Submitted as: methods for assessment of models
02 Jan 2024
Submitted as: methods for assessment of models |  | 02 Jan 2024
Status: this preprint is currently under review for the journal GMD.

Assessment of tropospheric ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran

Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti

Abstract. Tropospheric ozone time series consist of the effects of various scales of motion, from meso to large timescales, which is often challenging for global models to capture. This study uses two global datasets, namely the reanalysis and daily forecast of the Copernicus Atmospheric Monitoring Service (CAMS), to assess the capability of these prodcuts in presenting ozone’s features on regional scales. We obtained 17 relevant meteorological and several pollutant species, such as O3, CO, NOx, etc., from CAMS. Furthermore, we employ in situ measured ozone at 27 urban stations over Iran for the year 2020. We decompose the datasets into three spectral components, i.e., short (S), medium (M), and long (L) terms. To cope with the scaling issue between the measured data and the CAMS’ products, we downscale the datasets using a Long Short-Term Memory (LSTM) neural network. We only evaluate the S and M terms of the models against those of the observed datasets for all stations. Results show correlation coefficients larger than 0.7 for S and about 0.95 for M in both models. It turns out that both datasets demonstrate more correspondence precision for the M component than that for the S. The performance of the models varies across cities, for example, the highest error is for areas with high emissions of O3 precursors. The robustness of the results is confirmed by performing an additional downscaling method.

Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti

Status: open (until 27 Feb 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-226', Anonymous Referee #1, 24 Jan 2024 reply
  • CEC1: 'Comment on gmd-2023-226', Juan Antonio Añel, 26 Jan 2024 reply
    • AC1: 'Reply on CEC1', Najmeh Kaffashzadeh, 27 Jan 2024 reply
  • RC2: 'Comment on gmd-2023-226', Anonymous Referee #2, 15 Feb 2024 reply
Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti
Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti


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Short summary
Reanalysis data have been widely used as an initial condition for the daily forecast of the atmosphere or boundary conditions in regional models, for the study of climate change, and as proxies to complement insufficient in situ measurements. This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology.