MODEL BASED OBSERVATION LOCALIZATION WEIGHTING FUNCTION FOR AMAZON MAINSTREAM

抄録

 Data assimilation techniques are becoming popular in estimating hydraulic variables in ungauged basins with the recent advancements in the satellite technology. The Local Ensemble Transformation Kalman Filter (LETKF), which limits the assimilation domain by a “local patch”, is an efficient method for a global-scale data assimilation, but the optimization of the size and weighting function of the local patch is still challenging especially for river hydrodynamic models. Here we propose a method to estimate a reasonable local patch parameters, by fitting a Gaussian semi-variogram to the transformed Water Surface Elevation (WSE) data and defining the autocorrelation length for each river pixel. WSE simulated by CaMa-Flood hydrodynamic model was de-trended, seasonality removed and standardized to make the data suitable for semi-variogram analysis. A case study over the Amazon mainstem suggested that the auto-correlation lengths for upstream and downstream of Obidos GRDC location were derived respectively as 1886.69 km and 688.66 km. The semi-variogram analysis indicated that the river pixels of entire mainstream of the Amazon are correlated together. The estimated auto-correlation length and weighing function could be useful to determine the optimum parameters of the LETKF local patch.