Calibrating Radar Data in an Orographic Setting: A Case Study for the Typhoon Nakri in the Hallasan Mountain, Korea

Abstract

Abstract:The Typhoon Nakri passed the Jeju Island in Korea (1-3 August 2014) and recorded one-day rainfall of 1182 mm at Witse-Oreum (a place name where a small volcanic cone is) of the Hallasan Mountain, Korea. This one-day rainfall amount was the highest rainfall that was ever recorded in Korea. As the altitude of Witse-Oreum is 1673 m, it was believed that the orographic effect enhanced the rainfall depth significantly. It was also argued that the maximum rainfall could be recorded in some other locations in the Hallasan Mountain. In this study, the rainfall event due to the Typhoon Nakri in the Jeju Island was analyzed using the radar and rain gauge data that were collected during this rainfall event. Fortunately, two radars are available on the eastern and western side of the Jeju Island. The entire Hallasan Mountain is covered by these two radars. A total of 23 rain gauges are also available in the Jeju Island. As a first step, independent ground-level Z-R relations were derived for every 250 m interval from the sea-level. Each Z-R relation was then applied to the corresponding-altitude radar reflectivity data to generate the rain rate field over the Jeju Island. Finally, the generated radar rain rate data were examined fully to evaluate the orographic effect in the Hallasan Mountain, also to locate the point where the maximum rain rate was recorded. This result shows that the maximum one-day rainfall amount could be up to 1500 mm, which was about 30% higher than the rain gauge measurement.

Estimation of Hourly Rainfall during Typhoons Using Radar Mosaic-Based Convolutional Neural Networks

“…Researchers have studied the QPE of tropical cyclones. For example, Ku and Yoo [26] analyzed the rainfall engendered by typhoon Nakri by using radar and rain gauge data. Libertino et al [27] developed a quasi-real-time procedure to optimize the radar estimation of rainfall rates.…”

Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles

“…Because of radars' higher resolution and real-time data acquisition, many radar rainfall prediction systems employ the relationship between radar reflectivity and rainfall intensity, expressed as the relationship between radar reflectivity (Z) and rainfall rate (R); for instance, the Marshall-Palmer formula [9] (Z = 200 R 1.6 where Z is in mm 6 /m 3 and R is in mm/h) converts radar reflectivity into rainfall rate. Numerous studies have analyzed and explored radar reflectivity-based rainfall estimations [10][11][12][13][14][15][16][17]. For example, Borga et al [18] used high-resolution radar rainfall fields and space-time distributed hydrological models to evaluate the rainfall runoff during storm floods.…”