21. TC-RMW Cross-section Plot

21.1. Description

Generate cross-section plots for TC-RMW data based on height levels using output from the METcalcpy vertical interpolation module, vertical_interpolation.py:

https://metcalcpy.readthedocs.io/en/develop/Users_Guide/vertical_interpolation.html

NOTE:Data must have the following required fields: temperature, relative humidity, and surface pressure. These are required by the METcalcpy vertical_interpolation module to compute pressure indices.

21.2. Example

21.2.1. Sample Data

Create the sample data by performing the conversion from pressure levels to height levels by following the instructions:

https://metcalcpy.readthedocs.io/en/develop/Users_Guide/vertical_interpolation.html

The output directory you specified in the height_from_pressure_tcrmw.sh shell script (in the METcalcpy source code) is where your tc_rmw_example_vertical_interp.nc file will be located. The tc_rmw_example_vertical_interp.nc is the input file to the cross section plot, plot_cross_section.py.

21.2.2. Configuration Files

An example configuration file (YAML, with .yaml extension) is available as a starting point for customizing the cross-plot of the height level data:

$METPLOTPY_BASE/metplotpy/contributed/tc_rmw/plot_cross_section.yaml where $METPLOTPY_BASE is the location where you saved the METplotpy source code.

# Sample YAML configuration file for generating cross-section plots
# based on height (from vertical interpolation via METcalcpy module
# vertical_interpolation).


vertical_coord_name:
    'lev'

field:
    'TMP'

contour_level_start: 0
contour_level_end: 500
contour_level_stepsize: 5

# time slice of interest
index_time_slice: 0

#
# contour plot appearance customizations
#
line_width: 2
contour_line_colors: 'darkblue'
contour_label_color: 'orange'
contour_label_fmt: '%1.0f'

# axis labels
# x-axis is in units of RMW
x_label: 'units of RMW'
y_label: 'Height (meters)'

# x-ticks
x_tick_start: 1
x_tick_end: 20

# y-ticks
y_tick_start: 0
y_tick_end: 3000
y_tick_stepsize: 100

# y-axis limits
y_lim_start: 0
y_lim_end: 6000

# y_scale
#y_scale: 'symlog'
y_scale: 'linear'

# plot title
plot_title: 'Temperature Cross-section'

# plot name (only provide the filename without the extension,
# png and pdf files will be generated)
plot_filename: 'example'

# plot resolution in dpi (dot per square inch)
plot_res: 300

# plot size in inches width, height
plot_size_width: 8
plot_size_height: 4.5

In this example, the plot_cross_section.yaml configuration file is set up to generate the cross-section plot for the temperature field:

field:

‘TMP’

You can modify the plot_filename to give your output plot a different name. Two different file types are generated, a .png file and a .pdf file. In the plot_cross_section.yaml configuration file, the plot that will be created will be named example.png and example.pdf.

There are configuration settings to set the labels to the x-axis and y-axis, the plot size, plot resolution, contour colors, etc.

To generate a cross-section plot for a different field, replace the ‘TMP’ with the any other available field name from the input file (tc_rmw_example_vertical_interp.nc).

21.3. Run from the Command Line

You can generate a cross-section plot using the test_plot_cross_section.sh Bourne shell script:

# ============================*
# ** Copyright UCAR (c) 2020
# ** University Corporation for Atmospheric Research (UCAR)
# ** National Center for Atmospheric Research (NCAR)
# ** Research Applications Lab (RAL)
# ** P.O.Box 3000, Boulder, Colorado, 80307-3000, USA
# ============================*

export datadir=/Users/minnawin/AF_STIG/feature_57_METplotpy_logging/METplotpy/metplotpy/contributed/tc_rmw/Data
export plotdir=/Users/minnawin/AF_STIG/feature_57_METplotpy_logging/METplotpy/metplotpy/contributed/tc_rmw/plots
export filename=vertically_interpolated.nc
export configfile=/Users/minnawin/AF_STIG/feature_57_METplotpy_logging/METplotpy/metplotpy/contributed/tc_rmw/plot_cross_section.yaml
# Default is set to INFO in code, set to any other value here and add to the arguments
# in the call to the plot_cross_section.py below
export loglevel="ERROR"


python plot_cross_section.py \
    --datadir=$datadir \
    --plotdir=$plotdir \
    --filename=$filename \
    --config=$configfile \
#    --loglevel=$loglevel

Open the test_plot_cross_section.sh script in an editor of your choice.

Modify the datadir, plotdir, filename, and configfile values in the test_plot_cross_section.sh script to point to the appropriate locations:

  • datadir is the location of the input netCDF file (the directory where you saved the output from the METcalcpy vertical interpolation)

  • plotdir is the location of the output plot

  • filename is the name of the input netCDF file (tc_rmw_example_vertical_interp.nc that was created from running the METcalcpy vertical interpolation)

  • configfile is the YAML config file that is used to set plot customizations (line colors, labels, etc.)

Save and close the test_plot_cross_section.sh script.

To generate the plot, perform the following:

  • Make sure you have the following Python packages installed:

    • Python 3.7 or above

    • METcalcpy (use the same version number as this METplotpy)

    • matplotlib 3.4.3

    • metpy 1.1.0

    • netcdf4 1.5.7 or above

    • numpy

    • pandas

    • pint 0.17

    • xarray

    • yaml

Specific version numbers are specified when necessary. If versions are not specified, use a compatible version number for your operating system and existing packages.

  • If you are running in a conda environment, verify that you are running the conda environment that has the above Python packages installed.

  • cd to the $METPLOTPY_BASE/metplotpy/contributed/tc_rmw directory

cd $METPLOTPY_BASE/metplotpy/contributed/tc_rmw

replacing the $METPLOTPY_BASE with the directory where you saved the METplotpy source code.

  • run the test_plot_cross_section.sh script:

sh test_plot_cross_section.sh

Two files will be created, example.png and example.pdf (if you used the plot_filename value of ‘example’ in the plot_cross_section.yaml configuration file). They will be located in the output directory you specified in the test_plot_cross_section.sh Bourne shell script. The plot will look like the following:

../_images/example.png