Interactive plotting with HoloViews

Authors & Contributors

Authors

• Pier Lorenzo Marasco, Ispra (Italy), @pl-marasco

• Anne Fouilloux, Simula Research Laboratory (Norway), @annefou

Contributors

• Alejandro Coca-Castro, The Alan Turing Institute (United Kingdom), @acocac

Overview

Questions

• How to clip Xarray DataSet with a shapefile to plot an area of interest?
• How to generate interactive maps with Holoviews?
• How to browse multiple times with Holoviews interactive maps?
• How to generate an interactive 1D plot with Holoviews?

Objectives

• Learn about Holoviews and Xarray for creating interactive plots
• Learn to create interactive maps with Holoviews
• Learn to create multiple interactive plots with holoviews
• Learn to create a 1D line plot with holoviews

Context

We will be using HoloViews, a tool part of the HoloViz ecosystem, with Xarray to visualize the near-surface temperature from CMIP6 CESM2 (monthly dataset).

Data

We will visualize the same data that have been used in the previous episode.

If the dataset is not present in the same folder as this Jupyter notebook, it will be downloaded from zenodo using pooch, a very handy python-based library to download and cache your data files locally (see further info here).

import pooch

tas_file = pooch.retrieve(
    url="https://zenodo.org/record/7181714/files/CMIP6_NCAR_CESM2_historical_amon_gn.nc",
    known_hash="md5:5f86251e5bc5ef9b86a3a86cd06a536b",
    path=f".",
)

Setup

This episode uses the following main Python packages:

• xarray [HH17] with netCDF4 and h5netcdf engines

• pooch [USR+20]

• rioxarray [SBR+22]

• matplotlib [Hun07]

• cartopy [MetOffice15]

• hvplot [RSB+20]

• geopandas [JdBF+20]

Please install these packages if not already available in your Python environment.

Packages

In this episode, Python packages are imported when we start to use them. However, for best software practices, we recommend you to install and import all the necessary libraries at the top of your Jupyter notebook.

import xarray as xr

Open local dataset

tas_ds = xr.open_dataset(tas_file)

Tip

If you get an error with the previous command, check the previous episode where the input file some_hash-CMIP6_NCAR_CESM2_historical_amon_gn.nc is downloaded locally and it is in the same directory as your Jupyter Notebook.

tas_ds

<xarray.Dataset>
Dimensions:    (lat: 192, nbnd: 2, lon: 288, member_id: 1, time: 1980)
Coordinates:
  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
    lat_bnds   (lat, nbnd) float32 -90.0 -89.53 -89.53 ... 89.53 89.53 90.0
  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
    lon_bnds   (lon, nbnd) float32 -0.625 0.625 0.625 ... 358.1 358.1 359.4
  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    time_bnds  (time, nbnd) object 1850-01-01 00:00:00 ... 2015-01-01 00:00:00
  * member_id  (member_id) object 'r1i1p1f1'
Dimensions without coordinates: nbnd
Data variables:
    tas        (member_id, time, lat, lon) float32 ...
Attributes: (12/50)
    Conventions:             CF-1.7 CMIP-6.2
    activity_id:             CMIP
    branch_method:           standard
    branch_time_in_child:    674885.0
    branch_time_in_parent:   219000.0
    case_id:                 15
    ...                      ...
    variant_label:           r1i1p1f1
    status:                  2019-10-25;created;by nhn2@columbia.edu
    netcdf_tracking_ids:     hdl:21.14100/d9a7225a-49c3-4470-b7ab-a8180926f839
    version_id:              v20190308
    intake_esm_varname:      tas
    intake_esm_dataset_key:  CMIP.NCAR.CESM2.historical.Amon.gn

xarray.Dataset

• Dimensions:
  • lat: 192
  • nbnd: 2
  • lon: 288
  • member_id: 1
  • time: 1980
• Coordinates: (7)
  • lat
    (lat)
    float64
    -90.0 -89.06 -88.12 ... 89.06 90.0

    axis :

    Y

    bounds :

    lat_bnds

    standard_name :

    latitude

    title :

    Latitude

    type :

    double

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
           -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
           -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
           -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
           -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,
           -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,
           -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,
           -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,
           -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,
           -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,
           -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,
           -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,
           -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,
           -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,
           -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
            -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
             0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
             6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
            11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,
            17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,
            23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,
            28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,
            34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,
            40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,
            45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,
            51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,
            57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,
            62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,
            68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
            73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
            79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
            85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])

  • lat_bnds
    (lat, nbnd)
    float32
    ...

    array([[-90.     , -89.52879],
           [-89.52879, -88.58639],
           [-88.58639, -87.64398],
           ...,
           [ 87.64398,  88.58639],
           [ 88.58639,  89.52879],
           [ 89.52879,  90.     ]], dtype=float32)

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    axis :

    X

    bounds :

    lon_bnds

    standard_name :

    longitude

    title :

    Longitude

    type :

    double

    units :

    degrees_east

    valid_max :

    360.0

    valid_min :

    0.0

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

  • lon_bnds
    (lon, nbnd)
    float32
    ...

    array([[ -0.625,   0.625],
           [  0.625,   1.875],
           [  1.875,   3.125],
           ...,
           [355.625, 356.875],
           [356.875, 358.125],
           [358.125, 359.375]], dtype=float32)

  • time
    (time)
    object
    1850-01-15 12:00:00 ... 2014-12-...

    axis :

    T

    bounds :

    time_bnds

    standard_name :

    time

    title :

    time

    type :

    double

    array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, nbnd)
    object
    ...

    array([[cftime.DatetimeNoLeap(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True)],
           ...,
           [cftime.DatetimeNoLeap(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],
          dtype=object)

  • member_id
    (member_id)
    object
    'r1i1p1f1'

    array(['r1i1p1f1'], dtype=object)

• Data variables: (1)
  • tas
    (member_id, time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    near-surface (usually, 2 meter) air temperature

    description :

    near-surface (usually, 2 meter) air temperature

    frequency :

    mon

    id :

    tas

    long_name :

    Near-Surface Air Temperature

    mipTable :

    Amon

    out_name :

    tas

    prov :

    Amon ((isd.003))

    realm :

    atmos

    standard_name :

    air_temperature

    time :

    time

    time_label :

    time-mean

    time_title :

    Temporal mean

    title :

    Near-Surface Air Temperature

    type :

    real

    units :

    K

    variable_id :

    tas

    [109486080 values with dtype=float32]

• Attributes: (50)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  674885.0

  branch_time_in_parent :

  219000.0

  case_id :

  15

  cesm_casename :

  b.e21.BHIST.f09_g17.CMIP6-historical.001

  contact :

  cesm_cmip6@ucar.edu

  creation_date :

  2019-01-16T23:34:05Z

  data_specs_version :

  01.00.29

  experiment :

  all-forcing simulation of the recent past

  experiment_id :

  historical

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  mon

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r1i1p1f1

  grid :

  native 0.9x1.25 finite volume grid (192x288 latxlon)

  grid_label :

  gn

  initialization_index :

  1

  institution :

  National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA

  institution_id :

  NCAR

  license :

  CMIP6 model data produced by <The National Center for Atmospheric Research> is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

  mip_era :

  CMIP6

  model_doi_url :

  https://doi.org/10.5065/D67H1H0V

  nominal_resolution :

  100 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  CESM2

  parent_time_units :

  days since 0001-01-01 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  atmos

  source :

  CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)

  source_id :

  CESM2

  source_type :

  AOGCM BGC

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Amon

  tracking_id :

  hdl:21.14100/d9a7225a-49c3-4470-b7ab-a8180926f839

  variable_id :

  tas

  variant_info :

  CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)

  variant_label :

  r1i1p1f1

  status :

  2019-10-25;created;by nhn2@columbia.edu

  netcdf_tracking_ids :

  hdl:21.14100/d9a7225a-49c3-4470-b7ab-a8180926f839

  version_id :

  v20190308

  intake_esm_varname :

  tas

  intake_esm_dataset_key :

  CMIP.NCAR.CESM2.historical.Amon.gn

Clipping data according to a polygon

One of the basic concepts in GIS is to clip data using a vector geometry. Xarray is not directly capable of dealing with vectors but thanks to Rioxarray that can be easily achieved. Rioxarray extends Xarray with most of the features that Rasterio (GDAL) brings.

Read a shapefile with the Area Of Interest (AOI)

import geopandas as gpd

We define the area of interest using the Global Administrative Unit Layers GAUL G2015_2014 provided by FAO-UN (see Documentation). GeoPandas, a python-based library extending the capabilities of Pandas to deal with geometry and spatial operations, will help to manage geodata.

The official data distribution from FAO is through the WFS service (see below how to retrieve data):

GAUL = gpd.read_file('https://data.apps.fao.org/map/gsrv/gsrv1/gaul/wfs?'
                     'service=WFS&version=2.0.0&'
                     'Request=GetFeature&'
                     'TypeNames=gaul:g2015_2014_2&'
                     'srsName=EPSG%3A4326&'
                     'maxFeatures=2500&'
                     'outputFormat=json')

Unfortunately it seems that the service is pretty slow. As an alternative to this approach the JRC MARS unit is distributing the original dataset that was in shapefile format. To accelerate the fetch we highly recommend to follow this approach.

For the training course, we also created a tiny file containing information about Norway only.

try:
    GAUL = gpd.read_file('Norway.geojson')
except:
    GAUL = gpd.read_file('zip+https://mars.jrc.ec.europa.eu/asap/files/gaul1_asap.zip') 

Data are organized in a tabular structure. For each element an index, data (made of columns) and a geometry are defined.

Geometries are defined through shapely geometry objects with three different basic classes:

• Points and Multi-Points

• Lines and Multi-Lines

• Polygons and Multi-Polygons

GAUL.head(5)

	asap1_id	name1	name1_shr	name0	asap0_id	name0_shr	km2_tot	km2_crop	km2_range	an_crop	an_range	water_lim	geometry
0	1238	Hordaland	Hordaland	Norway	173	Norway	14021	631	81	1	0	0	MULTIPOLYGON (((5.67021 60.69386, 5.67357 60.6...
1	1446	Nord-troendelag	Nord-troendelag	Norway	173	Norway	22315	1344	9	1	0	0	MULTIPOLYGON (((11.93731 65.10981, 11.88333 65...
2	1472	Soer-Troendelag	Soer-Troendelag	Norway	173	Norway	18441	1313	4	1	0	0	MULTIPOLYGON (((9.74026 63.62652, 9.72871 63.6...
3	1426	Oestfold	Oestfold	Norway	173	Norway	4128	1030	0	1	0	0	POLYGON ((11.94171 59.69688, 11.89933 59.69182...
4	1496	Vestfold	Vestfold	Norway	173	Norway	2232	623	2	1	0	0	POLYGON ((10.39417 59.66410, 10.42124 59.62203...

In the cell below, we subset the polygon geometry in which the name0 field equals to Norway.

AOI_name = 'Norway'
AOI = GAUL[GAUL.name0 == AOI_name]
AOI_poly = AOI.geometry
AOI_poly

0     MULTIPOLYGON (((5.67021 60.69386, 5.67357 60.6...
1     MULTIPOLYGON (((11.93731 65.10981, 11.88333 65...
2     MULTIPOLYGON (((9.74026 63.62652, 9.72871 63.6...
3     POLYGON ((11.94171 59.69688, 11.89933 59.69182...
4     POLYGON ((10.39417 59.66410, 10.42124 59.62203...
5     MULTIPOLYGON (((22.69320 70.36594, 22.70137 70...
6     POLYGON ((8.13729 60.19619, 8.16250 60.19416, ...
7     MULTIPOLYGON (((13.97719 68.35144, 13.99107 68...
8     MULTIPOLYGON (((8.01763 63.32659, 7.97557 63.3...
9     POLYGON ((5.23273 62.19275, 5.25814 62.17775, ...
10    MULTIPOLYGON (((15.76350 68.74171, 15.77052 68...
11    POLYGON ((8.29320 61.08945, 8.31227 61.07882, ...
12    MULTIPOLYGON (((10.64400 59.90339, 10.63911 59...
13    POLYGON ((8.87194 62.37852, 8.89526 62.35425, ...
14    POLYGON ((11.13333 62.65833, 11.14690 62.64664...
15    POLYGON ((7.26296 59.65463, 7.33571 59.63810, ...
16    POLYGON ((7.02216 59.18047, 7.09886 59.17007, ...
17    POLYGON ((10.91103 59.92944, 10.89463 59.90425...
18    POLYGON ((6.51667 59.84167, 6.58167 59.83139, ...
Name: geometry, dtype: geometry

In a second step we set a EPSG:4326 Geodetic coordinate reference system to the polygon geometry. To achieve this we need to rely on rioxarray that extends xarray with the rasterio capabilities. The rio accessor is activated through importing rioxarray as has been done at the top.

tas_ds.rio.write_crs(4326, inplace=True)

<xarray.Dataset>
Dimensions:      (lat: 192, nbnd: 2, lon: 288, member_id: 1, time: 1980)
Coordinates:
  * lat          (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
    lat_bnds     (lat, nbnd) float32 -90.0 -89.53 -89.53 ... 89.53 89.53 90.0
  * lon          (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
    lon_bnds     (lon, nbnd) float32 -0.625 0.625 0.625 ... 358.1 358.1 359.4
  * time         (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    time_bnds    (time, nbnd) object 1850-01-01 00:00:00 ... 2015-01-01 00:00:00
  * member_id    (member_id) object 'r1i1p1f1'
    spatial_ref  int64 0
Dimensions without coordinates: nbnd
Data variables:
    tas          (member_id, time, lat, lon) float32 ...
Attributes: (12/50)
    Conventions:             CF-1.7 CMIP-6.2
    activity_id:             CMIP
    branch_method:           standard
    branch_time_in_child:    674885.0
    branch_time_in_parent:   219000.0
    case_id:                 15
    ...                      ...
    variant_label:           r1i1p1f1
    status:                  2019-10-25;created;by nhn2@columbia.edu
    netcdf_tracking_ids:     hdl:21.14100/d9a7225a-49c3-4470-b7ab-a8180926f839
    version_id:              v20190308
    intake_esm_varname:      tas
    intake_esm_dataset_key:  CMIP.NCAR.CESM2.historical.Amon.gn

xarray.Dataset

• Dimensions:
  • lat: 192
  • nbnd: 2
  • lon: 288
  • member_id: 1
  • time: 1980
• Coordinates: (8)
  • lat
    (lat)
    float64
    -90.0 -89.06 -88.12 ... 89.06 90.0

    axis :

    Y

    bounds :

    lat_bnds

    standard_name :

    latitude

    title :

    Latitude

    type :

    double

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,
           -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,
           -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,
           -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,
           -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,
           -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,
           -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,
           -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,
           -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,
           -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,
           -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,
           -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,
           -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,
           -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,
           -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,
            -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,
             0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,
             6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,
            11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,
            17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,
            23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,
            28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,
            34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,
            40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,
            45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,
            51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,
            57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,
            62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,
            68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,
            73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,
            79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,
            85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])

  • lat_bnds
    (lat, nbnd)
    float32
    -90.0 -89.53 -89.53 ... 89.53 90.0

    array([[-90.     , -89.52879],
           [-89.52879, -88.58639],
           [-88.58639, -87.64398],
           ...,
           [ 87.64398,  88.58639],
           [ 88.58639,  89.52879],
           [ 89.52879,  90.     ]], dtype=float32)

  • lon
    (lon)
    float64
    0.0 1.25 2.5 ... 356.2 357.5 358.8

    axis :

    X

    bounds :

    lon_bnds

    standard_name :

    longitude

    title :

    Longitude

    type :

    double

    units :

    degrees_east

    valid_max :

    360.0

    valid_min :

    0.0

    array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])

  • lon_bnds
    (lon, nbnd)
    float32
    -0.625 0.625 0.625 ... 358.1 359.4

    array([[ -0.625,   0.625],
           [  0.625,   1.875],
           [  1.875,   3.125],
           ...,
           [355.625, 356.875],
           [356.875, 358.125],
           [358.125, 359.375]], dtype=float32)

  • time
    (time)
    object
    1850-01-15 12:00:00 ... 2014-12-...

    axis :

    T

    bounds :

    time_bnds

    standard_name :

    time

    title :

    time

    type :

    double

    array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • time_bnds
    (time, nbnd)
    object
    1850-01-01 00:00:00 ... 2015-01-...

    array([[cftime.DatetimeNoLeap(1850, 1, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeNoLeap(1850, 2, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeNoLeap(1850, 3, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(1850, 4, 1, 0, 0, 0, 0, has_year_zero=True)],
           ...,
           [cftime.DatetimeNoLeap(2014, 10, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeNoLeap(2014, 11, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True)],
           [cftime.DatetimeNoLeap(2014, 12, 1, 0, 0, 0, 0, has_year_zero=True),
            cftime.DatetimeNoLeap(2015, 1, 1, 0, 0, 0, 0, has_year_zero=True)]],
          dtype=object)

  • member_id
    (member_id)
    object
    'r1i1p1f1'

    array(['r1i1p1f1'], dtype=object)

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    array(0)

• Data variables: (1)
  • tas
    (member_id, time, lat, lon)
    float32
    ...

    cell_measures :

    area: areacella

    cell_methods :

    area: time: mean

    comment :

    near-surface (usually, 2 meter) air temperature

    description :

    near-surface (usually, 2 meter) air temperature

    frequency :

    mon

    id :

    tas

    long_name :

    Near-Surface Air Temperature

    mipTable :

    Amon

    out_name :

    tas

    prov :

    Amon ((isd.003))

    realm :

    atmos

    standard_name :

    air_temperature

    time :

    time

    time_label :

    time-mean

    time_title :

    Temporal mean

    title :

    Near-Surface Air Temperature

    type :

    real

    units :

    K

    variable_id :

    tas

    [109486080 values with dtype=float32]

• Attributes: (50)

  Conventions :

  CF-1.7 CMIP-6.2

  activity_id :

  CMIP

  branch_method :

  standard

  branch_time_in_child :

  674885.0

  branch_time_in_parent :

  219000.0

  case_id :

  15

  cesm_casename :

  b.e21.BHIST.f09_g17.CMIP6-historical.001

  contact :

  cesm_cmip6@ucar.edu

  creation_date :

  2019-01-16T23:34:05Z

  data_specs_version :

  01.00.29

  experiment :

  all-forcing simulation of the recent past

  experiment_id :

  historical

  external_variables :

  areacella

  forcing_index :

  1

  frequency :

  mon

  further_info_url :

  https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r1i1p1f1

  grid :

  native 0.9x1.25 finite volume grid (192x288 latxlon)

  grid_label :

  gn

  initialization_index :

  1

  institution :

  National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA

  institution_id :

  NCAR

  license :

  CMIP6 model data produced by <The National Center for Atmospheric Research> is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

  mip_era :

  CMIP6

  model_doi_url :

  https://doi.org/10.5065/D67H1H0V

  nominal_resolution :

  100 km

  parent_activity_id :

  CMIP

  parent_experiment_id :

  piControl

  parent_mip_era :

  CMIP6

  parent_source_id :

  CESM2

  parent_time_units :

  days since 0001-01-01 00:00:00

  parent_variant_label :

  r1i1p1f1

  physics_index :

  1

  product :

  model-output

  realization_index :

  1

  realm :

  atmos

  source :

  CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)

  source_id :

  CESM2

  source_type :

  AOGCM BGC

  sub_experiment :

  none

  sub_experiment_id :

  none

  table_id :

  Amon

  tracking_id :

  hdl:21.14100/d9a7225a-49c3-4470-b7ab-a8180926f839

  variable_id :

  tas

  variant_info :

  CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)

  variant_label :

  r1i1p1f1

  status :

  2019-10-25;created;by nhn2@columbia.edu

  netcdf_tracking_ids :

  hdl:21.14100/d9a7225a-49c3-4470-b7ab-a8180926f839

  version_id :

  v20190308

  intake_esm_varname :

  tas

  intake_esm_dataset_key :

  CMIP.NCAR.CESM2.historical.Amon.gn

Once this has been done we can clip the data with the polygon that has been obtained through geopandas at the beginning of the notebook.

tas_AOI = tas_ds.tas.rio.clip(AOI_poly, crs=4326)

tas_AOI

<xarray.DataArray 'tas' (member_id: 1, time: 1980, lat: 13, lon: 21)>
array([[[[      nan, 268.18625, 263.21762, ...,       nan,       nan,
                nan],
         [      nan, 261.4894 , 259.58054, ...,       nan,       nan,
                nan],
         [270.17227, 260.38303, 257.93915, ...,       nan,       nan,
                nan],
         ...,
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ..., 262.4566 , 262.70773,
          262.97894]],

        [[      nan, 274.94598, 272.1838 , ...,       nan,       nan,
                nan],
         [      nan, 272.17087, 270.1366 , ...,       nan,       nan,
                nan],
         [276.09476, 271.16046, 269.08408, ...,       nan,       nan,
                nan],
...
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ..., 268.94943, 268.97186,
          269.03262]],

        [[      nan, 280.14944, 277.7591 , ...,       nan,       nan,
                nan],
         [      nan, 276.78476, 274.67453, ...,       nan,       nan,
                nan],
         [280.1642 , 275.19962, 273.14044, ...,       nan,       nan,
                nan],
         ...,
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ...,       nan,       nan,
                nan],
         [      nan,       nan,       nan, ..., 268.04092, 268.38055,
          268.77368]]]], dtype=float32)
Coordinates:
  * lat          (lat) float64 58.9 59.84 60.79 61.73 ... 68.32 69.27 70.21
  * lon          (lon) float64 5.0 6.25 7.5 8.75 10.0 ... 26.25 27.5 28.75 30.0
  * time         (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
  * member_id    (member_id) object 'r1i1p1f1'
    spatial_ref  int64 0
Attributes:
    cell_measures:  area: areacella
    cell_methods:   area: time: mean
    comment:        near-surface (usually, 2 meter) air temperature
    description:    near-surface (usually, 2 meter) air temperature
    frequency:      mon
    id:             tas
    long_name:      Near-Surface Air Temperature
    mipTable:       Amon
    out_name:       tas
    prov:           Amon ((isd.003))
    realm:          atmos
    standard_name:  air_temperature
    time:           time
    time_label:     time-mean
    time_title:     Temporal mean
    title:          Near-Surface Air Temperature
    type:           real
    units:          K
    variable_id:    tas

xarray.DataArray

'tas'

• member_id: 1
• time: 1980
• lat: 13
• lon: 21

• nan 268.2 263.2 264.3 nan nan ... 268.8 nan 268.2 268.0 268.4 268.8

  array([[[[      nan, 268.18625, 263.21762, ...,       nan,       nan,
                  nan],
           [      nan, 261.4894 , 259.58054, ...,       nan,       nan,
                  nan],
           [270.17227, 260.38303, 257.93915, ...,       nan,       nan,
                  nan],
           ...,
           [      nan,       nan,       nan, ...,       nan,       nan,
                  nan],
           [      nan,       nan,       nan, ...,       nan,       nan,
                  nan],
           [      nan,       nan,       nan, ..., 262.4566 , 262.70773,
            262.97894]],
  
          [[      nan, 274.94598, 272.1838 , ...,       nan,       nan,
                  nan],
           [      nan, 272.17087, 270.1366 , ...,       nan,       nan,
                  nan],
           [276.09476, 271.16046, 269.08408, ...,       nan,       nan,
                  nan],
  ...
           [      nan,       nan,       nan, ...,       nan,       nan,
                  nan],
           [      nan,       nan,       nan, ...,       nan,       nan,
                  nan],
           [      nan,       nan,       nan, ..., 268.94943, 268.97186,
            269.03262]],
  
          [[      nan, 280.14944, 277.7591 , ...,       nan,       nan,
                  nan],
           [      nan, 276.78476, 274.67453, ...,       nan,       nan,
                  nan],
           [280.1642 , 275.19962, 273.14044, ...,       nan,       nan,
                  nan],
           ...,
           [      nan,       nan,       nan, ...,       nan,       nan,
                  nan],
           [      nan,       nan,       nan, ...,       nan,       nan,
                  nan],
           [      nan,       nan,       nan, ..., 268.04092, 268.38055,
            268.77368]]]], dtype=float32)

• Coordinates: (5)
  • lat
    (lat)
    float64
    58.9 59.84 60.79 ... 69.27 70.21

    axis :

    Y

    bounds :

    lat_bnds

    standard_name :

    latitude

    title :

    Latitude

    type :

    double

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    long_name :

    latitude

    array([58.900524, 59.842932, 60.78534 , 61.727749, 62.670157, 63.612565,
           64.554974, 65.497382, 66.439791, 67.382199, 68.324607, 69.267016,
           70.209424])

  • lon
    (lon)
    float64
    5.0 6.25 7.5 ... 27.5 28.75 30.0

    axis :

    X

    bounds :

    lon_bnds

    standard_name :

    longitude

    title :

    Longitude

    type :

    double

    units :

    degrees_east

    valid_max :

    360.0

    valid_min :

    0.0

    long_name :

    longitude

    array([ 5.  ,  6.25,  7.5 ,  8.75, 10.  , 11.25, 12.5 , 13.75, 15.  , 16.25,
           17.5 , 18.75, 20.  , 21.25, 22.5 , 23.75, 25.  , 26.25, 27.5 , 28.75,
           30.  ])

  • time
    (time)
    object
    1850-01-15 12:00:00 ... 2014-12-...

    axis :

    T

    bounds :

    time_bnds

    standard_name :

    time

    title :

    time

    type :

    double

    array([cftime.DatetimeNoLeap(1850, 1, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 2, 14, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(1850, 3, 15, 12, 0, 0, 0, has_year_zero=True),
           ...,
           cftime.DatetimeNoLeap(2014, 10, 15, 12, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 11, 15, 0, 0, 0, 0, has_year_zero=True),
           cftime.DatetimeNoLeap(2014, 12, 15, 12, 0, 0, 0, has_year_zero=True)],
          dtype=object)

  • member_id
    (member_id)
    object
    'r1i1p1f1'

    array(['r1i1p1f1'], dtype=object)

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    GeoTransform :

    4.375 1.25 0.0 58.42931937172774 0.0 0.9424083769633498

    array(0)

• Attributes: (19)

  cell_measures :

  area: areacella

  cell_methods :

  area: time: mean

  comment :

  near-surface (usually, 2 meter) air temperature

  description :

  near-surface (usually, 2 meter) air temperature

  frequency :

  mon

  id :

  tas

  long_name :

  Near-Surface Air Temperature

  mipTable :

  Amon

  out_name :

  tas

  prov :

  Amon ((isd.003))

  realm :

  atmos

  standard_name :

  air_temperature

  time :

  time

  time_label :

  time-mean

  time_title :

  Temporal mean

  title :

  Near-Surface Air Temperature

  type :

  real

  units :

  K

  variable_id :

  tas

Visualize with matplotlib

import matplotlib.pyplot as plt
import cartopy.crs as ccrs

fig = plt.figure(1, figsize=[20, 10])

# We're using cartopy and are plotting in PlateCarree projection 
# (see documentation on cartopy)
ax = plt.subplot(1, 1, 1, projection=ccrs.AlbersEqualArea(central_longitude=20.0, central_latitude=40.0))
#ax.set_extent([15.5, 27.5, 36, 41], crs=ccrs.PlateCarree()) # lon1 lon2 lat1 lat2
ax.coastlines(resolution='10m')
ax.gridlines(draw_labels=True)

tas_AOI.sel(time='1980-06-15').plot(ax=ax, transform=ccrs.PlateCarree(), cmap="coolwarm")

# One way to customize your title
plt.title("Near surface temperature in Norway", fontsize=18)

Text(0.5, 1.0, 'Near surface temperature in Norway')

Visualization with HoloViews

import holoviews as hv
import hvplot.xarray
import hvplot.pandas

Plotting data through the HoloViews back-end thanks to the hvplot that acts as high-level plotting API.

tas_AOI.isel(time=0, member_id=0).hvplot(cmap="coolwarm", width=1000, height=1000)

Having a look to data distribution can reveal a lot about the data.

tas_AOI[0].hvplot.hist(cmap="coolwarm",bins=25, width=800, height=700)

Multi-plots using groupby

To be able to visualize interactively all the different available times, we can use groupby time.

tas_AOI.isel(member_id=0).hvplot(groupby ='time', cmap="coolwarm", width=800, height=700)

We can add a histogram to the visualization.

tas_AOI.isel(member_id=0).hvplot(groupby='time', cmap='coolwarm', width=800, height=700 ).hist()

Plot data on a particular projection

plot = tas_AOI.isel(time=0, member_id=0).hvplot.quadmesh(x='lon', y='lat'
                          , rasterize=True
                          , geo=True
                          , global_extent=False
                          , projection=ccrs.Orthographic(30, 90)  
                          , project=True
                          , cmap='coolwarm'
                          , coastline='50m'           
                          , frame_width=400
                          , title="Near-surface Temperature over Norway (CMIP6 CESM2)"
                            )

WARNING:param.main: Calling the .opts method with options broken down by options group (i.e. separate plot, style and norm groups) is deprecated. Use the .options method converting to the simplified format instead or use hv.opts.apply_groups for backward compatibility.

plot

Plot a single point over the time dimension

tas_AOI.isel(member_id=0).sel(lat=60, lon=10.75, method='nearest').hvplot(ylim=(260, 293))

WARNING:param.CurvePlot02669: Converting cftime.datetime from a non-standard calendar (noleap) to a standard calendar for plotting. This may lead to subtle errors in formatting dates, for accurate tick formatting switch to the matplotlib backend.

Key Points

• Rioxarray for clipping over an area of interest
• Interactive plot with HoloViews and Xarray

References

Kog95

F.N. Kogan. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11):91–100, 1995. Natural Hazards: Monitoring and Assessment Using Remote Sensing Technique. URL: https://www.sciencedirect.com/science/article/pii/027311779500079T, doi:https://doi.org/10.1016/0273-1177(95)00079-T.

Packages citation

HH17

S. Hoyer and J. Hamman. Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software, 2017. URL: https://doi.org/10.5334/jors.148, doi:10.5334/jors.148.

Hun07

J. D. Hunter. Matplotlib: a 2d graphics environment. Computing in Science & Engineering, 9(3):90–95, 2007. doi:10.1109/MCSE.2007.55.

JdBF+20

Kelsey Jordahl, Joris Van den Bossche, Martin Fleischmann, Jacob Wasserman, James McBride, Jeffrey Gerard, Jeff Tratner, Matthew Perry, Adrian Garcia Badaracco, Carson Farmer, Geir Arne Hjelle, Alan D. Snow, Micah Cochran, Sean Gillies, Lucas Culbertson, Matt Bartos, Nick Eubank, maxalbert, Aleksey Bilogur, Sergio Rey, Christopher Ren, Dani Arribas-Bel, Leah Wasser, Levi John Wolf, Martin Journois, Joshua Wilson, Adam Greenhall, Chris Holdgraf, Filipe, and François Leblanc. Geopandas/geopandas: v0.8.1. July 2020. URL: https://doi.org/10.5281/zenodo.3946761, doi:10.5281/zenodo.3946761.

RSB+20

Philipp Rudiger, Jean-Luc Stevens, James A. Bednar, Bas Nijholt, Andrew, Chris B, Achim Randelhoff, Jon Mease, Vasco Tenner, maxalbert, Markus Kaiser, ea42gh, Jordan Samuels, stonebig, Florian LB, Andrew Tolmie, Daniel Stephan, Scott Lowe, John Bampton, henriqueribeiro, Irv Lustig, Julia Signell, Justin Bois, Leopold Talirz, Lukas Barth, Maxime Liquet, Ram Rachum, Yuval Langer, arabidopsis, and kbowen. Holoviz/holoviews: version 1.13.3. June 2020. URL: https://doi.org/10.5281/zenodo.3904606, doi:10.5281/zenodo.3904606.

SBR+22

Alan D. Snow, David Brochart, Martin Raspaud, Ray Bell, RichardScottOZ, Taher Chegini, Alessandro Amici, Rémi Braun, Andrew Annex, David Hoese, Fred Bunt, GBallesteros, Joe Hamman, Markus Zehner, Mauricio Cordeiro, Scott Henderson, Seth Miller, The Gitter Badger, Tom Augspurger, apiwat-chantawibul, and pmallas. Corteva/rioxarray: 0.11.1 release. April 2022. URL: https://doi.org/10.5281/zenodo.6478182, doi:10.5281/zenodo.6478182.

USR+20

Leonardo Uieda, Santiago Rubén Soler, Rémi Rampin, Hugo van Kemenade, Matthew Turk, Daniel Shapero, Anderson Banihirwe, and John Leeman. Pooch: a friend to fetch your data files. Journal of Open Source Software, 5(45):1943, 2020. URL: https://doi.org/10.21105/joss.01943, doi:10.21105/joss.01943.

MetOffice15

Met Office. Cartopy: a cartographic python library with a matplotlib interface. Exeter, Devon, 2010 - 2015. URL: http://scitools.org.uk/cartopy.