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GHOST: a globally harmonised dataset of surface atmospheric composition measurements

Title: GHOST: a globally harmonised dataset of surface atmospheric composition measurements
Authors: D. Bowdalo; S. Basart; M. Guevara; O. Jorba; C. Pérez García-Pando; M. Jaimes Palomera; O. Rivera Hernandez; M. Puchalski; D. Gay; J. Klausen; S. Moreno; S. Netcheva; O. Tarasova
Source: Earth System Science Data, Vol 16, Pp 4417-4495 (2024)
Publisher Information: Copernicus Publications, 2024.
Publication Year: 2024
Collection: LCC:Environmental sciences; LCC:Geology
Subject Terms: Environmental sciences; GE1-350; Geology; QE1-996.5
Description: GHOST (Globally Harmonised Observations in Space and Time) represents one of the biggest collections of harmonised measurements of atmospheric composition at the surface. In total, 7 275 148 646 measurements from 1970 to 2023, of 227 different components from 38 reporting networks, are compiled, parsed, and standardised. The 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 the information provided by major public reporting networks, which is saved in 21 data variables and 163 metadata variables. Extensive effort in particular is made 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 or night). A range of standardised network quality assurance flags is associated with each individual measurement. GHOST's own quality assurance is also performed and associated with measurements. Measurements pre-filtered by the 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 the experiences gleaned through our efforts. The GHOST dataset is made freely available via the following repository: https://doi.org/10.5281/zenodo.10637449 (Bowdalo, 2024a).
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1866-3508; 1866-3516
Relation: https://essd.copernicus.org/articles/16/4417/2024/essd-16-4417-2024.pdf; https://doaj.org/toc/1866-3508; https://doaj.org/toc/1866-3516
DOI: 10.5194/essd-16-4417-2024
Access URL: https://doaj.org/article/2eebc8eec1ba47c9a8189d7ab6400a58
Accession Number: edsdoj.2eebc8eec1ba47c9a8189d7ab6400a58
Database: Directory of Open Access Journals