Evaluation of anomalous propagation echo detection in WSR-88D data: A large sample case study

Abstract

A method of detecting anomalous propagation echo in volume scan radar reflectivity data is evaluated. The method is based on a neural network approach and is suitable for operational implementation. It performs a classification of the base scan data on a pixel-by-pixel basis into two classes: Rain and no rain. The results of applying the method to a large sample of Weather Surveillance Radar-1988 Doppler (WSR-88D) level II archive data are described. The data consist of over 10 000 volume scans collected in 1994 and 1995 by the Tulsa, Oklahoma, WSR 88D. The evaluation includes analyses based on radar data only and on various comparisons of radar and rain gauge data. The rain gauge data are from the Oklahoma Mesonet, The results clearly show the effectiveness of the procedure as indicated by reduced bias in rainfall accumulation and improved behavior in other statistics. We used the same, but somewhat expanded, database as Greta and Krajewski (2000). The data were collected by the Tulsa, Oklahoma, Weather Surveillance Radar-1988 Doppler version (WSR-88D). In Oklahoma, the rainfall regime is dominated by midlatitude convective systems (Houze et al. 1990). The data cover mostly the warm season months of 1994 and 1995. Since the study of Grecu and Krajewski (2000), we filled several gaps in the radar data and, as a result, had available over 10000 volume scans (see Fig. 1 for the histogram). These radar data were converted from the Archive level II format (Klazura and lmy 1993) to the efficient format ASCII-RLE (Kruger and Krajewski 1997), allowing the rapid access required for such a large sample study. The rain gauge data we used are from the Oklahoma Mesonet (Brock et al. 1995), with some 49 rain gauges located within the Tulsa radar domain.