Nonmeteorological Echoes Identification Method Based on Bayesian Classifier and Echo Physical Characteristics Using C-Band Radar and Its Performance

Nonmeteorological factors usually interfere with weather radars during observation, resulting in nonmeteorological echoes that seriously affect the accuracy of the radar’s quantitative precipitation estimation and the performance of short-term precipitation forecasts. This study uses the scanning observations of C-band Doppler weather radars in Shaanxi Province (Xi’an, Yan’an, etc.) to construct a quality control method based on the Bayesian classifier and physical characteristics of the echo. First, the reflectivity factors of each radar’s precipitation echoes, ground clutter, and clear-air echoes are manually extracted. The reflectivity factor and its horizontal texture, the gradient of the reflectivity factor along the radial direction, the height of five dBZ, and the vertical gradient of the reflectivity of the different types of radar echoes from seven radars in the Shaanxi Province were analyzed based on the different types of radar echoes extracted. Additionally, we analyzed the probability density distribution functions of the corresponding characteristics of different types of radar echoes. Next, a Naïve Bayes classifier is established based on the statistical probability density distribution function to identify the radar echo. Then, combined with the physical characteristics of the echo, the sun spike filter, speckle filter, and hole filling are designed to further identify the echo. Finally, nonmeteorological echoes are removed to obtain precipitation echoes after quality control. The performance of the radar quality control method was systematically analyzed using the scanning observation data of seven radars in Shaanxi Province from July to September 2019. The radar data quality control method results for provincial business operations were compared and analyzed. The accuracy of the quality control results was evaluated using the Heidke skill score (HSS). Results show that the developed radar quality control method based on Bayesian classifiers and echo physical characteristics can better identify precipitation and nonprecipitation echoes, the recognition effect is better than the business results, and the HSS scores of data quality control results for seven radars are all above 0.75.