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GHOST: A globally harmonised dataset of surface atmospheric composition measurements.

Authors :
Bowdalo, Dene
Basart, Sara
Guevara, Marc
Jorba, Oriol
García-Pando, Carlos Pérez
Palomera, Monica Jaimes
Hernandez, Olivia Rivera
Puchalski, Melissa
Gay, David
Klausen, Jörg
Moreno, Sergio
Netcheva, Stoyka
Tarasova, Oksana
Source :
Earth System Science Data Discussions. 3/27/2024, p1-137. 137p.
Publication Year :
2024

Abstract

GHOST: Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, 7,275,148,646 measurements from 1970–2023, of 227 different components, from 38 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties. The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of provided information from the major public reporting networks, saved in 21 data variables, and 163 metadata variables. Extensive effort in particular is put towards the standardisation of measurement process information, and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use), and temporal classifications per measurement (e.g. day / night). A range of standardised network quality assurance flags are associated with each individual measurement. GHOST own quality assurance is also performed and associated with measurements. Measurements prefiltered by some default GHOST quality assurance are also provided. In this paper, we outline all steps undertaken to create the GHOST dataset, and give insights and recommendations for data providers based on experiences gleaned through our efforts. The GHOST dataset is made freely available via the following repository: https://doi.org/10.5281/zenodo.10637449 (Bowdalo, 2024). [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18663591
Database :
Academic Search Index
Journal :
Earth System Science Data Discussions
Publication Type :
Academic Journal
Accession number :
176319467
Full Text :
https://doi.org/10.5194/essd-2023-397