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- Title
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based On Ensemble Weather Forecast Data.
- Authors
Oettli, P.; Kotsuki, S.
- Abstract
In ensemble weather forecast, tropical cyclone (TC) tracks sometimes group together into trajectories parting away from each other. The goal of this study is to propose an objective method, based on a robust clustering approach, to detect such separation scenarios in the Japan Meteorological Agency Meso‐scale Ensemble Prediction System (MEPS) for three TCs: "Dolphin" (2020), "Nepartak" (2021), and "Meari" (2022). Taking advantage of the independence of the density‐based spatial clustering of applications with noise algorithm to the prior choice of the number of clusters, we first describe an objective way to calculate the aggregation distance, by searching the most frequent Euclidean distance between all the tracks. The clustering is then applied to the forecasted tracks, for each initialization time of MEPS (every 6 hr). Separation scenarios exist when the number of clusters is greater than one. Plain Language Summary: To predict the weather, meteorologists use numerical models to simulate the future condition of the atmosphere, that is, forecast, from an initial condition. By slightly modifying this initial condition, an ensemble of future conditions is available, which provides the likelihood of an event occurring. In the case of a tropical cyclone (TC), it is particularly important to estimate its future path and strength, to prepare for disasters. Sometimes, the forecasted paths visually separate into distinct directions. We propose a straightforward way to numerically detect this separation in the forecasts. The method is illustrated by the three cases studies: TCs "Dolphin" (2020), "Nepartak" (2021), and "Meari" (2022). Key Points: Detecting separation scenarios in forecasted tropical cyclone (TC) trajectories to help preparedness and mitigation measuresA clustering analysis is used as a simple but effective tool to detect separations in tracks, by grouping similar trajectoriesA separation scenario exists when the number of clusters for a given initialization time is greater than 1
- Subjects
WEATHER forecasting; EUCLIDEAN distance; CLUSTER analysis (Statistics); METEOROLOGISTS; DOLPHINS; TROPICAL cyclones
- Publication
Journal of Geophysical Research. Atmospheres, 2024, Vol 129, Issue 14, p1
- ISSN
2169-897X
- Publication type
Article
- DOI
10.1029/2024JD040830