Articles | Volume 16, issue 3
https://doi.org/10.5194/amt-16-707-2023
https://doi.org/10.5194/amt-16-707-2023
Research article
 | 
08 Feb 2023
Research article |  | 08 Feb 2023

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

Jinwook Lee, Jongyun Byun, Jongjin Baik, Changhyun Jun, and Hyeon-Joon Kim

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Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
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Revised manuscript accepted for AMT
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Cited articles

Allamano, P., Croci, A., and Laio, F.: Toward the camera rain gauge, Water Resour. Res., 51, 1744–1757, https://doi.org/10.1002/2014wr016298, 2015. 
Atlas, D., Srivastava, R. C., and Sekhon, R. S.: Doppler radar characteristics of precipitation at vertical incidence, Rev. Geophys., 11, 1–35, https://doi.org/10.1029/rg011i001p00001, 1973. 
Avanzato, R. and Beritelli, F.: A cnn-based differential image processing approach for rainfall classification, Adv. Sci. Technol. Eng. Syst. J., 5, 438–444, https://doi.org/10.25046/aj050452, 2020. 
Bouwmans, T., El Baf, F., and Vachon, B.: Statistical background modeling for foreground detection: A survey, in: Handbook of pattern recognition and computer vision, edited by: Chen, C. H., 4th edn., World Scientific, Singapore, 181–199, https://doi.org/10.1142/9789814273398_0008, 2010. 
Cai, F., Lu, W., Shi, W., and He, S.: A mobile device-based imaging spectrometer for environmental monitoring by attaching a lightweight small module to a commercial digital camera, Sci. Rep., 7, 1–9, https://doi.org/10.1038/s41598-017-15848-x, 2017. 
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Our study addresses raindrop size distribution and rain rate by extracting rain streaks using a k-nearest-neighbor-based algorithm, estimating rainfall intensity using raindrop size distribution based on physical optics analysis, and verifying the estimated raindrop size distribution using a disdrometer. Experimentation demonstrated the possibility of estimating an image-based raindrop size distribution and rain rate obtained based on such low-cost equipment in dark conditions.