Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8495-2024
https://doi.org/10.5194/gmd-17-8495-2024
Development and technical paper
 | 
29 Nov 2024
Development and technical paper |  | 29 Nov 2024

NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components

Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2024-78', Anonymous Referee #1, 06 Jul 2024
  • RC2: 'Comment on gmd-2024-78', Anonymous Referee #2, 22 Jul 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Ting Yang on behalf of the Authors (13 Aug 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (19 Aug 2024) by Lele Shu
RR by Anonymous Referee #1 (31 Aug 2024)
RR by Anonymous Referee #3 (18 Sep 2024)
ED: Publish as is (19 Sep 2024) by Lele Shu
AR by Ting Yang on behalf of the Authors (21 Sep 2024)  Author's response   Manuscript 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Ting Yang on behalf of the Authors (11 Nov 2024)   Author's adjustment   Manuscript
EA: Adjustments approved (17 Nov 2024) by Lele Shu
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.