Improving PM_2.5 Forecasts in China Using an Initial Error Transport Model

Abstract

Efforts of using data assimilation to improve PM 2.5 forecasts have been hindered by the limited number of species and incomplete vertical coverage in the observations. The common practice of initializing a chemical transport model (CTM) with assimilated initial conditions (ICs) may lead to model imbalances, which could confine the impacts of assimilated ICs within a day. To address this challenge, we introduce an initial error transport model (IETM) approach to improving PM 2.5 forecasts. The model describes the transport of initial errors by advection, diffusion, and decay processes and calculates the impacts of assimilated ICs separately from the CTM. The CTM forecasts with unassimilated ICs are then corrected by the IETM output. We implement our method to improve PM 2.5 forecasts over central and eastern China. The reduced root-mean-square errors for 1-, 2-, 3-, and 4-day forecasts during January 2018 were 51.2, 27.0, 16.4, and 9.4 μg m −3 , respectively, which are 3.2, 6.9, 8.6, and 10.4 times those by the CTM forecasts with assimilated ICs. More pronounced improvements are found for highly reactive PM 2.5 components. These and similar results for July 2017 suggest that our method can enhance and extend the impacts of the assimilated data without being affected by the imbalance issue.

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

“…However, although groundbased lidar generally has more intensive coverage than space-borne measurements, it can only provide single-point information with limited spatial coverage. 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 nonetheless has large uncertainties in chemical initial/boundary conditions, meteorological initial/boundary conditions, emissions, and parameterizations of physical and chemical processes (Wu et al, 2020b) and may differ substantially from the real situation.…”