Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter

Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter

Pang, Mijie (Nanjing University of Information Sciences and Technology)
Jin, J. (Nanjing University of Information Sciences and Technology)
Segers, Arjo (DIANA FEA)
Jiang, Huiya (Nanjing Agricultural University)
Fang, Li (Nanjing University of Information Sciences and Technology)
Lin, H.X. (TU Delft Mathematical Physics; Institute of Environmental Sciences; Universiteit Leiden)
Liao, Hong (Nanjing University of Information Sciences and Technology)

2023

Super dust storms re-occurred over East Asia in 2021 spring and casted great health damages and property losses. It is essential to achieve an accurate dust forecast to reduce the damage for early warning. The forecasting system fundamentally relies on a numerical model which can forecast the full evolution of dust storms. However, large uncertainties exist in model forecasts. Meanwhile, various near-real-time observations are available that contain valuable dust information. A dust storm forecasting system is here developed through coupling a chemical transport model, LOTOS-EUROS, and Localized EnKF (LEnKF) assimilation approach. The assimilations are carried out via an interface of our self-designed assimilation toolbox, PyFilter v1.0. Ground-based PM₁₀ measurements from air quality monitoring network are assimilated. Sequential assimilation tests are carried out over the 2021 spring super dust storms. The results show that the assimilation-based forecasting system produces a promising dust forecast than model-only forecast, and the improvements is also validated through comparing to the independent MODIS aerosol optical depth (AOD). Superior performance is obtained when LEnKF is implemented, as the localization helps EnKF in resolving the PM₁₀ measurements that have a large spatial variability with limited ensemble members. In addition, sensitivity experiments are conducted to exploit the distance-dependent localization for the LEnKF. Considering both cases, the optimal choice of the distance is tested to be around 500 km: the larger distance is less effective in removing the spurious correction, while the smaller one easily falls into the local optimum and the model would become divergent rapidly.

Data assimilation
Dust aerosol
EnKF

http://resolver.tudelft.nl/uuid:3dfaa6cb-4d2f-4839-874a-918d2e56dad6

https://doi.org/10.1016/j.atmosenv.2023.119831

2023-11-13

1352-2310

Atmospheric Environment, 306

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Mijie Pang, J. Jin, Arjo Segers, Huiya Jiang, Li Fang, H.X. Lin, Hong Liao