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

Abstract

Abstract. Aerosol vertical stratification 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 based on a memory-based way with two-level parallelization, and the arrangement of state vectors during the filter is specifically designed. Scaling tests demonstrate that the NAQPMS-PDAF can make efficient use of parallel computational resources for up to 25 000 processors with a weak scaling efficiency of up to 0.7. The 1-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 1 h 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 mean bias (BIAS) changing from −0.20 (−0.14) km−1 to −0.02 (−0.01) km−1 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.

Strong Impacts of Regional Atmospheric Transport on the Vertical Distribution of Aerosol Ammonium over Beijing

“…Meanwhile, we have developed an aerosol vertical data assimilation system (NAQPMS-PDAF version 1.0), which has produced highly accurate hourly three-dimensional spatial and temporal variations in aerosol NH 4 + and other chemical components of PM 2.5 . 31 The system assimilated three types of observations, including the vertical components retrieved from ground-based measurements as well as the surface observations of PM 2.5 and its chemical components. The Localized Error Subspace Transformation Kalman Filter method was employed for the assimilation.…”

“…A detailed description can be found in ref . Meanwhile, we have developed an aerosol vertical data assimilation system (NAQPMS-PDAF version 1.0), which has produced highly accurate hourly three-dimensional spatial and temporal variations in aerosol NH 4 + and other chemical components of PM 2.5 …”

NAQPMS-PDAF v2.0: A Novel Hybrid Nonlinear Data Assimilation System for Improved Simulation of PM2.5 Chemical Components

“…The Parallel Data Assimilation Framework (PDAF, https://pdaf.awi.de/trac/wiki) is an open-source and high-expandability software developed by the Alfred Wegener Institute (AWI) in Germany to integrate observations, numerical models, and assimilation systems for DA tasks, widely applied in meteorology, oceanography, land surface and atmospheric chemistry (Kurtz et al, 2016;Nerger et al, 2020;Mingari et al, 2022;Strebel et al, 2022;Wang et al, 2022;Yu et al, 2022). The initial version of PDAF (PDAF v1.0) was released in 2004.…”

Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data

“…The meteorology field was provided by the Weather Research and Forecasting model (WRF), which is driven by Final Analysis data (FNL) from the National Center for Environmental Prediction (NCEP). The outputs of the NAQPMS used in this paper have been assimilated through the Parallel Data Assimilation Framework (PDAF) system, which has a fairly good correlation with measurements (Wang et al, 2022).…”