Development of the Eulerian atmospheric transport model GEARN-FDM: Validation against the European tracer experiment

Kadowaki, Masanao; Katata, Genki; Terada, Hiroaki; Nagai, Hiroki

doi:10.1016/j.apr.2016.10.012

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…35 The algorithm employs dual-linear segment approximations and a special treatment near the local maxima and minima to preserve extremes and reduce numerical diffusion. It has been widely used in CTMs to advect chemical species with nonnegative concentrations; 34,36,37 however, it does not require positivedefinite initial fields, and negative quantities can be advected. A two-dimensional implementation of the scheme is described in SI Section S3 and applied for the horizontal advection of forecast errors.…”

Section: ■ Introductionmentioning

confidence: 99%

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

Zheng

Zhu

et al. 2020

Environ. Sci. Technol.

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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.

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