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KORUS-AQ Ancillary Model Data Products

Metadata Updated: December 7, 2023

KORUSAQ_Model_Data features ancillary model data products for the KORUS-AQ field campaign. This product features output from the WRF model, CAM-chem, model inter-comparisons, and GEOS-chem models. Data collection for this product is complete.

The KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea’s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.

Surface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.

The major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

Downloads & Resources

Dates

Metadata Created Date December 1, 2022
Metadata Updated Date December 7, 2023

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date December 1, 2022
Metadata Updated Date December 7, 2023
Publisher NASA/LARC/SD/ASDC
Maintainer
Identifier C2210941541-LARC_ASDC
Data First Published 2021-05-14
Language en-US
Data Last Modified 2022-04-18
Category KORUS-AQ, geospatial
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Citation 2022-01-27. Archived by National Aeronautics and Space Administration, U.S. Government, NASA/LARC/SD/ASDC. https://doi.org/10.5067/ASDC/SUBORBITAL/KORUSAQ_Model_Data_1.
Harvest Object Id 753defd7-583d-4df6-94c1-28e528677e7a
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL https://doi.org/10.5067/ASDC/SUBORBITAL/KORUSAQ_Model_Data_1
Metadata Type geospatial
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Program Code 026:001
Source Datajson Identifier True
Source Hash 15b1295f7f01ad2237ec6cf336a5c908dfea476f0b9e6c5bbfbee3234dfe9193
Source Schema Version 1.1
Spatial
Temporal 2016-04-30T00:00:00Z/2016-06-11T23:59:59.999Z

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