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.
Highlights
Aerosol vertical distribution has a significant impact on the estimation of the global budget of aerosols on climate (Torres et al, 1998; Peters et al, 2011; Meyer et al, 2013), planetary boundary layer (PBL) stability (Li et al, 2017b, 2020b; Su et al, 2020), the understanding of the aerosol evolutionary process and surface concentration (Chen et al, 2009; Liu et al, 2018; 35 Quan et al, 2020) and the retrieval of aerosol optical properties from passive sensors (Li et al, 2020a).In a broad sense, aerosol optical depth (AOD) measurements, which are the vertical integral of aerosol extinction coefficients, can be deemed to include vertical information and have relatively low uncertainty (Holben et al, 2001)
The data storage subsystem of the Big Data Cloud Service Infrastructure Platform (BDCSIP) can be accessed by all computing nodes through InfiniBand, and the total capacities are more than 25 PB
We couple the atmospheric chemistry-transport model Nested Air Quality Prediction Model System (NAQPMS) with the Parallel Data Assimilation Framework (PDAF) online for the first time to establish a high-performance ensemble filter system NAQPMS-PDAF to mainly investigate the impact of assimilating measurements, including aerosol vertical information
Summary
Aerosol vertical distribution has a significant impact on the estimation of the global budget of aerosols on climate (Torres et al, 1998; Peters et al, 2011; Meyer et al, 2013), planetary boundary layer (PBL) stability (Li et al, 2017b, 2020b; Su et al, 2020), the understanding of the aerosol evolutionary process and surface concentration (Chen et al, 2009; Liu et al, 2018; 35 Quan et al, 2020) and the retrieval of aerosol optical properties from passive sensors (Li et al, 2020a).In a broad sense, aerosol optical depth (AOD) measurements, which are the vertical integral of aerosol extinction coefficients, can be deemed to include vertical information and have relatively low uncertainty (Holben et al, 2001). AOD with passive remote sensors can only be used to investigate the qualitative impact of aerosol vertical distribution (Zhu et al, 2018) and quantitative relationship with surface concentration, which neglects vertical information to some extent (Li et al, 2018b; 40 Yang et al, 2019; Wei et al, 2021). An effective tool to measure aerosol stratification with active remote sensors, is widely used in vertical research on aerosols (Shimizu, 2004; Liu et al, 2013; Sicard et al, 2015; Proestakis et al, 2019; Mehta et al, 2021) and is generally composed of ground-based and space-borne lidar. The three-dimensional structure of aerosols, especially their vertical structure (Solazzo et al, 2013; Kipling et al, 2016), can be simulated by the atmospheric chemistry-transport model (CTM), which has large uncertainties in chemical initial/boundary conditions, meteorological initial/boundary conditions, emissions, and 50 parameterizations of physical and chemical processes (Wu et al, 2020b) and may differ substantially from the real situation
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