Comment on amt-2022-196

Abstract

This study estimated raindrop size distribution (DSD) and rainfall intensity with an infrared surveillance camera in dark conditions. Accordingly, rain streaks were extracted using a k-nearest neighbor (KNN)-based algorithm. The rainfall intensity was estimated using DSD based on physical optics analysis. The estimated DSD was verified using a disdrometer. Furthermore, a tipping-bucket rain gauge was used for comparison. The results are summarized as follows. First, a KNN-based algorithm can accurately recognize rain streaks from complex backgrounds captured by the camera. Second, the number concentration of raindrops obtained through closed-circuit television (CCTV) images was similar to the actual PArticle SIze and VELocity (PARSIVEL)-observed number concentration in the 0.5 to 1.5 mm section. Third, maximum raindrop diameter and the number concentration of 1 mm or less produced similar results during the period with a high ratio of diameters of 3 mm or less. Finally, after comparing with the 15-min cumulative PARSIVEL rain rate, the mean absolute percent error (MAPE) was 44 %. The differences according to rain rate can be determined. The MAPE was 32 % at a rain rate of less than 2 mm h-1 and 73 % at a rate above 2 mm h-1. We confirmed the possibility of estimating an image-based DSD and rain rate obtained based on low-cost equipment during dark conditions.