Table of Contents

  • 1  Plot of Surface Pressure on MPAS grid

    • 1.1  Get Data

    • 1.2  Read Data from MPAS Grid

    • 1.3  Convert to degrees from radians

    • 1.4  Global tripcolor plot

Plot of Surface Pressure on MPAS grid

[1]:

%matplotlib inline
import xarray as xr
import numpy as np
import cartopy.crs as ccrs
import matplotlib.tri as tri
import matplotlib.pyplot as plt
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER

Get Data

[2]:

from ngallery_utils import DATASETS
DATASETS.registry_files

[2]:

['thetao_Omon_historical_GISS-E2-1-G_r1i1p1f1_gn_185001-185512.nc',
 'woa2013v2-O2-thermocline-ann.nc',
 'NOAA_NCDC_ERSST_v3b_SST.nc',
 'sst_indices.csv',
 'air_temperature.nc',
 'rasm.nc',
 'co2.nc',
 'moc.nc',
 'aviso_madt_2015.tar.gz',
 'NARR_19930313_0000.nc',
 'MPAS.nc',
 'Oklahoma.static.nc',
 'uas.rcp85.CanESM2.CRCM5-UQAM.day.NAM-44i.raw.Colorado.nc',
 'uas.hist.CanESM2.CRCM5-UQAM.day.NAM-44i.raw.Colorado.nc',
 'uas.gridMET.NAM-44i.Colorado.nc',
 'T2_RR_F_2014_08.nc',
 'wrfinput_d02']

[3]:

path = DATASETS.fetch("MPAS.nc")

Downloading file 'MPAS.nc' from 'ftp://ftp.cgd.ucar.edu/archive/aletheia-data/tutorial-data/MPAS.nc' to '/home/jovyan/aletheia-data/tutorial-data'.

Read Data from MPAS Grid

[4]:

ds = xr.open_dataset(path)
ds = ds.squeeze()
ds

[4]:

<xarray.Dataset>
Dimensions:           (nCells: 163842)
Dimensions without coordinates: nCells
Data variables:
    lonCell           (nCells) float64 ...
    latCell           (nCells) float64 ...
    surface_pressure  (nCells) float64 ...
[5]:

# Not sure what the pressure units are, there's not much metadata info on this file
ds["surface_pressure"] = ds["surface_pressure"] / 1000.

Convert to degrees from radians

[6]:

ds["lonCell"] = np.degrees(ds.lonCell)
ds["latCell"] = np.degrees(ds.latCell)

Global tripcolor plot

[7]:

# Add cyclic point before computing the triangulation
ds["lonCell"] = ((ds["lonCell"] + 180) % 360) - 180
triang = tri.Triangulation(ds.lonCell, ds.latCell)

[8]:

import cmaps as cm  # for NCL colormaps

[9]:

# Don't use white by cutting out the first section of the colormap
from matplotlib.colors import LinearSegmentedColormap
interval = np.hstack([np.linspace(0.15, 1)])
colors = cm.WhiteBlueGreenYellowRed(interval)
c = LinearSegmentedColormap.from_list('name', colors)

[10]:

%%time

fig = plt.figure(figsize=(13, 13))
ax = plt.subplot(111, projection=ccrs.PlateCarree())
mm = ax.tripcolor(triang,
                  ds.surface_pressure,
                  edgecolors='k',
                  lw=0.01,
                  alpha=1,
                  transform=ccrs.PlateCarree(),
                  cmap=c,
                  vmin=55.0,
                  vmax=100.)
ax.set_global()
ax.coastlines(lw=1.5, resolution='110m')
gl = ax.gridlines(draw_labels=True)
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER
gl.xlabels_top = gl.ylabels_right = False
gl.xlines = gl.ylines = False
plt.colorbar(mm, orientation='horizontal', pad=0.03)
plt.title(f"Surface pressure on MPAS grid ({len(ds.lonCell)} cells)",
          fontweight="bold",
          fontsize=14)

plt.show()

/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/gridliner.py:307: UserWarning: The .xlabels_top attribute is deprecated. Please use .top_labels to toggle visibility instead.
  warnings.warn('The .xlabels_top attribute is deprecated. Please '
/srv/conda/envs/notebook/lib/python3.8/site-packages/cartopy/mpl/gridliner.py:343: UserWarning: The .ylabels_right attribute is deprecated. Please use .right_labels to toggle visibility instead.
  warnings.warn('The .ylabels_right attribute is deprecated. Please '
../../../../../../_images/repos_NCAR_notebook-gallery_notebooks_Run-Anywhere_mpas_plot_of_surface_pressure_on_mpas_grid_15_1.png 
CPU times: user 38.6 s, sys: 769 ms, total: 39.4 s
Wall time: 38.4 s

NCL Script: https://www.ncl.ucar.edu/Applications/Scripts/mpas_1.ncl

NCL Plot:

image0

[11]:

%load_ext watermark
%watermark -ud -iv -m -g -h

Last updated: 2021-02-12

Compiler    : GCC 9.3.0
OS          : Linux
Release     : 4.19.112+
Machine     : x86_64
Processor   : x86_64
CPU cores   : 6
Architecture: 64bit

Hostname: jupyter-ncar-2dnotebook-2dgallery-2djua1e9u8

Git hash: f054bc8c5f99da733516cd9abadb1544163c9b67

matplotlib: 3.3.4
numpy     : 1.20.1
cartopy   : 0.18.0
xarray    : 0.16.2