Along with the improvement of numerical models and the enhancement of observation technology, data assimilation has become an efficient method that can further improve numerical prediction level. Since the seventies of last century, data assimilation has been used in air quality prediction, and has become a new direction in the research on the atmospheric environmental science. The meaning of data assimilation is briefly described, and the basic principle, advantages and drawbacks of Kalman filter, four-dimensional variational assimilation and nudging methods are introduced in detail, then the research progress of data assimilation in air quality prediction is mainly reviewed. The existing problems and further research directions of data assimilation applied in air quality prediction are discussed
Progress of research on artificial neural network in air pollution prediction
Costabile F;
2006
Abstract
Along with the improvement of numerical models and the enhancement of observation technology, data assimilation has become an efficient method that can further improve numerical prediction level. Since the seventies of last century, data assimilation has been used in air quality prediction, and has become a new direction in the research on the atmospheric environmental science. The meaning of data assimilation is briefly described, and the basic principle, advantages and drawbacks of Kalman filter, four-dimensional variational assimilation and nudging methods are introduced in detail, then the research progress of data assimilation in air quality prediction is mainly reviewed. The existing problems and further research directions of data assimilation applied in air quality prediction are discussedI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
