Plotting MAIA netCDF-4 PM Level 2 Data Variables

Notebook Author / Affiliation

David F. Moroni (david.f.moroni@jpl.nasa.gov) / Jet Propulsion Laboratory, Californa Institute of Technology

Acknowledgements

The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).

© 2021 California Institute of Technology. Government sponsorship acknowledged.

Getting Started

In this notebook, we will access a local file of simulated MAIA Level 2 PM data. These simulated data are provided courtesy of the MAIA Project in netCDF-4 format.

We will access the data file from local storage, load data variables into memory, and plot the data on a 2-D regional map over the Primary Target Area (PTA) of Los Angeles (USA).

MAIA Orbital Targetting Simulation

MAIA Sequential Orbit Time

Requirements

Python 3

Most of these imports are from the Python standard library. However, you will need to install these packages into your Python 3 environment if you have not already done so:

  • netCDF4
  • numpy
  • matplotlib
  • mpl_toolkits
  • cartopy
  • optparse
  • IPython
import netCDF4
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.axes
import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeat
from optparse import OptionParser
from netCDF4 import Dataset
from IPython.display import HTML

Read the netCDF file into memory

pm_file = "MAIA_L2_PM_PM-10-total_20190102T000000Z_XX_XXX_RXX_USA-LosAngeles_FXX_VXXpXXpXXpXX.nc"
print ( pm_file )

# -----------------------------
# Open netCDF4 file for reading
# -----------------------------
try:
    nc_file = Dataset( pm_file, 'r' )
except IOError:
    print ('not a valid netCDF file')
MAIA_L2_PM_PM-10-total_20190102T000000Z_XX_XXX_RXX_USA-LosAngeles_FXX_VXXpXXpXXpXX.nc

Open with the netCDF file, starting with reading in the Global Attributes.

def readGlobalAttrs( nc_file ):

  attr_list = []
  global_attr = []
  for attr_name in nc_file.ncattrs():
# print ( attr_name, '=', getattr(nc_file, attr_name) )
    attr_list.append( attr_name )
    atts = getattr( nc_file, attr_name )
    global_attr.append( atts )
  return attr_list, global_attr

Read the netCDF Variables and Attribute Metadata

var_list = []
var_attr_list = []
var_data_list = []
def readVars ( nc_file ):
      vars =  nc_file.variables.keys()
      for var_name in vars:
          attr = nc_file.variables[var_name]
          vardata = nc_file.variables[var_name][:]
          var_attr_list.append( attr )
          var_data_list.append( vardata)
      return vars, var_attr_list, var_data_list

Read and Describe the netCDF Un-Grouped Variable Attributes

print ('Un-Grouped netCDF Variables:')
[vars, var_attr_list, var_data_list] = readVars( nc_file )
nvars = len(vars)
print ('Number of Un-Grouped variables = ', nvars)
for i in range(0, nvars):
    vardata = var_data_list[i]
    print ('----------')
    print (var_attr_list[i])
    print (list(vars)[i], '[0:10] =\n', list(vardata)[0:10])
Un-Grouped netCDF Variables:
Number of Un-Grouped variables =  3
----------
<class 'netCDF4._netCDF4.Variable'>
float32 X_Dim(X_Dim)
    _FillValue: 9.96921e+36
    unit: meter
    standard_name: projection_x_coordinate
    long_name: Projection X Coordinate
    _CoordinateAxisType: GeoX
unlimited dimensions: X_Dim
current shape = (352,)
filling on
X_Dim [0:10] =
 [-175625.0, -174625.0, -173625.0, -172625.0, -171625.0, -170625.0, -169625.0, -168625.0, -167625.0, -166625.0]
----------
<class 'netCDF4._netCDF4.Variable'>
float32 Y_Dim(Y_Dim)
    _FillValue: 9.96921e+36
    unit: meter
    standard_name: projection_y_coordinate
    long_name: Projection Y Coordinate
    _CoordinateAxisType: GeoY
unlimited dimensions: Y_Dim
current shape = (420,)
filling on
Y_Dim [0:10] =
 [209625.0, 208625.0, 207625.0, 206625.0, 205625.0, 204625.0, 203625.0, 202625.0, 201625.0, 200625.0]
----------
<class 'netCDF4._netCDF4.Variable'>
int32 Albers_Equal_Area()
    semi_major_axis: 6378137.0
    inverse_flattening: 298.257223563
    min_x: -500000.0
    min_y: -500000.0
    max_x: 500000.0
    max_y: 500000.0
    long_name: Albers Equal Area Conic Projection
    grid_mapping_name: albers_conical_equal_area
    standard_parallel: [33.3 37.3]
    longitude_of_central_meridian: -118.6
    latitude_of_projection_origin: 35.3
    false_easting: 0.0
    false_northing: 0.0
    origin_code: 0
    pix_reg_code: 0
    crs_wkt: PROJCS["unnamed",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433],AUTHORITY["EPSG","4326"]],PROJECTION["Albers_Conic_Equal_Area"],PARAMETER["standard_parallel_1",33.3],PARAMETER["standard_parallel_2",37.3],PARAMETER["latitude_of_center",35.3],PARAMETER["longitude_of_center",-118.6],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]]]
    dimension_order: 1
unlimited dimensions: 
current shape = ()
filling on, default _FillValue of -2147483647 used
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-0d601eea16a1> in <module>
      7     print ('----------')
      8     print (var_attr_list[i])
----> 9     print (list(vars)[i], '[0:10] =\n', list(vardata)[0:10])

TypeError: iteration over a 0-d array

List the Un-grouped Variables

list(vars)
['X_Dim', 'Y_Dim', 'Albers_Equal_Area']

Read the netCDF Group Variables

nc_file.groups
{'Geometric_Parameters': <class 'netCDF4._netCDF4.Group'>
 group /Geometric_Parameters:
     dimensions(sizes): 
     variables(dimensions): float32 Longitude(X_Dim, Y_Dim), float32 Latitude(X_Dim, Y_Dim)
     groups: ,
 'PM_10': <class 'netCDF4._netCDF4.Group'>
 group /PM_10:
     dimensions(sizes): 
     variables(dimensions): float32 PM_10_Total_Mean(X_Dim, Y_Dim), float32 PM_10_Total_Uncertainty(X_Dim, Y_Dim)
     groups: ,
 'PM_2.5': <class 'netCDF4._netCDF4.Group'>
 group /PM_2.5:
     dimensions(sizes): 
     variables(dimensions): float32 PM_2.5_Dust_Mean(X_Dim, Y_Dim), float32 PM_2.5_Dust_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_EC_Mean(X_Dim, Y_Dim), float32 PM_2.5_EC_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_Nitrate_Mean(X_Dim, Y_Dim), float32 PM_2.5_Nitrate_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_OC_Mean(X_Dim, Y_Dim), float32 PM_2.5_OC_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_Sulfate_Mean(X_Dim, Y_Dim), float32 PM_2.5_Sulfate_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_Total_Mean(X_Dim, Y_Dim), float32 PM_2.5_Total_Uncertainty(X_Dim, Y_Dim)
     groups: }

Read the Geometric Parameters Group Variables

grp_geo = nc_file.groups['Geometric_Parameters']

Describe the Geometric Group Variables

grp_geo
<class 'netCDF4._netCDF4.Group'>
group /Geometric_Parameters:
    dimensions(sizes): 
    variables(dimensions): float32 Longitude(X_Dim, Y_Dim), float32 Latitude(X_Dim, Y_Dim)
    groups:

List the Geometric Group Variables

list(grp_geo.variables)
['Longitude', 'Latitude']

Read the PM 10 Group Variables

grp_pm10 = nc_file.groups['PM_10']

Describe the PM 10 Variables

grp_pm10
<class 'netCDF4._netCDF4.Group'>
group /PM_10:
    dimensions(sizes): 
    variables(dimensions): float32 PM_10_Total_Mean(X_Dim, Y_Dim), float32 PM_10_Total_Uncertainty(X_Dim, Y_Dim)
    groups:

List the PM 10 Variables

list(grp_pm10.variables)
['PM_10_Total_Mean', 'PM_10_Total_Uncertainty']

Read the PM 2.5 Group Variables

grp_pm2d5 = nc_file.groups['PM_2.5']

Describe the PM 2.5 Variables

grp_pm2d5
<class 'netCDF4._netCDF4.Group'>
group /PM_2.5:
    dimensions(sizes): 
    variables(dimensions): float32 PM_2.5_Dust_Mean(X_Dim, Y_Dim), float32 PM_2.5_Dust_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_EC_Mean(X_Dim, Y_Dim), float32 PM_2.5_EC_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_Nitrate_Mean(X_Dim, Y_Dim), float32 PM_2.5_Nitrate_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_OC_Mean(X_Dim, Y_Dim), float32 PM_2.5_OC_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_Sulfate_Mean(X_Dim, Y_Dim), float32 PM_2.5_Sulfate_Uncertainty(X_Dim, Y_Dim), float32 PM_2.5_Total_Mean(X_Dim, Y_Dim), float32 PM_2.5_Total_Uncertainty(X_Dim, Y_Dim)
    groups:

List the PM 2.5 Variables

list(grp_pm2d5.variables)
['PM_2.5_Dust_Mean',
 'PM_2.5_Dust_Uncertainty',
 'PM_2.5_EC_Mean',
 'PM_2.5_EC_Uncertainty',
 'PM_2.5_Nitrate_Mean',
 'PM_2.5_Nitrate_Uncertainty',
 'PM_2.5_OC_Mean',
 'PM_2.5_OC_Uncertainty',
 'PM_2.5_Sulfate_Mean',
 'PM_2.5_Sulfate_Uncertainty',
 'PM_2.5_Total_Mean',
 'PM_2.5_Total_Uncertainty']

Reading the Discrete Variables within the Variable Groups

lon = grp_geo.variables['Longitude'][:]
lat = grp_geo.variables['Latitude'][:]
pm_10_total = grp_pm10.variables['PM_10_Total_Mean'][:]
pm_10_uncert = grp_pm10.variables['PM_10_Total_Uncertainty'][:]
print, lon
(<function print>,
 masked_array(
   data=[[-120.57638 , -120.57616 , -120.575935, ..., -120.487076,
          -120.48687 , -120.486664],
         [-120.565125, -120.5649  , -120.564674, ..., -120.47632 ,
          -120.47612 , -120.475914],
         [-120.55386 , -120.55365 , -120.55342 , ..., -120.465576,
          -120.46537 , -120.465164],
         ...,
         [-116.64613 , -116.646355, -116.64658 , ..., -116.73443 ,
          -116.734634, -116.73483 ],
         [-116.63488 , -116.6351  , -116.63533 , ..., -116.72368 ,
          -116.723885, -116.72409 ],
         [-116.62363 , -116.62384 , -116.62407 , ..., -116.71293 ,
          -116.713135, -116.71334 ]],
   mask=False,
   fill_value=1e+20,
   dtype=float32))
print, lat
(<function print>,
 masked_array(
   data=[[37.171444, 37.162437, 37.153427, ..., 33.415184, 33.40617 ,
          33.397156],
         [37.171623, 37.162617, 37.153606, ..., 33.415356, 33.40634 ,
          33.397327],
         [37.171803, 37.162792, 37.153786, ..., 33.415524, 33.40651 ,
          33.397495],
         ...,
         [37.171803, 37.162792, 37.153786, ..., 33.415524, 33.40651 ,
          33.397495],
         [37.171623, 37.162617, 37.153606, ..., 33.415356, 33.40634 ,
          33.397327],
         [37.171444, 37.162437, 37.153427, ..., 33.415184, 33.40617 ,
          33.397156]],
   mask=False,
   fill_value=1e+20,
   dtype=float32))

Generate a “Psuedo” AlbersEqualArea 2-D Lat/Lon Map of PM 10 Total Mean

plt.figure(figsize=(10, 10))

ax1 = plt.axes(projection=ccrs.AlbersEqualArea())
ax1.gridlines(crs=ccrs.AlbersEqualArea(central_longitude=-118.6, central_latitude=35.3, false_easting=0.0, false_northing=0.0, standard_parallels=(33.3, 37.3), globe=None), alpha=0.3)
ax1.coastlines()

plt.figure(figsize=(10, 10))

ax2 = plt.axes(projection=ccrs.PlateCarree())
ax2.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, alpha=0.3)
ax2.coastlines()

x = ax2.scatter(lon[:], lat[:], c=pm_10_total[:], lw=2, vmin=35., vmax=40.)

plt.colorbar(x, orientation='horizontal', pad=0.05)
plt.show()
print('Units = micrograms m^-3')

Units = micrograms m^-3

Generate PlateCarree 2-D Lat/Lon Map of PM 10 Total Mean

plt.figure(figsize=(10, 10))

ax1 = plt.axes(projection=ccrs.PlateCarree())
ax1.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, alpha=0.3)
ax1.coastlines()

plt.figure(figsize=(10, 10))

ax2 = plt.axes(projection=ccrs.PlateCarree())
ax2.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, alpha=0.3)
ax2.coastlines()

x = ax2.scatter(lon[:], lat[:], c=pm_10_total[:], lw=2, vmin=35., vmax=40.)

plt.colorbar(x, orientation='horizontal', pad=0.05)
plt.show()
print('Units = micrograms m^-3')

Units = micrograms m^-3

Generate a 2-D Lat/Lon Map of PM 10 Total Uncertainty

plt.figure(figsize=(10, 10))

ax1 = plt.axes(projection=ccrs.PlateCarree())
ax1.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, alpha=0.3)
ax1.coastlines()

plt.figure(figsize=(10, 10))

ax2 = plt.axes(projection=ccrs.PlateCarree())
ax2.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, alpha=0.3)
ax2.coastlines()

x = ax2.scatter(lon[:], lat[:], c=pm_10_uncert[:], lw=2, vmin=7.5, vmax=9.)

plt.colorbar(x, orientation='horizontal', pad=0.05)
plt.show()
print('Units = micrograms m^-3')

Units = micrograms m^-3