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The Development and Evaluation of a Tropical Cyclone Probabilistic Landfall Forecast Product
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2023
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Source: Weather and Forecasting (2023)
[PDF-2.30 MB]
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Journal Title:Weather and Forecasting
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Description:Improving estimates of tropical cyclone forecast uncertainty remains an important goal of the Hurricane Forecast Improvement Project (HFIP). Intensity forecast uncertainty near landfall is especially complicated because intensity forecasts depend on track forecasts. Ensembles can be difficult to interpret near land due to differences in both spatial and temporal resolution and differences in landfall timing (if at all) and location. The Monte Carlo Wind Speed Probability (WSP) model is a statistical ensemble based on the error characteristics of forecasts by the National Hurricane Center (NHC) and the spread of several track forecast models. The landfall distribution product (LDP) introduced in this paper was developed to use the statistical ensemble of forecasts from the WSP model to estimate both the track and intensity forecast uncertainty associated with potential landfalls. The LDP includes probabilistic intensity estimates as well as estimates of the most likely and reasonable strongest intensity at landfall. These products could communicate concise intensity uncertainty information to users at risk for tropical cyclone impacts. Demonstration on a retrospective dataset from 2010-2018 and evaluation of the LDP on the 2020-2021 Atlantic hurricane seasons shows that the probability of landfall and the landfall intensity probabilities generated by the WSP model are reliable and potentially useful for preparedness decision making. A case study of Hurricane Ida (2021) highlights how the LDP can be implemented to communicate landfall uncertainty to a broad range of users.
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Source:Weather and Forecasting (2023)
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ISSN:0882-8156;1520-0434;
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