Lagrangian Tropical Cyclone Precipitation Estimates and Moisture Sources (LagTCPMoS) Dataset.

Area of Science:

• Atmospheric Science
• Meteorology
• Climate Science

Background:

• Tropical cyclones (TCs) are significant weather phenomena impacting coastal regions.
• Accurate estimation of TC precipitation and understanding moisture sources are crucial for forecasting and impact assessment.
• Existing datasets may lack the detailed Lagrangian perspective on moisture tracking.

Purpose of the Study:

• To introduce a novel dataset, LagTCPMoS, for Lagrangian tropical cyclone precipitation estimates and moisture sources.
• To provide a comprehensive dataset covering the North Atlantic basin from 1980 to 2023.
• To validate the dataset's accuracy using a case study.

Main Methods:

• Utilized TC track data from the U.S. National Hurricane Center (HURDAT2).
• Employed a Lagrangian moisture tracking approach with the FLEXible PARTicle (FLEXPART v10.4) dispersion model.
• Generated precipitation estimates within a 500 km radius and mapped moisture origins at 0.5° × 0.5° resolution.

Main Results:

• The LagTCPMoS dataset provides 6-hourly precipitation estimates and moisture source distributions for North Atlantic TCs.
• Validation using Hurricane Harvey (2017) demonstrated accurate capture of Lagrangian precipitation and moisture uptake.
• The dataset offers high spatial resolution for moisture origin analysis.

Conclusions:

• The LagTCPMoS dataset is a valuable resource for studying TC precipitation and moisture dynamics.
• The Lagrangian approach effectively captures key aspects of TC moisture transport and precipitation.
• This dataset can enhance research on TC characteristics and their impacts.