Analysis of influence of observation operator on sequential data assimilation through soil temperature simulation with common land model

Abstract An observation operator is a bridge linking the system state vector and observations in a data assimilation system. Despite its importance, the degree to which an observation operator influences the performance of data assimilation methods is still poorly understood. This study aimed to analyze the influences of linear and nonlinear observation operators on the sequential data assimilation through soil temperature simulation using the unscented particle filter (UPF) and the common land model. The linear observation operator between unprocessed simulations and observations was first established. To improve the correlation between simulations and observations, both were processed based on a series of equations. This processing essentially resulted in a nonlinear observation operator. The linear and nonlinear observation operators were then used along with the UPF in three assimilation experiments: an hourly in situ soil surface temperature assimilation, a daily in situ soil surface temperature assimilation, and a moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) assimilation. The results show that the filter improved the soil temperature simulation significantly with the linear and nonlinear observation operators. The nonlinear observation operator improved the UPF's performance more significantly for the hourly and daily in situ observation assimilations than the linear observation operator did, while the situation was opposite for the MODIS LST assimilation. Because of the high assimilation frequency and data quality, the simulation accuracy was significantly improved in all soil layers for hourly in situ soil surface temperature assimilation, while the significant improvements of the simulation accuracy were limited to the lower soil layers for the assimilation experiments with low assimilation frequency or low data quality.