Development of a Hybrid Ensemble–Variational Data Assimilation System over the Western Maritime Continent

Abstract

Abstract A hybrid three-dimensional ensemble–variational (En3D-Var) data assimilation system has been developed to explore incorporating information from an 11-member regional ensemble prediction system, which is dynamically downscaled from a global ensemble system, into a 3-hourly cycling convective-scale data assimilation system over the western Maritime Continent. From the ensemble, there exists small-scale ensemble perturbation structures associated with positional differences of tropical convection, but these structures are well represented only after the downscaled ensemble forecast has evolved for at least 6 h due to spinup. There was also a robust moderate negative correlation between total specific humidity and potential temperature background errors, presumably because of incorrect vertical motion in the presence of clouds. Time shifting of the ensemble perturbations, by using those available from adjacent cycles, helped to ameliorate the sampling error prevalent in their raw autocovariances. Monthlong hybrid En3D-Var trials were conducted using different weights assigned to the ensemble-derived and climatological background error covariances. The forecast fits to radiosonde relative humidity and wind observations were generally improved with hybrid En3D-Var, but in all experiments, the fits to surface observations were degraded compared to the baseline 3D-Var configuration. Over the Singapore radar domain, there was a general improvement in the precipitation forecasts, especially when the weighting toward the climatological background error covariance was larger, and with the additional application of time-shifted ensemble perturbations. Future work involves consolidating the ensemble prediction and deterministic system, by centering the ensemble prediction system on the hybrid analysis, to better represent the analysis and forecast uncertainties.