Regarding 250 Vorticity Plot (Time 1) in MPAS Tutorial 240-km Case

[Snow_Rain_Cloud]

Hi there,

I am currently working through the tutorial case and analyzing the outputs. I’m pleased to report that all my tests have run smoothly without any error messages.

Many thanks to the NCAR MPAS and MPAS-JEDI team for their hard work in developing such excellent tutorial materials — they have been incredibly helpful!

However, I’ve encountered an issue with the Time 1 plot for 250 Vorticity values. The field exhibits some unphysical, wavy patterns around the 2 contour line, as well as near larger values. For example, I noticed these patterns near 158°E, between 50°N - 70°N.

Could any experts provide insights into what might be causing these wavy features? Could it be due to interpolation artifacts or something else?

I’ve attached the plot from the first time step, as well as a GIF of the entire 5-day simulation for further reference.

Thank you in advance for your time and assistance!

Regards,
Minghua

 

[Ming Chen]

Is this from global MPAS run with mesh resolution of 240km? What is the UTC time of your 'Time 1' ?

The plot is from data on native MPAS mesh or from latlon data created by convert_mpas?

Did you use NCL or python to produce the plots? Please upload a single time data and your script for me to take a look.

Thanks.

 

[Snow_Rain_Cloud]

Is this from global MPAS run with mesh resolution of 240km? What is the UTC time f your 'Time 1' ?

The plot is from data on native MPAS mesh or from latlon data created by convert_mpas?

Did you use NCL or python to produce the plots? Please upload a single time data and your script for me to take a look.

Thanks.

Yes, this is from global MPAS with uniform 240 km run. The UTC time for Time 1 is 2014-09-10_00.00.00.
The plot is from latlon data created by convert_mpas.
I developed a python code to produce the plots.

I could not get the data for now, but below is my script:

import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset
file_path = "latlon.nc" # Replace with the actual path to your NetCDF file
nc_data = Dataset(file_path, 'r')
vorticity_250hPa = nc_data.variables['vorticity_250hPa'][:]
latitude = nc_data.variables['latitude'][:]
longitude = nc_data.variables['longitude'][:]
coef=100000
contour_levels = np.linspace(-12, 12, 25)
for t in range(len(vorticity_250hPa)): # Loop through all time steps
# Create a new figure for each time step
fig, ax = plt.subplots(figsize=(8, 6)) # Adjust the size if needed
vorticity_at_time = coef*vorticity_250hPa[t, :, :] # Extract vorticity for the current time step
im = ax.contourf(longitude, latitude, vorticity_at_time, levels=contour_levels, cmap='jet')
ax.contour(longitude, latitude, vorticity_at_time, levels=[2], colors='black', linewidths=2)
ax.set_title(title, fontsize=10)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
cbar=fig.colorbar(im, ax=ax, orientation='horizontal', fraction=0.04, pad=0.1)
cbar.set_label(r'250-hPa vorticity ($10^{-5}$ s$^{-1}$)', fontsize=10)
# Save the individual plot
output_file = f'vorticity_250hPa_time_{t+1}.png' # Save as a PNG file for each time step
plt.savefig(output_file, dpi=300) # Save the figure with 300 DPI resolution
plt.close(fig) # Close the current figure to avoid memory issues
# Close the NetCDF file
nc_data.close()