Preprints
https://doi.org/10.5194/gmd-2021-374
https://doi.org/10.5194/gmd-2021-374

Submitted as: development and technical paper 07 Jan 2022

Submitted as: development and technical paper | 07 Jan 2022

Review status: this preprint is currently under review for the journal GMD.

An aerosol vertical data assimilation system (NAQPMS-PDAF v1.0): development and application

Haibo Wang1,4,, Ting Yang1,3,, Zifa Wang1,3,4, Jianjun Li2, Wenxuan Chai2, Guigang Tang2, Lei Kong1,4, and Xueshun Chen1,3 Haibo Wang et al.
  • 1The State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2China national Environmental Monitoring Centre, Beijing
  • 3Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
  • 4College of Earth and Planetary Science, University of Chinese Academy of Sciences, Beijing 100029, China
  • These authors contributed equally to this work.

Abstract. Aerosol vertical stratification information is important for global climate and planetary boundary layer (PBL) stability, and no single method can obtain spatiotemporally continuous vertical profiles. This paper develops an online data assimilation (DA) framework for the Eulerian atmospheric chemistry-transport model (CTM) Nested Air Quality Prediction Model System (NAQPMS) with the Parallel Data Assimilation Framework (PDAF) as the NAQPMS-PDAF for the first time. Online coupling occurs via a memory-based approach with two-level parallelization, and the arrangement of state vectors during the filter is specifically designed. Scaling tests provide evidence that the NAQPMS-PDAF can make efficient use of parallel computational resources for up to 2.5 k processors with weak scaling efficiency up to 0.7. One-month-long aerosol extinction coefficient profiles measured by the ground-based lidar and the concurrent hourly surface PM2.5 are solely and simultaneously assimilated to investigate the performance and application of the DA system. The hourly analysis and subsequent one-hour simulation are validated through lidar and surface PM2.5 measurements assimilated and not assimilated. The results show that lidar DA can significantly improve the underestimation of aerosol loading, especially at a height of approximately 400 m in the free-running (FR) experiment, with the BIAS changing from −0.20 (−0.14) 1/km to −0.02 (−0.01) 1/km and correlation coefficients increasing from 0.33 (0.28) to 0.91 (0.53) averaged over sites with measurements assimilated (not assimilated). Compared with the FR experiment, simultaneously assimilating PM2.5 and lidar can have a more consistent pattern of aerosol vertical profiles with a combination of surface PM2.5 and lidar, independent extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET). Lidar DA has a larger temporal impact than that in PM2.5 DA but has deficiencies in subsequent quantification on the surface PM2.5. The proposed NAQPMS-PDAF has great potential for further research on the impact of aerosol vertical distribution.

Haibo Wang et al.

Status: open (until 04 Mar 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Haibo Wang et al.

Haibo Wang et al.

Viewed

Total article views: 212 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
171 37 4 212 2 1
  • HTML: 171
  • PDF: 37
  • XML: 4
  • Total: 212
  • BibTeX: 2
  • EndNote: 1
Views and downloads (calculated since 07 Jan 2022)
Cumulative views and downloads (calculated since 07 Jan 2022)

Viewed (geographical distribution)

Total article views: 215 (including HTML, PDF, and XML) Thereof 215 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Jan 2022
Download
Short summary
In this paper, we develop an online data coupled assimilation system NAQPMS-PDAF with the Eulerian atmospheric chemistry-transport model. NAQPMS-PDAF allows efficient use of large computational resources. The application and performance of the system are investigated by assimilating one month of vertical aerosol observations. The results show that NAQPMS-PDAF can significantly improve the performance of aerosol vertical structure simulation and reduce the uncertainty to a large extent.