부산대학교 도서관

• An observer integrates measurements with large-scale, nonlinear atmospheric models. • Model error exponentially decays by adjusting scaling factors on uncertain processes. • Variance in estimated parameter is tuned based on measurement uncertainty. • Aerosol optical depth measurements are utilized to estimate sea spray emission flux. Atmospheric aerosol models simulate the concentration of gaseous pollutants and particulate matter in the atmosphere. These models include significant mismatch from the true atmosphere resulting from uncertainty in the inputs including meteorology and emission fields, extrapolation and simplification of experimentally-derived relationships for the process dynamics, and possibly un-accounted for processes. In this paper we estimate time-varying parameters that adjust uncertain dynamics to account for some of the model mismatch. We show how to design a passivity-based input observer (PBIO) using proportional feedback so that the model-measurement error exponentially decays. As a case study, the PBIO is applied to an atmospheric aerosol model simulating a uniformly mixed control volume to estimate the particulate matter emission rate. A relationship between known measurement noise and uncertainty in the PBIO-estimated unknown parameters is developed and illustrated in a case study that utilizes noisy measurements. [ABSTRACT FROM AUTHOR]