DOI: 10.5061/dryad.xgxd254nq
title: Dependence of tropical cyclone weakening rate in response to an imposed moderate environmental vertical wind shear on the warm-core strength and height of the initial vortex

description: This study investigated the dependence of the early tropical cyclone (TC)
 weakening rate in response to an imposed moderate environmental vertical
 wind shear (VWS) on the warm-core strength and height of the TC vortex
 using idealized numerical simulations. Results show that the weakening of
 the warm core by upper-level ventilation is the primary factor leading to
 the early TC weakening in response to an imposed environmental VWS. The
 upper-level ventilation is dominated by eddy radial advection of the
 warm-core air. The TC weakening rate is roughly proportional to the
 warm-core strength and height of the initial TC vortex. The boundary-layer
 ventilation shows no relationship with the early weakening rate of the TC
 in response to an imposed moderate VWS. The findings suggest that some
 previous diverse results regarding the TC weakening in environmental VWS
 could be partly due to the different warm-core strengths and heights of
 the initial TC vortex.
description: 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 potential temperature, 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. 
description: # Title of Dataset This is the dataset that reproduces the Figures in four
 vertical wind shear (VWS) experiments in the manuscript "Dependence
 of tropical cyclone weakening rate in response to an imposed moderate
 environmental vertical wind shear on the warm-core strength and height of
 the initial vortex" # Description of the data and file structure
 `theta_azimuth_ano_d01.d`,`theta_azimuth_ano_d02.d`,
 `theta_azimuth_ano_d03.d`, `theta_azimuth_ano_d04.d` The data are the
 azimuthal mean of the potential temperature for each experiment,
 respectively. These variables are three-dimensional arrays. var = (t,k,r),
 t = 961, k = 59, r = 141 `eth_azimuth_output_d01.d`,
 `eth_azimuth_output_d02.d`, `eth_azimuth_output_d03.d`,
 `eth_azimuth_output_d04.d` The data are the azimuthal mean of the total
 advection of the potential temperature, boundary layer turbulent mixing
 from the boundary layer parameterization scheme, the diabatic heating and
 the the actual change of the potential temperature for each experiment
 directly obtained from the model output at 6 min intervals during the
 model simulation, respectively. These variables are three-dimensional
 arrays. var = (t,k,r), t = 961, k = 59, r = 141 `eth_azimuth_budget_d03.d`
 The data are the azimuthal mean of each term in the potential temperature
 diagnostic formula in SHE72_AXI. These variables are three-dimensional
 arrays. var = (t,k,r), t = 961, k = 59, r = 141 trajectory/ The data in
 this package are the results of the forward trajectory tracking.
 x0,y0,z,thetae in the suffix of each file name represent the
 three-dimensional coordinate position of the tracking particle and the
 corresponding equivalent potential temperature, respectively, and the
 numbers in the file name represent the different tracking particles in
 SHE72_AXI. `lower_venti_d01.d`, `lower_venti_d02.d`, `lower_venti_d03.d`,
 `lower_venti_d04.d` The data are the the lower-level ventilation averaged
 below z=1.5 km in the 24-h period after the VWS is imposed in each
 experiment, respectively. These variables are two-dimensional arrays. var
 = (x,y), x = 300, y = 300 # Sharing/Access information NCL can be obtained
 by
 [https://www.ncl.ucar.edu/Download/](https://www.ncl.ucar.edu/Download/) #
 Code/Software The data can be read using Python, NCL, Grads, Fortran,
 Matlab, and other tools. Here we take NCL and Python as examples: To read
 the variable in the data theta_azimuth_ano_d01.d as an example:  **NCL:**
 do t = 0,960 do k=0,58 var(t,k,:) = fbindirread(“theta_azimuth_ano_d01.d”,
 t*59+k, (/141/),“float”) end do end do **Python:** import numpy as np
 file_path = 'theta_azimuth_ano_d01.d' data_type = np.float32
 shape = (961, 59, 141) var = np.fromfile(file_path, dtype=data_type)