On the Representation of Spatial Uncertainties with Stochastic Simulation in Land Data Assimilation
Xujun Han + and Xin Li
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu, 730000, China
Abstract. We use the random simulation and geostatistical sequential simulation to represent the model uncertainties by generating the uncertain input ensembles in land data assimilation, as the proper representation of the spatial uncertainties is very important to ensembles-based filter land assimilation methods and the efficiency of the filter depends on the proper representation of the model noise statistics. The method outlined in this paper explicitly acknowledges three sources of uncertainty and takes the spatial structure of the variables into consideration. To restrict the simulation interval of the uncertain inputs, the geostatistical interpolation technique, the geostatistical extrapolation technique and the truncated normal random number generator were applied. Simulation results from these uncertain inputs indicated that this method was sufficient to guarantee the separation of the soil moisture ensembles and easy to introduce the non additive noise. We proved the applicability of the stochastic simulation in representing the model spatial uncertainties in the land data assimilation.
Keywords: random simulation, geostatistics, sequential Gaussian simulation, data assimilation.
In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).