Application of dynamic data driven application system in environmental science

Abstract

The paradigm of dynamic data driven application system (DDDAS) has been proposed as a framework to analyze and predict the character and behavior of complex systems that influence computational models significantly. Its accuracy and efficiency lies in its ability to integrate observations on different temporal and spatial scales from real-time sensors, and in its measurement steering and controlling capabilities. Many problems in environmental sciences are nonlinear and complex, impossible to solve by using input/output sequence flows without feedback control. Nonlinear system efficiency depends on measurement control and steering, on-line data assimilation, and model selection with dynamic optimization. Compared with traditional methods, DDDAS possesses the capacity to overcome these limitations. This paper discusses DDDAS and classifies typical cases of its application in environmental sciences into three levels of paradigm. Short reviews of multi-model simulation and data assimilation are provided for practical use. Recent developments and future perspectives are reviewed. Future work may address determining automatically where, when, and how to acquire real-time data, and its integration with GIS, to improve efficiency and accuracy. User-generated content will find wide application in the future. Considering the differences between DDDAS and other data-driven methods in solving the same nonlinear complex system problems, a combination of nonlinear science and chaos theory is advocated.