A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter

Statistical methods, particularly machine learning models, have gained significant popularity in air quality predictions. These prediction models are commonly trained using the historical measurement datasets independently collected at the environmental monitoring stations and their operational forecasts in advance using inputs of the real-time...

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

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...

4DEnVar-based inversion system for ammonia emission estimation in China through assimilating IASI ammonia retrievals

Atmospheric ammonia has been hazardous to the environment and human health for decades. Current inventories are usually constructed in a bottom-up manner and subject to uncertainties and incapable of reproducing the spatiotemporal characteristics of ammonia emission. Satellite measurements, for example, Infrared Atmospheric Sounder...

Inverse modeling of the 2021 spring super dust storms in East Asia

Last spring, super dust storms reappeared in East Asia after being absent for one and a half decades. The event caused enormous losses in both Mongolia and China. Accurate simulation of such super sandstorms is valuable for the quantification of health damage, aviation risks, and profound impacts on the Earth system, but also to reveal the...

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large...