Estimation of raindrop size distribution and rain rate with infrared surveillance camera in dark conditions

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.