ALBERT

Description: Aerodynamic roughness height (Zom) is a key parameter required in land surface hydrological model, since errors in heat flux estimations are largely dependent on accurate optimization of this parameter. Despite its significance, it remains an uncertain parameter that is not easily determined. This is mostly because of non-linear relationship in Monin-Obukhov Similarity (MOS) and unknown vertical characteristic of vegetation. Previous studies determined aerodynamic roughness using traditional wind profile method, remotely sensed vegetation index, minimization of cost function over MOS relationship or linear regression. However, these are complicated procedures that presume high accuracy for several other related parameters embedded in MOS equations. In order to simplify a procedure and reduce the number of parameters in need, this study suggests a new approach to extract aerodynamic roughness parameter via Ensemble Kalman Filter (EnKF) that affords non-linearity and that requires only single or two heat flux measurement. So far, to our knowledge, no previous study has applied EnKF to aerodynamic roughness estimation, while a majority of data assimilation study has paid attention to land surface state variables such as soil moisture or land surface temperature. This approach was applied to grassland in semi-arid Tibetan area and maize on moderately wet condition in Italy. It was demonstrated that aerodynamic roughness parameter can inversely be tracked from data assimilated heat flux analysis. The aerodynamic roughness height estimated in this approach was consistent with eddy covariance result and literature value. Consequently, this newly estimated input adjusted the sensible heat overestimated and latent heat flux underestimated by the original Surface Energy Balance System (SEBS) model, suggesting better heat flux estimation especially during the summer Monsoon period. The advantage of this approach over other methodologies is that aerodynamic roughness height estimated in this way is useful even when eddy covariance data are absent and is time-variant over vegetation growth, as well as is not affected by saturation problem of remotely sensed vegetation index.