This study analyzed spatiotemporal variability in domestic PM10 data from 2001 to 2019. From annual numbers of stations between 175 and 484, the point data at each station were spatially interpolated using the inverse distance weighted method. A periodic variability in daily mean data was examined through wavelet analysis, which showed that there was a clear annual pattern with the periodic change following a regular cycle. The Mann-Kendall Test for monthly and annual mean data showed a decreasing trend in about 1 µg/m3 per year. The spatial change in the grid data for annual mean data represented that it was relatively higher in the northern regions than that in the southern regions and its mean and deviation decreased significantly over time. For the entire period of observation data, it was found that annual mean and standard deviation of PM10 concentrations were relatively high in the region near the metropolitan area.

<p>Real-time monitoring and analysis of rainfall are important in reducing potential damage and losses in water-related disasters. Nowadays, IoT sensor data is being widely used in weather observation because of cost-effectiveness with high spatiotemporal resolutions. This study proposes a novel approach to estimate rainfall intensity from binarized rain streak images in surveillance cameras. Here, several background subtract algorithms are considered to extract rain streak images from raw video data recorded by surveillance cameras installed in six different points in Seoul, Korea. Various ranges of binarization threshold values are also used to calculate the number of white pixel values from rain streak images. As results, it indicates that rainfall intensity is properly estimated from binarized rain streak images with a relation equation between the number of white values and observation rainfall intensity data, which shows high dependence on the amount of illumination and recording environment characteristics (e.g. rainfall type, camera parameter, etc.).</p><p>Keywords: Rainfall Estimation, Rain Streak, CCTV, Computer Vision, Korea</p><p><strong>Acknowledgement</strong></p><p>This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-01910 and in part supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).</p>

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.

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.

<p>  This study proposes a novel approach on estimation of fine dust concentration from raw video data recorded by surveillance cameras. At first, several regions of interest are defined from specific images extracted from videos in surveillance cameras installed at Chung-Ang University. Among them, sky fields are mainly considered to figure out changes in characteristics of each color. After converting RGB images into BGR images, a number of discrete pixels with brightness intensities in a blue channel is mainly analyzed by investigating any relationships with fine dust concentration measured from automatic monitoring stations near the campus. Here, different values of thresholds from 125 to 200 are considered to find optimal conditions from changes in values of each pixel in the blue channel. This study uses the Pearson correlation coefficient to calculate the correlation between the number of pixels with values over the selected threshold and observed data for fine dust concentration. As an example on one specific date, the coefficients reflect their positive correlations with a range from 0.57 to 0.89 for each threshold. It should be noted that this study is a novel attempt to suggest a new, simple, and efficient method for estimating fine dust concentration from surveillance cameras common in many areas around the world.</p><p> </p><p><strong>Keywords:</strong> Fine Dust Concentration, BGR Image, Surveillance Camera, Threshold, Correlation Analysis</p><p> </p><p><strong>Acknowledgment</strong></p><p>  This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838) and this work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-01910 and this work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2020R1G1A1013624).</p>