The CASA Nowcasting System

Abstract

Abstract Short-term prediction (nowcasting) of high-impact weather events can lead to significant improvement in warnings and advisories and is of great practical importance. Nowcasting using weather radar reflectivity data has been shown to be particularly useful and the Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network provides high-resolution (0.5-km spatial and 1-min temporal resolution) reflectivity data that are amenable to producing valuable nowcasts. This paper describes the theory and implementation of a nowcasting system operating in the CASA Distributed Collaborative Adaptive Sensing network and shows that nowcasting can be reliably performed in such a distributed environment. In this context, nowcasting is used in a traditional sense to produce predictions of radar reflectivity fields up to 10 min into the future to support emergency manager decision making, and in a novel manner to support researchers and operational forecasters where 1–5-min nowcasts are used to steer the radar nodes to better observe moving precipitation systems. The high-resolution nature of CASA data and distributed system architecture necessitate the use of a fast nowcasting algorithm. A method is described that uses linear least squares estimation implemented in the Fourier domain for motion estimation with advection performed via a kernel-based method formulated in the spatial domain. Results of a performance evaluation during the CASA 2009 Integrative Project 1 experiment are presented that show that the nowcasting system significantly outperformed persistence forecasts of radar reflectivity in terms of critical success index and mean absolute error for lead times up to 10 min. Feedback from end users regarding the use of nowcasting for adaptive scanning was also unanimously positive.