Estimation of raindrop size distribution and rain rate with infrared surveillance camera in dark conditions
Jinwook Lee, Jongyun Byun, Jongjin Baik, Changhyun Jun, Hyeon-Joon Kim
Abstract
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 a physical optics analysis. The estimated DSD was verified using a disdrometer for the two rainfall events. 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 had values between 100 and 1000 mm−1 m−3, and the root mean square error (RMSE) for the number concentration by CCTV and PARticle SIze and VELocity (PARSIVEL) was 72.3 and 131.6 mm−1 m−3 in the 0.5 to 1.5 mm section. Third, the 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 49 % and 23 %, respectively. In addition, the differences according to rain rate are that the MAPE was 36 % at a rain rate of less than 2 mm h−1 and 80 % at a rate above 2 mm h−1. Also, when the rain rate was greater than 5 mm h−1, MAPE was 33 %. We confirmed the possibility of estimating an image-based DSD and rain rate obtained based on low-cost equipment during dark conditions.