| description abstract | Statistical and case study?oriented comparisons of the quantitative precipitation nowcasting (QPN) schemes demonstrated during the first World Weather Research Programme (WWRP) Forecast Demonstration Project (FDP), held in Sydney, Australia, during 2000, served to confirm many of the earlier reported findings regarding QPN algorithm design and performance. With a few notable exceptions, nowcasting algorithms based upon the linear extrapolation of observed precipitation motion (Lagrangian persistence) were generally superior to more sophisticated, nonlinear nowcasting methods. Centroid trackers [Thunderstorm Identification, Tracking, Analysis and Nowcasting System (TITAN)] and pattern matching extrapolators using multiple vectors (Auto-nowcaster and Nimrod) were most reliable in convective scenarios. During widespread, stratiform rain events, the pattern matching extrapolators were superior to centroid trackers and wind advection techniques (Gandolf, Nimrod). There is some limited case study and statistical evidence from the FDP to support the use of more sophisticated, nonlinear QPN algorithms. In a companion paper in this issue, Wilson et al. demonstrate the advantages of combining linear extrapolation with algorithms designed to predict convective initiation, growth, and decay in the Auto-nowcaster. Ebert et al. show that the application of a nonlinear scheme [Spectral Prognosis (S-PROG)] designed to smooth precipitation features at a rate consistent with their observed temporal persistence tends to produce a nowcast that is superior to Lagrangian persistence in terms of rms error. However, the value of this approach in severe weather forecasting is called into question due to the rapid smoothing of high-intensity precipitation features. | |
