Two Extensions of Data Assimilation by Field Alignment

Abstract

Abstract. Classical formulations of data-assimilation perform poorly when forecast locations of weather systems are displaced from their observations. They compensate position errors by adjusting amplitudes, which can produce unacceptably "distorted" states. Motivated by cyclones, in earlier work we show a new method for handling position and amplitude errors using a single variational objective. The solution could be used with either ensemble or deterministic methods. In this paper, extension of this work in two directions is reported. First, the methodology is extended to multivariate fields commonly used in models, thus making this method readily applicable. Second, an application of this methodology to rainfall modeling is presented.

Application of Ensemble Algorithm Based on the Feature-Oriented Mean in Tropical Cyclone-Related Precipitation Forecasting

“…between any ensemble member j and every other ensemble member. The field adjustment algorithm, initially proposed, developed, and applied by [19] for fluid dynamics research, was enhanced by incorporating a spatial-scale restriction module. This module allows structural position adjustments at a specific scale and smooths the remaining scales, greatly improving the computational efficiency and applicability to variable atmospheric fields [18].…”