Acknowledgement
본 연구는 국립기상과학원 용역사업(KMA2018-00622)의 지원으로 수행되었습니다.
References
- Campbell Scientific., 2018: LWS: Dielectric leaf wetness sensor instruction manual. Revision 11/18. Available at http://s.campbellsci.com/documents/us/manuals/lws.pdf [accessed 23 June 2022].
- Inouye, D. W., 2000: The ecological and evolutionary significance of frost in the context of climate change. Ecology Letters 3, 457-463. https://doi.org/10.1046/j.1461-0248.2000.00165.x
- Kim, Y. S., K. M. Shim, M. P. Jung, and I. T. Choi, 2017: Study on the estimation of frost occurrence classification using machine learning methods. Korean Journal of Agricultural and Forest Meteorology 19(3), 86-92. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2017.19.3.86
- Ko, B. S., 2019: Development of frost occurrence prediction model in Chungbuk region using multinomial logistic regression analysis. Korea Meteorological Society poster, 500-500.
- Kwon, Y. A., 2006: The spatial distribution and recent trend of frost occurrence days in South Korea. Journal of the Korean Geographical Society 41(3), 361-372. (in Korean with English abstract)
- Kwon, Y. A., H. S. Lee, W. T. Kwon, and K. O. Boo, 2008: The weather characteristics of frost occurrence days for protecting crops against frost damage. Journal of the Korean Geographical Society 43(6), 824-842. (in Korean with English abstract)
- Lee, Y. B., and S. T. Ro, 2002: Frost formation on a vertical plate in simultaneously developing flow. Experimental Thermal and Fluid Science 26(8), 939-945. https://doi.org/10.1016/S0894-1777(02)00216-9
- MAFRA, 2014: Support for disaster recovery costs for farms affected by abnormally low temperatures, Ministry of Agriculture, Food and Rural Affairs, Sejong, Korea.
- Ministry of Agriculture, Food and Rural Affairs, 2014: Support for disaster recovery coasts for farms affected by abnormally low temperatures and frost.
- Noh, I. S., H. W. Doh, S. O. Kim, S. H. Kim, S. E. Shin, and S.-J. Lee, 2021: Machine learning-based hourly frost-prediction system optimized for orchards using automatic weather station and digital camera image data. Atmosphere 12(7), 846. https://doi.org/10.3390/atmos12070846
- Sallis, P., M. Jarur, and M. Trujillo, 2009: Frost prediction characteristics and classification using computational neural networks. In Australian Journal of Intelligent Information Processing Systems 10(1), 50-58.
- Savage, M. J., 2012: Estimation of frost occurrence and duration of frost for a short-grass surface. South African Journal of Plant and Soil 29, 173-187. https://doi.org/10.1080/02571862.2012.748938
- Song, M. J., and C. Dang, 2018: Review on the measurement and calculation of frost characteristics. International Journal of Head and Mass Transfer 124, 586-614. https://doi.org/10.1016/j.ijheatmasstransfer.2018.03.094
- Warmund, M. R., P. Guinan, and G. Fernandez, 2008: Temperatures and cold damage to small fruit crops across the Eastern United States associated with the april 2007 freeze. HortScience 43(6), 1643-1647. https://doi.org/10.21273/hortsci.43.6.1643