the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GHOST: A globally harmonised dataset of surface atmospheric composition measurements
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).
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RC1: 'Comment on essd-2023-397', Anonymous Referee #1, 12 Jun 2024
The GHOST harmonised dataset is a valuable contribution to the surface atmospheric composition measurement community in that it brings together key data from 38 observing networks in a standardized way. The dataset has the potential to streamline data workflows for the atmospheric composition measurement community and also opens the possibility of conducting longer scale spatial and temporal analyses. I rated the uniqueness of this manuscript as 'good' due to the fact that this dataset could be replicated if needed. However, the cost and effort required to replicate the dataset would be high, making the open sharing of this data important. The usefulness of this manuscript and the corresponding dataset is excellent. I especially appreciated the thorough definitions of the variables and metadata included in the appendix. As a researcher, I wish that more datasets had such clear and thorough documentation on variables and metadata. The manuscript and dataset is complete and also includes access to the code.
The presentation quality should be improved as the manuscript is lengthy and long articles are not expected for ESSD. More specifically, section 3 (GHOST processing workflow) should be streamlined and made more concise in order to improve the presentation quality.
With some revisions, especially to section 3, this manuscript will be a nice contribution to the Earth science community.
Citation: https://doi.org/10.5194/essd-2023-397-RC1 -
RC2: 'Comment on essd-2023-397', Anonymous Referee #2, 16 Jun 2024
This is an excellent paper and an important contribution to our field. Atmospheric composition observations are important to society and are needed to quantity trends and current levels of pollutants in the atmosphere, to evaluate models, to assimilate with models to provide optimal estimates of the state of the atmosphere, among others. But atmospheric composition measurements occur in many different and disparate locations, and it is often very difficult to discover and access the data. The approach developed and described in this paper goes a long way to improving the discovery and use of atmospheric composition data. It is a major development, and the authors are to be congratulated.
The paper is well written, and the relevant information is made available, and methods described in appropriate detail. My only comment is related to how can this workflow be made even more useful. Specifically, much of the atmospheric composition data sits in individual measurement sites and data available and described in publications. How easy is it for a single measurement site to make their data available to GHOST? Can they provide the DOI and meta data etc. in an easy way?
Citation: https://doi.org/10.5194/essd-2023-397-RC2
Data sets
GHOST: A globally harmonised dataset of surface atmospheric composition measurements Dene Bowdalo https://doi.org/10.5281/zenodo.10637449
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