The Nowcasting of Precipitation during Sydney 2000: An Appraisal of the QPF Algorithms

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.

A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

“…In this study, a rainfall region is defined as those clustered pixels on the reflectivity images where cell values are larger than 10 dB Z. Although it is ideal to create a model to track movements for individual storms, as previously mentioned, TCs are mainly composed of stratiform clouds, and individual storm tracking techniques underperform in this scenario because cloud boundaries of individual storms are difficult to distinguish [36]. For example, if we define storm boundaries using a certain reflectivity threshold, two large regions of that reflectivity value may be connected by a single pixel to produce a single larger region, whereas the desired outcome would be to split the regions at the location of the single pixel.…”

Urine separation as part of a real-time control strategy

“…For the sake of simplicity, uncertainties associated with rain forecasting (see e.g. Pierce et al 2004) are neglected.…”

Increase of River Base Flow by Hydropower Gate Operation for Mitigation of CSO Impacts – Potential and Limitations

“…Further the location, the topography and the size of the catchment have a strong influence, as well as the time horizon required for the forecast. Uncertainties associated with a forecast horizon of T + 30 min can be found in (Pierce et al, 2004), where various systems/methods were tested at the Forecast Demonstration Project (FDP), held in Sydney, Australia, during 2000. Uncertainties quoted for the rain volume are in the range of 5%-10% (mean square error -MSE) where for rain intensities 45%-75% (MSE) are noted.…”