52 netcdf

Visualizing NetCDF data

Uncomment the following line to install leafmap if needed.

In [1]:

# !pip install leafmap

# !pip install leafmap

In [2]:

# !pip install xarray rioxarray netcdf4 localtileserver

# !pip install xarray rioxarray netcdf4 localtileserver

In [3]:

import leafmap

import leafmap

Download a sample NetCDF dataset.

In [4]:

url = 'https://github.com/giswqs/leafmap/raw/master/examples/data/wind_global.nc'
filename = 'wind_global.nc'

url = 'https://github.com/giswqs/leafmap/raw/master/examples/data/wind_global.nc' filename = 'wind_global.nc'

In [5]:

leafmap.download_file(url, output=filename)

leafmap.download_file(url, output=filename)

Downloading...
From: https://github.com/giswqs/leafmap/raw/master/examples/data/wind_global.nc
To: /home/runner/work/leafmap/leafmap/docs/notebooks/wind_global.nc
100%|██████████| 1.05M/1.05M [00:00<00:00, 7.89MB/s]

Out[5]:

'/home/runner/work/leafmap/leafmap/docs/notebooks/wind_global.nc'

Read the NetCDF dataset.

In [6]:

data = leafmap.read_netcdf(filename)
data

data = leafmap.read_netcdf(filename) data

Out[6]:

<xarray.Dataset>
Dimensions:  (lat: 181, lon: 360)
Coordinates:
  * lat      (lat) float64 90.0 89.0 88.0 87.0 86.0 ... -87.0 -88.0 -89.0 -90.0
  * lon      (lon) float64 0.0 1.0 2.0 3.0 4.0 ... 355.0 356.0 357.0 358.0 359.0
Data variables:
    u_wind   (lat, lon) float64 ...
    v_wind   (lat, lon) float64 ...
Attributes:
    centerName:      US National Weather Service - NCEP(WMC)
    disciplineName:  Meteorological products
    refTime:         2016-04-30T06:00:00.000Z

xarray.Dataset

• Dimensions:
  • lat: 181
  • lon: 360
• Coordinates: (2)
  • lat
    (lat)
    float64
    90.0 89.0 88.0 ... -89.0 -90.0

    array([ 90.,  89.,  88.,  87.,  86.,  85.,  84.,  83.,  82.,  81.,  80.,  79.,
            78.,  77.,  76.,  75.,  74.,  73.,  72.,  71.,  70.,  69.,  68.,  67.,
            66.,  65.,  64.,  63.,  62.,  61.,  60.,  59.,  58.,  57.,  56.,  55.,
            54.,  53.,  52.,  51.,  50.,  49.,  48.,  47.,  46.,  45.,  44.,  43.,
            42.,  41.,  40.,  39.,  38.,  37.,  36.,  35.,  34.,  33.,  32.,  31.,
            30.,  29.,  28.,  27.,  26.,  25.,  24.,  23.,  22.,  21.,  20.,  19.,
            18.,  17.,  16.,  15.,  14.,  13.,  12.,  11.,  10.,   9.,   8.,   7.,
             6.,   5.,   4.,   3.,   2.,   1.,   0.,  -1.,  -2.,  -3.,  -4.,  -5.,
            -6.,  -7.,  -8.,  -9., -10., -11., -12., -13., -14., -15., -16., -17.,
           -18., -19., -20., -21., -22., -23., -24., -25., -26., -27., -28., -29.,
           -30., -31., -32., -33., -34., -35., -36., -37., -38., -39., -40., -41.,
           -42., -43., -44., -45., -46., -47., -48., -49., -50., -51., -52., -53.,
           -54., -55., -56., -57., -58., -59., -60., -61., -62., -63., -64., -65.,
           -66., -67., -68., -69., -70., -71., -72., -73., -74., -75., -76., -77.,
           -78., -79., -80., -81., -82., -83., -84., -85., -86., -87., -88., -89.,
           -90.])

  • lon
    (lon)
    float64
    0.0 1.0 2.0 ... 357.0 358.0 359.0

    array([  0.,   1.,   2., ..., 357., 358., 359.])

• Data variables: (2)
  • u_wind
    (lat, lon)
    float64
    ...

    [65160 values with dtype=float64]

  • v_wind
    (lat, lon)
    float64
    ...

    [65160 values with dtype=float64]

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Float64Index([ 90.0,  89.0,  88.0,  87.0,  86.0,  85.0,  84.0,  83.0,  82.0,
                   81.0,
                  ...
                  -81.0, -82.0, -83.0, -84.0, -85.0, -86.0, -87.0, -88.0, -89.0,
                  -90.0],
                 dtype='float64', name='lat', length=181))

  • lon
    PandasIndex

    PandasIndex(Float64Index([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,
                    9.0,
                  ...
                  350.0, 351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0,
                  359.0],
                 dtype='float64', name='lon', length=360))

• Attributes: (3)

  centerName :

  US National Weather Service - NCEP(WMC)

  disciplineName :

  Meteorological products

  refTime :

  2016-04-30T06:00:00.000Z

Convert the NetCDF dataset to GeoTIFF. Note that the longitude range of the NetCDF dataset is [0, 360]. We need to convert it to [-180, 180] by setting shift_lon=True so that it can be displayed on the map.

In [7]:

tif = 'wind_global.tif'
leafmap.netcdf_to_tif(filename, tif, variables=['u_wind', 'v_wind'], shift_lon=True)

tif = 'wind_global.tif' leafmap.netcdf_to_tif(filename, tif, variables=['u_wind', 'v_wind'], shift_lon=True)

Out[7]:

'/home/runner/work/leafmap/leafmap/docs/notebooks/wind_global.tif'

Add the GeoTIFF to the map. We can also overlay the country boundary on the map.

In [8]:

geojson = 'https://github.com/giswqs/leafmap/raw/master/examples/data/countries.geojson'

geojson = 'https://github.com/giswqs/leafmap/raw/master/examples/data/countries.geojson'

In [9]:

m = leafmap.Map(layers_control=True)
m.add_raster(tif, band=[1], palette='coolwarm', layer_name='u_wind')
m.add_geojson(geojson, layer_name='Countries')
m

m = leafmap.Map(layers_control=True) m.add_raster(tif, band=[1], palette='coolwarm', layer_name='u_wind') m.add_geojson(geojson, layer_name='Countries') m

Out[9]: