Objective identification method for tropical cyclone landfall information

Compiling long-term time-series datasets of landfalling tropical cyclones (TCs) is crucial for research on climate change of TC activities, especially under the background of global warming. In this study, using the best-track datasets, satellite data, and coastal geospatial information, three objective identification methods for TCs’ landfall information are proposed. The results show that: significant discrepancies are observed in the computational outcomes of objective identification methods for TC landfall information when compared with official landfall records during 2003-2020. Specifically, the direct method of linearly intersecting TC tracks with coastlines produces mean biases of 1.41, -1.04, 0.05, -0.03, -0.49, and -0.86 hours, respectively in landfall time of six TC intensity categories. mean biases of 0, 1.7, 1.2, 1.8, 2.0, and 6.6 m/s, respectively in landfall intensity (maximum sustained wind speed) of six TC intensity categories. 0,-2.1,-1.8,-2.4,-5.0 and -13 hpa in landfall intensity (minimum sea level pressure) estimation. Notably, applying parabolic fitting approach yields no measurable improvement over the first method. However, incorporating satellite data substantially improves the estimation accuracy for TCs reaching typhoon category or higher, achieving improvements of up to 0.45 hours in landfall time, 4.4 m/s and 8 hPa in landfall intensity. The results will support ongoing research into the climate activity of landfalling TCs.