Estimation of raindrop size distribution and rain rate with infrared 1 surveillance camera in dark conditions

. This study estimated raindrop size distribution (DSD) and rainfall intensity with an infrared surveillance camera in 7 dark conditions. Accordingly, rain streaks were extracted using a k -nearest neighbor (KNN)-based algorithm. The rainfall 8 intensity was estimated using DSD based on physical optics analysis. The estimated DSD was verified using a disdrometer. 9 Furthermore, a tipping-bucket rain gauge was used for comparison. The results are summarized as follows. First, a KNN- 10 based algorithm can accurately recognize rain streaks from complex backgrounds captured by the camera. Second, the 11 number concentration of raindrops obtained through closed-circuit television (CCTV) images was similar to the actual 12 PArticle SIze and VELocity (PARSIVEL)-observed number concentration in the 0.5 to 1.5 mm section. Third, maximum 13 raindrop diameter and the number concentration of 1 mm or less produced similar results during the period with a high ratio 14 of diameters of 3 mm or less. Finally, after comparing with the 15-min cumulative PARSIVEL rain rate, the mean absolute 15 percent error (MAPE) was 44%. The differences according to rain rate can be determined. The MAPE was 32% at a rain rate 16 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 17 rain rate obtained based on low-cost equipment during dark conditions. This task is not easy to solve or requires excessive computation time. Therefore, existing studies present techniques suitable 90 for post-analysis rather than application in real-time. The use of complex algorithms can increase versatility and accuracy, but there is a trade-off that reduces computational speed. The time required for such computing is a critical disadvantage in 92 practical applications for estimating rainfall intensity. In this study, a KNN-based segmentation algorithm (Zivkovic and Heijden, 2006), a popular non-parametrical method 94 for background subtraction, was considered for segmenting the rain streaks (foreground) and background layers. KNN is 95 used in classification and regression problems (Bouwmans et al., 2010). The concept of KNN is that similar things are close—the KNN-based segmentation algorithm finds the closest k samples (neighbors) to the unknown sample using Euclidean distance to determine the class (i.e., foreground or background). Thus, the KNN-based segmentation method to detect foreground changes in the video was used to identify rain streaks by recording infrared videos under conditions with 99 little background influence. In the algorithm, The KNN subtractor works by updating the parameters of a Gaussian mixture model for more accurate kernel density estimation (Trnovszký et al., 2017). KNN is more efficient for local density estimation (Qasim et al., 2021); therefore, the algorithm is highly efficient if the number of foreground pixels is low. than 3 mm were not 324 observed in CCTV. However, the number concentration of small diameters of 0.5 mm or less had similar values between 325 1,000 and 5,000 mm -1 m 3 . Despite the difference in the maximum size of the drops, there was no predominant difference in 326 the Dm because the number concentration of raindrops smaller than 1 mm had similar values. 327 From 0300 LST to 0530 LST, number concentrations higher than 5,000 mm -1 m -3 in the raindrops smaller than 1 mm 328 were observed using PARSIVEL. However, CCTV data revealed that number concentrations less than 5,000 mm -1 m -3 were 329 consistently observed. At 1714 LST, raindrops of up to 3.2 mm were observed through PARSIVEL, but the maximum 330 diameter was overestimated to be greater than 3.5 mm based on CCTV. In CCTV images, the D m was close to 3 mm because 331 of the overestimation of diameter and underestimation of number concentration for raindrops less than 1 mm. There was a 332 difference with the D m value obtained through PARSIVEL observation.