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.