Preprints
https://doi.org/10.5194/gmd-2024-73
https://doi.org/10.5194/gmd-2024-73
Submitted as: methods for assessment of models
 | 
17 Jun 2024
Submitted as: methods for assessment of models |  | 17 Jun 2024
Status: this preprint is currently under review for the journal GMD.

Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator

Maria Rosa Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham

Abstract. This work presents the first step in the development of the VISION toolkit, a set of python tools that allows for easy, efficient and more meaningful comparison between global atmospheric models and observational data. Whilst observational data and modelling capabilities are expanding in parallel, there are still barriers preventing these two data sources to be used in synergy. This arises from differences in spatial and temporal sampling between models and observational platforms: observational data from a research aircraft, for example, is sampled on specified flight trajectories at very high temporal resolution. Proper comparison with model data requires generating, storing and handling a large amount of highly temporally resolved model files, resulting in a process which is data, labour, and time intensive. In this paper we focus on comparison between model data and in-situ observations (from aircrafts, ships, buoys, sondes etc.). A stand-alone code, In-Situ Observation simulator, or ISO_simulator in short, is described here: this software reads modelled variables and observational data files and outputs model data interpolated in space and time to match observations. This model data is then written to NetCDF files that can be efficiently archived, due to their small sizes, and directly compared to observations. This method achieves a large reduction in the size of model data being produced for comparison with flight and other in-situ data. By interpolating global, gridded, hourly files onto observations locations, we reduce data output for a typical climate resolution run, from ~3 Gb per model variable per month to ~15 Mb per model variable per month (a 200 times reduction in data volume). The VISION toolkit is fast and easy to use, therefore enabling the exploitation of large observational datasets spanning decades, to be used for large scale model evaluation. Although this code has been initially tested within the Unified Model (UM) framework, which is shared by the UK Earth System Model (UKESM), it was written as a flexible tool and it can be extended to work with other models.

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Maria Rosa Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham

Status: open (until 26 Aug 2024)

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Maria Rosa Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham

Data sets

UKESM1 hourly modelled ozone for comparison to observations N. L. Abraham and M. R. Russo https://catalogue.ceda.ac.uk/uuid/300046500aeb4af080337ff86ae8e776

Continuous Cape Verde Atmospheric Observatory Observations L. J. Carpenter et al. https://catalogue.ceda.ac.uk/uuid/81693aad69409100b1b9a247b9ae75d5

FAAM ozone dataset 2010 to 2020. NERC EDS Centre for Environmental Data Analysis M. R. Russo, N. L. Abraham, and FAAM Airborne Laboratory https://catalogue.ceda.ac.uk/uuid/8df2e81dbfc2499983aa87781fb3fd5a

ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols, Version 2 S. C. Wofsy et al. https://doi.org/10.3334/ORNLDAAC/1925

Model code and software

NCAS-VISION/VISION-toolkit: 1.0 M. R. Russo, S. L. Bartholomew, and N. L. Abraham https://doi.org/10.5281/ZENODO.10927302

Maria Rosa Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham

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Short summary
Observational data and modelling capabilities are expanding in recent years, but there are still barriers preventing these two data sources to be used in synergy. Proper comparison requires generating, storing and handling a large amount of data. This manuscript describes the first step in the development of a new set of software tools, the ‘VISION toolkit’, which can enable the easy and efficient integration of observational and model data required for model evaluation.