Differential evolution algorithm-based multiple-factor optimization methods for data assimilation

Abstract

The methods of searching for optimized parameters have substantial effects on the forecast accuracy of ensemble data assimilation systems. The selection of these factors is usually performed using trial-and-error methods, and poor parameterizations may lead to filter divergence. Combined with the local ensemble transform Kalman filtering method (LETKF), a technique for an automated search of the best configuration (parameters) of a data assimilation system is proposed. To obtain better assimilation, a differential evolution (DE) algorithm-based multiple-factor parameterization method results in the corresponding circumstances. By combining with fast-searching DE algorithms, we may retrieve the most ideal parameter combinations. Several numerical experiments performed with the Lorenz-96 model show that new methods performed better than the original one-parameter optimization methods. As the basis of DE methods, the best combinations of the local radius and the covariance inflation parameter, which can guarantee the best DA performances in the corresponding circumstances, are retrieved. It is found that the new method is capable of outperforming previous search algorithms under both perfect and imperfect model scenarios, and the calculation cost in Lorenz-96 model is lower. However, how to apply the new proposed method to more complex atmospheric or land surface models requires further verification.

Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN

“…Additional approaches to address potential spurious correlations between variables include adaptive localization [ 72 ] and the use of two iterative filters instead of one [ 73 ]. The adaptive localization-based filter can improve the accuracy as well as the convergence of ensemble-based methods in the context of sequential data assimilation methods [ 74 , 75 , 76 ]. The degree of skill improvement also varies significantly by region which were attributed to the number of assimilated observations [ 77 ].…”

“…Inflation methods can effectively increase state uncertainties. Along with the localization method, the inflation factors can also be an improvement configuration in the context of sequential data assimilation methods [ 74 , 75 ]. Typically, inflation functions are regarded as functions of the singular values of background or analysis perturbations.…”