Determination of Rain-/Wind-Dominant Type for Typhoons Approaching South Korea Based on Satellite-Estimated Rainfall and Best-Track Data

Kim, H.-J. and Moon, I.-J., 2019. Determination of rain-/wind-dominant type for typhoons approaching South Korea based on satellite-estimated rainfall and best-track data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue, No. 90, pp. 340-345. Coconut Creek (Florida), ISSN 0749-0208Typhoons are one of the most influential natural hazards in South Korea. When a typhoon approaches the Korean Peninsula (KP), detailed disaster plans are needed to prevent typhoon-induced damage. In general, the official forecast in South Korea provides basic information on the track and the intensity of the typhoon, but the forecast does not provide detailed information on the impact of the typhoon on the KP based on the type of damage (i.e., rain-, wind-, or rain-wind-dominant). Impact-based information is crucial to prevent disasters, because preparations for an approaching typhoon should be planned differently depending on the type. Based on satellite-estimated rainfall and best-track wind data, this study developed an algorithm for estimating the rainfall and maximum wind speed (MWS) percentiles induced by a typhoon, compared to those induced by previous tropical cyclones (TCs) that have moved through the same area, at intervals of 1° latitude. This information is used to determine the dominant types of typhoons along their tracks. From the estimated rainfall and MWS percentiles, all TCs that approached the KP during 2001–2016 were classified as wind-, rain-, and wind-rain-dominant types. As a result, Maemi in 2003 was the most wind-dominant type, while Nabi in 2005 was the most rain-dominant type. This result has important implications for providing a guidance tool based on impact-based TC prediction for decision makers preparing disaster prevention plans based on real-time satellite-estimated rainfall data.