Wind distribution in the eye of tropical cyclone revealed by a novel atmospheric motion vector derivation

Taiga Tsukada, Takeshi Horinouchi, and Satoki Tsujino

Published: 2024
DOI

Abstract

Observations of wind distribution in the eye of tropical cyclones (TCs) are still limited. In this study, a method to derive atmospheric motion vectors (AMVs) for TCs is developed, where selection from multiple local rotation speeds is made by considering continuity among neighboring grid points. The method is applied to 2.5‐min interval image sequences of three TCs, Lan (2017), Haishen (2020), and Nanmadol (2022), observed by the Himawari‐8 satellite. The results are compared with AMVs derived from research‐based 30‐s Himawari‐8 special observations conducted for Haishen and Nanmadol, as well as with in‐situ dropsonde observations conducted for Lan and Nanmadol. In these storms, the AMVs obtained from the 2.5‐min interval images in the eye are found to be in good agreement with the dropsonde observations. Examinations of AMVs in the eye reveal transient azimuthal wavenumber‐1 features in all three TCs. These features are consistent with algebraically growing wavenumber‐1 disturbances, which transport angular momentum inward and accelerate the eye rotation. In the case of Lan, the angular velocity in the eye increased by approximately 1.5 times within 1 hr. This short‐term increase is further examined. Visualization of low‐level vorticity in the eye and angular momentum budget analysis suggest that angular momentum transport associated with mesovortices played an important role in the increase of tangential wind and the homogenization of angular velocity in the eye of Lan.

Citation

Tsukada, T., T. Horinouchi, and S. Tsujino, 2024: Wind distribution in the eye of tropical cyclone revealed by a novel atmospheric motion vector derivation. Journal of Geophysical Research: Atmospheres, 129, e2023JD040585. https://doi.org/10.1029/2023JD040585.

Public Data

Zenodo

The manually tracked storm positions, the derived atmospheric motion vectors, and the dropsonde sounding data used for comparison are available at:
https://doi.org/10.5281/zenodo.10798896

LICENSE

The data complies with Creative Commons Attribution 4.0 International

Public Software

openTCAMV pyVTTrac VTTrac.jl

The source code of openTCAMV developed in this study is publicly available at:
https://github.com/tsukada-cs/openTCAMV

In openTCAMV, the pyVTTrac library is used for the cloud tracking. pyVTTrac is a Python wrapper for VTTrac.jl. The source codes for pyVTTrac and VTTrac.jl are publicly available in the following repository:

LICENSE

These softwares comply with BSD 2-Clause License