006 (AIRS3SP8) at GES DISC
The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The L3 support products are similar to the L3 standard products but contain fields which are not fully validated, or are inputs or intermediary values. Because no quality control information is available for some of these fields, values from failed retrievals may be included.
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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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Annotated Behaviour and Observability Dataset (ABODe)
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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.