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A platform for research: civil engineering, architecture and urbanism
Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter
https://orcid.org/0000-0001-9773-0488
https://orcid.org/0000-0002-2868-9343
https://orcid.org/0000-0002-1653-4854
Abstract 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.
Highlights A dust storm forecasting system is developed by coupling LOTOS-EUROS and EnKF. Dust forecast is improved when data assimilation is applied. LEnKF is proved to be superior than EnKF in dust storm forecasting. Sensitivities of dust forecast to the localization distance in LEnKF are exploited.
Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter
https://orcid.org/0000-0001-9773-0488
https://orcid.org/0000-0002-2868-9343
https://orcid.org/0000-0002-1653-4854
Abstract 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.
Highlights A dust storm forecasting system is developed by coupling LOTOS-EUROS and EnKF. Dust forecast is improved when data assimilation is applied. LEnKF is proved to be superior than EnKF in dust storm forecasting. Sensitivities of dust forecast to the localization distance in LEnKF are exploited.
Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter
https://orcid.org/0000-0001-9773-0488
https://orcid.org/0000-0002-2868-9343
https://orcid.org/0000-0002-1653-4854
Abstract 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.
Highlights A dust storm forecasting system is developed by coupling LOTOS-EUROS and EnKF. Dust forecast is improved when data assimilation is applied. LEnKF is proved to be superior than EnKF in dust storm forecasting. Sensitivities of dust forecast to the localization distance in LEnKF are exploited.
Dust storm forecasting through coupling LOTOS-EUROS with localized ensemble Kalman filter
Pang, Mijie (author) / Jin, Jianbing (author) / Segers, Arjo (author) / Jiang, Huiya (author) / Fang, Li (author) / Lin, Hai Xiang (author) / Liao, Hong (author)
Atmospheric Environment ; 306
2023-05-05
Article (Journal)
Electronic Resource
English
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