Improving PM2.5 Forecasts in China Using an Initial Error Transport Model
- 4 August 2020
- journal article
- research article
- Published by American Chemical Society (ACS) in Environmental Science & Technology
- Vol. 54 (17), 10493-10501
- https://doi.org/10.1021/acs.est.0c01680
Abstract
The efforts of using data assimilation to improve PM2.5 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 PM2.5 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 PM2.5 forecasts over central and eastern China. The reduced root-mean-square errors for 1- to 4-day forecasts during January 2018 are 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 PM2.5 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.Funding Information
- Ministry of Science and Technology of the People's Republic of China (2016YFC0207701, 2016YFC0207703, 2018YFC0213100, 2018YFC0213106)
- Natural Science Foundation of Beijing Municipality (Z190001)
- National Natural Science Foundation of China (11671018, 41705108, 71532001, 91644216)
- Beijing Academy of Artificial Intelligence
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