A Dynamic and Spatial Scaling Approach to Advection Forecasting

Abstract

Quantitative nowcasts of rainfall are frequently based on the advection of rain fields observed by weather radar. Spectral Prognosis (S-PROG) is an advection-based nowcasting system that uses the observations that rain fields commonly exhibit both spatial and dynamic scaling properties, that is, the lifetime of a feature in the field is dependent on the scale of the feature (large features evolve more slowly than small features), and that features at all scales between the outer and inner observed scales are present in the field. The logarithm of the radar reflectivity field is disaggregated into a set or cascade of fields, in which each field in the set (or level in the cascade) represents the features of the original field over a limited range of scales. The Lagrangian temporal evolution of each level in the cascade is modeled using a simple autoregressive (lag 2) model, which automatically causes the forecast field to become smooth as the structures at the various scales evolve through their life cycles, or can be used to generate conditional simulations if the noise term is included. This paper describes the model and presents preliminary results.