Preprints
https://doi.org/10.5194/gmd-2023-216
https://doi.org/10.5194/gmd-2023-216
Submitted as: methods for assessment of models
 | 
09 Feb 2024
Submitted as: methods for assessment of models |  | 09 Feb 2024
Status: a revised version of this preprint is currently under review for the journal GMD.

Observational operator for fair model calibration with ground NO2 measurements

Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao

Abstract. Measurements collected from ground monitoring stations have gained popularity as a valuable data source for calibrating numerical models and correcting model errors through data assimilation. Both model calibration and assimilation are driven by the penalty quantified by simulation-minus-observations. However, the penal forces are challenged by the existence of a spatial scale disparity between model simulations and observations. The Chemical Transport Models (CTMs) allow the division of the atmosphere into grid cells, yet their spatial resolution may not align with the limited range of in-situ measurements, particularly for short-lived air pollutants. Within a broad grid pattern, air pollutant concentrations can exhibit significant heterogeneity due to their rapid generation and dissipation. Ground observations with traditional methods (including nearest search and grid mean) are less representative when compared to model simulations. This study develops a new land-use-based representative (LUBR) observational operator to generate spatially representative gridded observation for model calibration and evaluation. It incorporates high-resolution urban-rural land use data to address intra-grid variability. The LUBR operator is validated to consistently provide insights that align with satellite OMI measurements. It is an effective solution to accurately quantify these spatial scale mismatches and further resolve them via assimilation. Model calibrations with 2015–2017 NO2 measurement in China demonstrates biases and errors differed substantially when the LUBR and other operator are used, respectively. The results highlight the importance of considering fine-scale urban-rural differences when comparing models and observations, especially for short-lived pollutants like NO2.

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Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-216', Anonymous Referee #1, 14 Mar 2024
    • AC1: 'Reply on RC1', Jianbing Jin, 08 May 2024
  • RC2: 'Comment on gmd-2023-216', Anonymous Referee #2, 28 Mar 2024
    • AC2: 'Reply on RC2', Jianbing Jin, 08 May 2024
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao

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Short summary
The model evaluation against ground observations is usually unfair. The former simulates mean status over coarse grids while the latter represents the very surrounding atmosphere. To solve this, we proposed a new approach called "LUBR" that considers the intra-grid variance. The LUBR is validated to provide insights that align with satellite OMI measurements. The results highlight the importance of considering fine-scale urban-rural differences when comparing models and observation.