The effect of initial vortex asymmetric structure on tropical cyclone intensity change in response to an imposed environmental vertical wind shear

Previous studies have investigated how the environmental vertical wind shear (VWS) may trigger the asymmetric structure in an initially axisymmetric tropical cyclone (TC) vortex and how TC intensity changes in response. In this study, the possible effect of the initial vortex asymmetric structure on the TC intensity change in response to an imposed environmental VWS is investigated based on idealized full-physics model simulations. Results show that the effect of the asymmetric structure in the initial TC vortex can either enhance or suppress the initial weakening of the TC in response to the imposed environmental VWS. When the initial asymmetric structure is in phase of the VWS-induced asymmetric structure, the TC weakening will be enhanced and vice versa. Our finding calls for realistic representation of initial TC asymmetric structure in numerical weather prediction models and observations to better resolve the asymmetric structure in TCs.

Methods
The original data generated by our idealized experiments using the WRF model is very large, so we used Fortran (you can also use Python, MATLAB and other tools) to preprocess the data and obtain the main variables needed for our research analysis. The preprocessed data is in binary format.

The WRF model is a numerical weather prediction and atmospheric research model developed by organizations including the National Center for Atmospheric Research (NCAR) and the National Centers for Environmental Prediction (NCEP) in the USA. WRF is open-source software and can be downloaded from https://github.com/wrf-model/WRF/releases.

The specific parameters and settings used to configure the WRF model runs are described in detail in the paper. Interested researchers can follow the settings in the paper to regenerate the original raw data. However, the raw data files are very large (tens of GB per file), making direct analysis difficult. Therefore, we used tools like Fortran to preprocess the raw data into smaller binary files containing the key variables needed for analysis, such as radar reflectivity, winds, etc. The binary files are around a few hundred MB in size. We strongly recommend that subsequent researchers directly use these preprocessed binary data files, which will greatly simplify the data processing workflow.