.ipynb .pdf

repository open issue suggest edit

Contents

02_InteractiveVisualization Part 1: Geoviews

---

Overview

A team at Google Research & Cloud are making parts of the ECMWF Reanalysis version 5 (aka ERA-5) accessible in a Analysis Ready, Cloud Optimized (aka ARCO) format.

In this notebook, we will do the following:

1. Access the ERA-5 ARCO catalog

2. Select a particular dataset and variable from the catalog

3. Convert the data from Gaussian to Cartesian coordinates

4. Plot a map of sea-level pressure contours and 2-meter temperature mesh using Geoviews.

Prerequisites

Concepts	Importance	Notes
Cartopy	Necessary
Xarray	Necessary
[Geoviews]	Necessary

• Time to learn: 30 minutes

---

Imports

import fsspec
import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from metpy import calc as mpcalc
from metpy.units import units
import metpy
import scipy.spatial
import numpy as np

/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'ucx.cuda-copy' has been deprecated; please use 'distributed.ucx.cuda_copy' instead
  warnings.warn(
/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'ucx.tcp' has been deprecated; please use 'distributed.ucx.tcp' instead
  warnings.warn(
/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'ucx.nvlink' has been deprecated; please use 'distributed.ucx.nvlink' instead
  warnings.warn(
/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'ucx.infiniband' has been deprecated; please use 'distributed.ucx.infiniband' instead
  warnings.warn(
/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'ucx.rdmacm' has been deprecated; please use 'distributed.ucx.rdmacm' instead
  warnings.warn(
/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'ucx.net-devices' has been deprecated; please use 'distributed.ucx.net-devices' instead
  warnings.warn(
/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'ucx.reuse-endpoints' has been deprecated; please use 'distributed.ucx.reuse-endpoints' instead
  warnings.warn(
/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'rmm.pool-size' has been deprecated; please use 'distributed.rmm.pool-size' instead
  warnings.warn(

Access the ARCO ERA-5 catalog on Google Cloud

Let’s open the 37-level isobaric surfaces reanalysis Zarr file

reanalysis = xr.open_zarr(
    'gs://gcp-public-data-arco-era5/ar/1959-2022-full_37-1h-0p25deg-chunk-1.zarr-v2', 
    chunks={'time': 48},
    consolidated=True,
)

reanalysis

<xarray.Dataset>
Dimensions:                                           (time: 552264,
                                                       latitude: 721,
                                                       longitude: 1440,
                                                       level: 37)
Coordinates:
  * latitude                                          (latitude) float32 90.0...
  * level                                             (level) int64 1 2 ... 1000
  * longitude                                         (longitude) float32 0.0...
  * time                                              (time) datetime64[ns] 1...
Data variables: (12/31)
    10m_u_component_of_wind                           (time, latitude, longitude) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>
    10m_v_component_of_wind                           (time, latitude, longitude) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>
    2m_temperature                                    (time, latitude, longitude) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>
    angle_of_sub_gridscale_orography                  (latitude, longitude) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>
    anisotropy_of_sub_gridscale_orography             (latitude, longitude) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>
    geopotential                                      (time, level, latitude, longitude) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>
    ...                                                ...
    total_precipitation                               (time, latitude, longitude) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>
    type_of_high_vegetation                           (latitude, longitude) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>
    type_of_low_vegetation                            (latitude, longitude) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>
    u_component_of_wind                               (time, level, latitude, longitude) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>
    v_component_of_wind                               (time, level, latitude, longitude) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>
    vertical_velocity                                 (time, level, latitude, longitude) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 552264
  • latitude: 721
  • longitude: 1440
  • level: 37
• Coordinates: (4)
  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    long_name :

    latitude

    units :

    degrees_north

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)

  • level
    (level)
    int64
    1 2 3 5 7 ... 900 925 950 975 1000

    array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,
            150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,
            650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,
           1000])

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1959-01-01 ... 2021-12-31T23:00:00

    array(['1959-01-01T00:00:00.000000000', '1959-01-01T01:00:00.000000000',
           '1959-01-01T02:00:00.000000000', ..., '2021-12-31T21:00:00.000000000',
           '2021-12-31T22:00:00.000000000', '2021-12-31T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (31)
  • 10m_u_component_of_wind
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre U wind component

    short_name :

    u10

    units :

    m s**-1

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_v_component_of_wind
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre V wind component

    short_name :

    v10

    units :

    m s**-1

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 2m_temperature
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    2 metre temperature

    short_name :

    t2m

    units :

    K

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • angle_of_sub_gridscale_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Angle of sub-gridscale orography

    short_name :

    anor

    units :

    radians

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • anisotropy_of_sub_gridscale_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Anisotropy of sub-gridscale orography

    short_name :

    isor

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 77.18 TiB 6.87 GiB Shape (552264, 37, 721, 1440) (48, 37, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential_at_surface
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • high_vegetation_cover
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    High vegetation cover

    short_name :

    cvh

    units :

    (0 - 1)

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lake_cover
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Lake cover

    short_name :

    cl

    units :

    (0 - 1)

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lake_depth
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Lake total depth

    short_name :

    dl

    units :

    m

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • land_sea_mask
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Land-sea mask

    short_name :

    lsm

    standard_name :

    land_binary_mask

    units :

    (0 - 1)

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • low_vegetation_cover
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Low vegetation cover

    short_name :

    cvl

    units :

    (0 - 1)

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_sea_level_pressure
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    Mean sea level pressure

    short_name :

    msl

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    Pa

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sea_ice_cover
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    Sea ice area fraction

    short_name :

    siconc

    standard_name :

    sea_ice_area_fraction

    units :

    (0 - 1)

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sea_surface_temperature
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    Sea surface temperature

    short_name :

    sst

    units :

    K

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • slope_of_sub_gridscale_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Slope of sub-gridscale orography

    short_name :

    slor

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soil_type
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Soil type

    short_name :

    slt

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • specific_humidity
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>

    long_name :

    Specific humidity

    short_name :

    q

    standard_name :

    specific_humidity

    units :

    kg kg**-1

    Array Chunk Bytes 77.18 TiB 6.87 GiB Shape (552264, 37, 721, 1440) (48, 37, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • standard_deviation_of_filtered_subgrid_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Standard deviation of filtered subgrid orography

    short_name :

    sdfor

    units :

    m

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • standard_deviation_of_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Standard deviation of orography

    short_name :

    sdor

    units :

    m

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_pressure
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    Surface pressure

    short_name :

    sp

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temperature
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>

    long_name :

    Temperature

    short_name :

    t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 77.18 TiB 6.87 GiB Shape (552264, 37, 721, 1440) (48, 37, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • toa_incident_solar_radiation
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    TOA incident solar radiation

    short_name :

    tisr

    units :

    J m**-2

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_cloud_cover
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    Total cloud cover

    short_name :

    tcc

    standard_name :

    cloud_area_fraction

    units :

    (0 - 1)

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_column_water_vapour
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    Total column vertically-integrated water vapour

    short_name :

    tcwv

    standard_name :

    lwe_thickness_of_atmosphere_mass_content_of_water_vapor

    units :

    kg m**-2

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 2.09 TiB 190.11 MiB Shape (552264, 721, 1440) (48, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • type_of_high_vegetation
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Type of high vegetation

    short_name :

    tvh

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • type_of_low_vegetation
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Type of low vegetation

    short_name :

    tvl

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u_component_of_wind
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>

    long_name :

    U component of wind

    short_name :

    u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 77.18 TiB 6.87 GiB Shape (552264, 37, 721, 1440) (48, 37, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_component_of_wind
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>

    long_name :

    V component of wind

    short_name :

    v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 77.18 TiB 6.87 GiB Shape (552264, 37, 721, 1440) (48, 37, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vertical_velocity
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>

    long_name :

    Vertical velocity

    short_name :

    w

    standard_name :

    lagrangian_tendency_of_air_pressure

    units :

    Pa s**-1

    Array Chunk Bytes 77.18 TiB 6.87 GiB Shape (552264, 37, 721, 1440) (48, 37, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float32', name='latitude', length=721))

  • level
    PandasIndex

    PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,
            150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,
            650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,
           1000],
          dtype='int64', name='level'))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float32', name='longitude', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1959-01-01 00:00:00', '1959-01-01 01:00:00',
                   '1959-01-01 02:00:00', '1959-01-01 03:00:00',
                   '1959-01-01 04:00:00', '1959-01-01 05:00:00',
                   '1959-01-01 06:00:00', '1959-01-01 07:00:00',
                   '1959-01-01 08:00:00', '1959-01-01 09:00:00',
                   ...
                   '2021-12-31 14:00:00', '2021-12-31 15:00:00',
                   '2021-12-31 16:00:00', '2021-12-31 17:00:00',
                   '2021-12-31 18:00:00', '2021-12-31 19:00:00',
                   '2021-12-31 20:00:00', '2021-12-31 21:00:00',
                   '2021-12-31 22:00:00', '2021-12-31 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=552264, freq=None))

• Attributes: (0)

geop = reanalysis.geopotential
temp = reanalysis.temperature

geop

<xarray.DataArray 'geopotential' (time: 552264, level: 37, latitude: 721,
                                  longitude: 1440)>
dask.array<open_dataset-geopotential, shape=(552264, 37, 721, 1440), dtype=float32, chunksize=(48, 37, 721, 1440), chunktype=numpy.ndarray>
Coordinates:
  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0
  * level      (level) int64 1 2 3 5 7 10 20 30 ... 850 875 900 925 950 975 1000
  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
  * time       (time) datetime64[ns] 1959-01-01 ... 2021-12-31T23:00:00
Attributes:
    long_name:      Geopotential
    short_name:     z
    standard_name:  geopotential
    units:          m**2 s**-2

xarray.DataArray

'geopotential'

• time: 552264
• level: 37
• latitude: 721
• longitude: 1440

• dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>

  Array Chunk Bytes 77.18 TiB 6.87 GiB Shape (552264, 37, 721, 1440) (48, 37, 721, 1440) Dask graph 11506 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (4)
  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    long_name :

    latitude

    units :

    degrees_north

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)

  • level
    (level)
    int64
    1 2 3 5 7 ... 900 925 950 975 1000

    array([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,
            150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,
            650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,
           1000])

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1959-01-01 ... 2021-12-31T23:00:00

    array(['1959-01-01T00:00:00.000000000', '1959-01-01T01:00:00.000000000',
           '1959-01-01T02:00:00.000000000', ..., '2021-12-31T21:00:00.000000000',
           '2021-12-31T22:00:00.000000000', '2021-12-31T23:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (4)
  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float32', name='latitude', length=721))

  • level
    PandasIndex

    PandasIndex(Index([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,
            150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,
            650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,
           1000],
          dtype='int64', name='level'))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float32', name='longitude', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1959-01-01 00:00:00', '1959-01-01 01:00:00',
                   '1959-01-01 02:00:00', '1959-01-01 03:00:00',
                   '1959-01-01 04:00:00', '1959-01-01 05:00:00',
                   '1959-01-01 06:00:00', '1959-01-01 07:00:00',
                   '1959-01-01 08:00:00', '1959-01-01 09:00:00',
                   ...
                   '2021-12-31 14:00:00', '2021-12-31 15:00:00',
                   '2021-12-31 16:00:00', '2021-12-31 17:00:00',
                   '2021-12-31 18:00:00', '2021-12-31 19:00:00',
                   '2021-12-31 20:00:00', '2021-12-31 21:00:00',
                   '2021-12-31 22:00:00', '2021-12-31 23:00:00'],
                  dtype='datetime64[ns]', name='time', length=552264, freq=None))

• Attributes: (4)

  long_name :

  Geopotential

  short_name :

  z

  standard_name :

  geopotential

  units :

  m**2 s**-2

Select time and level ranges from the dataset.

geopSub1 = geop.sel(time=slice('1993-03-13T18:00:00','1993-03-14T00:00:00'), level=500).compute()
tempSub1 = temp.sel(time=slice('1993-03-13T18:00:00','1993-03-14T00:00:00'), level=850).compute()

/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'ucx' has been deprecated; please use 'distributed.comm.ucx' instead
  warnings.warn(
/knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'rmm' has been deprecated; please use 'distributed.rmm' instead
  warnings.warn(

Convert to dam and deg C.

Z = mpcalc.geopotential_to_height(geopSub1).metpy.convert_units('dam')

T = tempSub1.metpy.convert_units('degC')

Z

<xarray.DataArray (time: 7, latitude: 721, longitude: 1440)>
<Quantity([[[496.11923 496.11923 496.11923 ... 496.11923 496.11923 496.11923]
  [495.4044  495.40805 495.40988 ... 495.39526 495.4008  495.4026 ]
  [494.79037 494.7941  494.79956 ... 494.77393 494.77945 494.78488]
  ...
  [491.92023 491.91656 491.91656 ... 491.92023 491.92023 491.92023]
  [491.47482 491.47482 491.47482 ... 491.47482 491.47482 491.47482]
  [491.01846 491.01846 491.01846 ... 491.01846 491.01846 491.01846]]

 [[496.9404  496.9404  496.9404  ... 496.9404  496.9404  496.9404 ]
  [496.1687  496.17236 496.17422 ... 496.15958 496.16138 496.16684]
  [495.5034  495.51074 495.51627 ... 495.48688 495.4924  495.49786]
  ...
  [492.12735 492.12735 492.12735 ... 492.12915 492.12915 492.12915]
  [491.70944 491.70578 491.70578 ... 491.70944 491.70944 491.70944]
  [491.2622  491.2622  491.2622  ... 491.2622  491.2622  491.2622 ]]

 [[497.71384 497.71384 497.71384 ... 497.71384 497.71384 497.71384]
  [496.88535 496.88718 496.89084 ... 496.87436 496.878   496.87985]
  [496.17422 496.18335 496.187   ... 496.1577  496.16504 496.17056]
  ...
...
  ...
  [492.77792 492.77792 492.77792 ... 492.7798  492.7798  492.7798 ]
  [492.3491  492.34543 492.34543 ... 492.3491  492.3491  492.3491 ]
  [491.8854  491.8854  491.8854  ... 491.8854  491.8854  491.8854 ]]

 [[500.47592 500.47592 500.47592 ... 500.47592 500.47592 500.47592]
  [499.58884 499.5925  499.5962  ... 499.57593 499.5796  499.58517]
  [498.8355  498.84283 498.85016 ... 498.81174 498.819   498.8264 ]
  ...
  [492.83292 492.83292 492.83292 ... 492.83478 492.83292 492.83292]
  [492.38574 492.38574 492.38574 ... 492.38574 492.38574 492.38574]
  [491.92023 491.92023 491.92023 ... 491.92023 491.92023 491.92023]]

 [[501.2972  501.2972  501.2972  ... 501.2972  501.2972  501.2972 ]
  [500.40225 500.4078  500.40973 ... 500.39102 500.39478 500.40036]
  [499.6473  499.65485 499.66232 ... 499.6268  499.63428 499.64175]
  ...
  [492.91885 492.91885 492.91885 ... 492.9226  492.9226  492.9226 ]
  [492.4518  492.4518  492.4518  ... 492.4518  492.4518  492.4518 ]
  [491.97714 491.97714 491.97714 ... 491.97714 491.97714 491.97714]]], 'decameter')>
Coordinates:
  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0
    level      int64 500
  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
  * time       (time) datetime64[ns] 1993-03-13T18:00:00 ... 1993-03-14

xarray.DataArray

• time: 7
• latitude: 721
• longitude: 1440

• <Quantity([[[496.11923 496.11923 496.11923 ... 496.11923 496.11923 ...

  Magnitude

  [[[496.1192321777344 496.1192321777344 496.1192321777344 ... 496.1192321777344 496.1192321777344 496.1192321777344] [495.4043884277344 495.4080505371094 495.4098815917969 ... 495.395263671875 495.4007873535156 495.402587890625] [494.7903747558594 494.7940979003906 494.799560546875 ... 494.77392578125 494.7794494628906 494.7848815917969] ... [491.92022705078125 491.91656494140625 491.91656494140625 ... 491.92022705078125 491.92022705078125 491.92022705078125] [491.4748229980469 491.4748229980469 491.4748229980469 ... 491.4748229980469 491.4748229980469 491.4748229980469] [491.0184631347656 491.0184631347656 491.0184631347656 ... 491.0184631347656 491.0184631347656 491.0184631347656]] [[496.9403991699219 496.9403991699219 496.9403991699219 ... 496.9403991699219 496.9403991699219 496.9403991699219] [496.168701171875 496.17236328125 496.1742248535156 ... 496.1595764160156 496.161376953125 496.1668395996094] [495.5033874511719 495.5107421875 495.5162658691406 ... 495.48687744140625 495.4924011230469 495.49786376953125] ... [492.1273498535156 492.1273498535156 492.1273498535156 ... 492.129150390625 492.129150390625 492.129150390625] [491.7094421386719 491.7057800292969 491.7057800292969 ... 491.7094421386719 491.7094421386719 491.7094421386719] [491.26220703125 491.26220703125 491.26220703125 ... 491.26220703125 491.26220703125 491.26220703125]] [[497.7138366699219 497.7138366699219 497.7138366699219 ... 497.7138366699219 497.7138366699219 497.7138366699219] [496.8853454589844 496.8871765136719 496.8908386230469 ... 496.8743591308594 496.87799072265625 496.8798522949219] [496.1742248535156 496.183349609375 496.18701171875 ... 496.15771484375 496.1650390625 496.1705627441406] ... [492.3545837402344 492.3545837402344 492.3545837402344 ... 492.3582458496094 492.3582458496094 492.3545837402344] [491.94219970703125 491.94219970703125 491.94219970703125 ... 491.94219970703125 491.94219970703125 491.94219970703125] [491.4950256347656 491.4950256347656 491.4950256347656 ... 491.4950256347656 491.4950256347656 491.4950256347656]] ... [[499.31573486328125 499.31573486328125 499.31573486328125 ... 499.31573486328125 499.31573486328125 499.31573486328125] [498.4359436035156 498.4396057128906 498.4432678222656 ... 498.4248962402344 498.4267578125 498.4322814941406] [497.678955078125 497.68634033203125 497.6917419433594 ... 497.6587829589844 497.664306640625 497.67169189453125] ... [492.7779235839844 492.7779235839844 492.7779235839844 ... 492.77978515625 492.77978515625 492.77978515625] [492.3490905761719 492.3454284667969 492.3454284667969 ... 492.3490905761719 492.3490905761719 492.3490905761719] [491.8854064941406 491.8854064941406 491.8854064941406 ... 491.8854064941406 491.8854064941406 491.8854064941406]] [[500.4759216308594 500.4759216308594 500.4759216308594 ... 500.4759216308594 500.4759216308594 500.4759216308594] [499.5888366699219 499.5924987792969 499.59619140625 ... 499.575927734375 499.57958984375 499.5851745605469] [498.83551025390625 498.84283447265625 498.85015869140625 ... 498.8117370605469 498.8190002441406 498.8263854980469] ... [492.8329162597656 492.8329162597656 492.8329162597656 ... 492.83477783203125 492.8329162597656 492.8329162597656] [492.3857421875 492.3857421875 492.3857421875 ... 492.3857421875 492.3857421875 492.3857421875] [491.92022705078125 491.92022705078125 491.92022705078125 ... 491.92022705078125 491.92022705078125 491.92022705078125]] [[501.2972106933594 501.2972106933594 501.2972106933594 ... 501.2972106933594 501.2972106933594 501.2972106933594] [500.4022521972656 500.4078063964844 500.40972900390625 ... 500.3910217285156 500.394775390625 500.4003601074219] [499.6473083496094 499.65484619140625 499.6623229980469 ... 499.6268005371094 499.63427734375 499.6417541503906] ... [492.9188537597656 492.9188537597656 492.9188537597656 ... 492.922607421875 492.922607421875 492.922607421875] [492.4518127441406 492.4518127441406 492.4518127441406 ... 492.4518127441406 492.4518127441406 492.4518127441406] [491.9771423339844 491.9771423339844 491.9771423339844 ... 491.9771423339844 491.9771423339844 491.9771423339844]]]

  Units

  decameter

• Coordinates: (4)
  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    long_name :

    latitude

    units :

    degrees_north

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)

  • level
    ()
    int64
    500

    array(500)

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1993-03-13T18:00:00 ... 1993-03-14

    array(['1993-03-13T18:00:00.000000000', '1993-03-13T19:00:00.000000000',
           '1993-03-13T20:00:00.000000000', '1993-03-13T21:00:00.000000000',
           '1993-03-13T22:00:00.000000000', '1993-03-13T23:00:00.000000000',
           '1993-03-14T00:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float32', name='latitude', length=721))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float32', name='longitude', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1993-03-13 18:00:00', '1993-03-13 19:00:00',
                   '1993-03-13 20:00:00', '1993-03-13 21:00:00',
                   '1993-03-13 22:00:00', '1993-03-13 23:00:00',
                   '1993-03-14 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (0)

T

<xarray.DataArray 'temperature' (time: 7, latitude: 721, longitude: 1440)>
<Quantity([[[-27.231277 -27.231277 -27.231277 ... -27.231277 -27.231277 -27.231277]
  [-27.653152 -27.650772 -27.650772 ... -27.656723 -27.654343 -27.653152]
  [-28.072647 -28.070251 -28.069061 ... -28.080978 -28.076218 -28.075027]
  ...
  [-29.216705 -29.2191   -29.2191   ... -29.215515 -29.216705 -29.216705]
  [-29.601654 -29.601654 -29.601654 ... -29.601654 -29.601654 -29.601654]
  [-30.096222 -30.096222 -30.096222 ... -30.096222 -30.096222 -30.096222]]

 [[-27.255112 -27.255112 -27.255112 ... -27.255112 -27.255112 -27.255112]
  [-27.598328 -27.595947 -27.594757 ... -27.601898 -27.600708 -27.598328]
  [-27.967773 -27.964188 -27.961807 ... -27.973724 -27.971344 -27.970154]
  ...
  [-29.051056 -29.051056 -29.053452 ... -29.047485 -29.047485 -29.049866]
  [-29.444336 -29.444336 -29.444336 ... -29.444336 -29.444336 -29.444336]
  [-29.961548 -29.961548 -29.961548 ... -29.961548 -29.961548 -29.961548]]

 [[-27.234848 -27.234848 -27.234848 ... -27.234848 -27.234848 -27.234848]
  [-27.538742 -27.537552 -27.535172 ... -27.543518 -27.541122 -27.538742]
  [-27.901031 -27.898651 -27.89627  ... -27.908188 -27.906982 -27.904602]
  ...
...
  ...
  [-28.604156 -28.607727 -28.610123 ... -28.59462  -28.598206 -28.600586]
  [-28.928314 -28.930695 -28.931885 ... -28.924744 -28.925934 -28.925934]
  [-29.38713  -29.38713  -29.38713  ... -29.38713  -29.38713  -29.38713 ]]

 [[-26.82727  -26.82727  -26.82727  ... -26.82727  -26.82727  -26.82727 ]
  [-27.004837 -27.002457 -27.001266 ... -27.008423 -27.007233 -27.007233]
  [-27.172882 -27.169312 -27.166916 ... -27.182419 -27.178833 -27.175262]
  ...
  [-28.549347 -28.554108 -28.555298 ... -28.54219  -28.54457  -28.548141]
  [-28.811523 -28.813904 -28.813904 ... -28.807953 -28.809143 -28.809143]
  [-29.171432 -29.171432 -29.171432 ... -29.171432 -29.171432 -29.171432]]

 [[-26.590942 -26.590942 -26.590942 ... -26.590942 -26.590942 -26.590942]
  [-26.659225 -26.658035 -26.658035 ... -26.664032 -26.664032 -26.661621]
  [-26.710754 -26.708359 -26.704758 ... -26.717941 -26.716736 -26.714355]
  ...
  [-28.46251  -28.46611  -28.470901 ... -28.450531 -28.454132 -28.458923]
  [-28.663803 -28.666199 -28.667404 ... -28.660217 -28.661407 -28.661407]
  [-28.958557 -28.958557 -28.958557 ... -28.958557 -28.958557 -28.958557]]], 'degree_Celsius')>
Coordinates:
  * latitude   (latitude) float32 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0
    level      int64 850
  * longitude  (longitude) float32 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
  * time       (time) datetime64[ns] 1993-03-13T18:00:00 ... 1993-03-14
Attributes:
    long_name:      Temperature
    short_name:     t
    standard_name:  air_temperature

xarray.DataArray

'temperature'

• time: 7
• latitude: 721
• longitude: 1440

• <Quantity([[[-27.231277 -27.231277 -27.231277 ... -27.231277 -27.23...

  Magnitude

  [[[-27.231277465820312 -27.231277465820312 -27.231277465820312 ... -27.231277465820312 -27.231277465820312 -27.231277465820312] [-27.653152465820312 -27.650772094726562 -27.650772094726562 ... -27.656723022460938 -27.654342651367188 -27.653152465820312] [-28.072647094726562 -28.07025146484375 -28.069061279296875 ... -28.080978393554688 -28.076217651367188 -28.075027465820312] ... [-29.216705322265625 -29.219100952148438 -29.219100952148438 ... -29.21551513671875 -29.216705322265625 -29.216705322265625] [-29.601654052734375 -29.601654052734375 -29.601654052734375 ... -29.601654052734375 -29.601654052734375 -29.601654052734375] [-30.096221923828125 -30.096221923828125 -30.096221923828125 ... -30.096221923828125 -30.096221923828125 -30.096221923828125]] [[-27.255111694335938 -27.255111694335938 -27.255111694335938 ... -27.255111694335938 -27.255111694335938 -27.255111694335938] [-27.59832763671875 -27.595947265625 -27.594757080078125 ... -27.601898193359375 -27.6007080078125 -27.59832763671875] [-27.9677734375 -27.964187622070312 -27.961807250976562 ... -27.973724365234375 -27.971343994140625 -27.97015380859375] ... [-29.051055908203125 -29.051055908203125 -29.053451538085938 ... -29.0474853515625 -29.0474853515625 -29.04986572265625] [-29.4443359375 -29.4443359375 -29.4443359375 ... -29.4443359375 -29.4443359375 -29.4443359375] [-29.9615478515625 -29.9615478515625 -29.9615478515625 ... -29.9615478515625 -29.9615478515625 -29.9615478515625]] [[-27.234848022460938 -27.234848022460938 -27.234848022460938 ... -27.234848022460938 -27.234848022460938 -27.234848022460938] [-27.538742065429688 -27.537551879882812 -27.535171508789062 ... -27.54351806640625 -27.541122436523438 -27.538742065429688] [-27.901031494140625 -27.898651123046875 -27.896270751953125 ... -27.908187866210938 -27.906982421875 -27.90460205078125] ... [-28.861572265625 -28.86395263671875 -28.865142822265625 ... -28.855621337890625 -28.858001708984375 -28.85919189453125] [-29.277496337890625 -29.277496337890625 -29.279876708984375 ... -29.275115966796875 -29.275115966796875 -29.277496337890625] [-29.79351806640625 -29.79351806640625 -29.79351806640625 ... -29.79351806640625 -29.79351806640625 -29.79351806640625]] ... [[-27.020339965820312 -27.020339965820312 -27.020339965820312 ... -27.020339965820312 -27.020339965820312 -27.020339965820312] [-27.314697265625 -27.314697265625 -27.313507080078125 ... -27.320648193359375 -27.318267822265625 -27.31707763671875] [-27.604293823242188 -27.601898193359375 -27.59832763671875 ... -27.61383056640625 -27.611434936523438 -27.607864379882812] ... [-28.604156494140625 -28.60772705078125 -28.610122680664062 ... -28.594619750976562 -28.59820556640625 -28.6005859375] [-28.928314208984375 -28.930694580078125 -28.931884765625 ... -28.92474365234375 -28.925933837890625 -28.925933837890625] [-29.387130737304688 -29.387130737304688 -29.387130737304688 ... -29.387130737304688 -29.387130737304688 -29.387130737304688]] [[-26.8272705078125 -26.8272705078125 -26.8272705078125 ... -26.8272705078125 -26.8272705078125 -26.8272705078125] [-27.004837036132812 -27.002456665039062 -27.001266479492188 ... -27.0084228515625 -27.007232666015625 -27.007232666015625] [-27.172882080078125 -27.1693115234375 -27.166915893554688 ... -27.182418823242188 -27.1788330078125 -27.175262451171875] ... [-28.549346923828125 -28.554107666015625 -28.5552978515625 ... -28.542190551757812 -28.544570922851562 -28.548141479492188] [-28.8115234375 -28.81390380859375 -28.81390380859375 ... -28.807952880859375 -28.80914306640625 -28.80914306640625] [-29.171432495117188 -29.171432495117188 -29.171432495117188 ... -29.171432495117188 -29.171432495117188 -29.171432495117188]] [[-26.5909423828125 -26.5909423828125 -26.5909423828125 ... -26.5909423828125 -26.5909423828125 -26.5909423828125] [-26.659225463867188 -26.658035278320312 -26.658035278320312 ... -26.664031982421875 -26.664031982421875 -26.66162109375] [-26.71075439453125 -26.708358764648438 -26.704757690429688 ... -26.717941284179688 -26.71673583984375 -26.71435546875] ... [-28.462509155273438 -28.466110229492188 -28.470901489257812 ... -28.450531005859375 -28.454132080078125 -28.45892333984375] [-28.663803100585938 -28.66619873046875 -28.667404174804688 ... -28.66021728515625 -28.661407470703125 -28.661407470703125] [-28.95855712890625 -28.95855712890625 -28.95855712890625 ... -28.95855712890625 -28.95855712890625 -28.95855712890625]]]

  Units

  degree_Celsius

• Coordinates: (4)
  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    long_name :

    latitude

    units :

    degrees_north

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)

  • level
    ()
    int64
    850

    array(850)

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1993-03-13T18:00:00 ... 1993-03-14

    array(['1993-03-13T18:00:00.000000000', '1993-03-13T19:00:00.000000000',
           '1993-03-13T20:00:00.000000000', '1993-03-13T21:00:00.000000000',
           '1993-03-13T22:00:00.000000000', '1993-03-13T23:00:00.000000000',
           '1993-03-14T00:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (3)
  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float32', name='latitude', length=721))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float32', name='longitude', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1993-03-13 18:00:00', '1993-03-13 19:00:00',
                   '1993-03-13 20:00:00', '1993-03-13 21:00:00',
                   '1993-03-13 22:00:00', '1993-03-13 23:00:00',
                   '1993-03-14 00:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (3)

  long_name :

  Temperature

  short_name :

  t

  standard_name :

  air_temperature

Plot the data using Matplotlib

projData = ccrs.PlateCarree()

lons, lats = Z.longitude, Z.latitude

tLevels = np.arange(-45,39,3)
zLevels = np.arange(468, 606, 6)

res = '110m'
dpi = 100
fig = plt.figure(figsize=(2048/dpi, 1024/dpi))
ax = plt.subplot(1,1,1,projection=projData, frameon=False)

ax.add_feature(cfeature.COASTLINE.with_scale(res), edgecolor='brown', linewidth=2.5)
ax.add_feature(cfeature.BORDERS.with_scale(res), edgecolor='brown', linewidth=2.5)
ax.add_feature(cfeature.STATES.with_scale(res), edgecolor='brown')

# Temperature (T) contour fills

# Note we don't need the transform argument since the map/data projections are the same, but we'll leave it in
CF = ax.contourf(lons,lats,T,levels=tLevels,cmap=plt.get_cmap('coolwarm'), extend='both', transform=projData) 

# Height (Z) contour lines
CL = ax.contour(lons,lats,Z,zLevels,linewidths=1.25,colors='yellow', transform=projData)
ax.clabel(CL, inline_spacing=0.2, fontsize=8, fmt='%.0f')
fig.tight_layout(pad=.01)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[14], line 13
      8 ax.add_feature(cfeature.STATES.with_scale(res), edgecolor='brown')
     10 # Temperature (T) contour fills
     11 
     12 # Note we don't need the transform argument since the map/data projections are the same, but we'll leave it in
---> 13 CF = ax.contourf(lons,lats,T,levels=tLevels,cmap=plt.get_cmap('coolwarm'), extend='both', transform=projData) 
     15 # Height (Z) contour lines
     16 CL = ax.contour(lons,lats,Z,zLevels,linewidths=1.25,colors='yellow', transform=projData)

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:315, in _add_transform.<locals>.wrapper(self, *args, **kwargs)
    310     raise ValueError(f'Invalid transform: Spherical {func.__name__} '
    311                      'is not supported - consider using '
    312                      'PlateCarree/RotatedPole.')
    314 kwargs['transform'] = transform
--> 315 return func(self, *args, **kwargs)

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:359, in _add_transform_first.<locals>.wrapper(self, *args, **kwargs)
    357     # Use the new points as the input arguments
    358     args = (x, y, z) + args[3:]
--> 359 return func(self, *args, **kwargs)

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:1655, in GeoAxes.contourf(self, *args, **kwargs)
   1652             if not hasattr(sub_trans, 'force_path_ccw'):
   1653                 sub_trans.force_path_ccw = True
-> 1655 result = super().contourf(*args, **kwargs)
   1657 # We need to compute the dataLim correctly for contours.
   1658 if _MPL_VERSION.release[:2] < (3, 8):

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/matplotlib/__init__.py:1446, in _preprocess_data.<locals>.inner(ax, data, *args, **kwargs)
   1443 @functools.wraps(func)
   1444 def inner(ax, *args, data=None, **kwargs):
   1445     if data is None:
-> 1446         return func(ax, *map(sanitize_sequence, args), **kwargs)
   1448     bound = new_sig.bind(ax, *args, **kwargs)
   1449     auto_label = (bound.arguments.get(label_namer)
   1450                   or bound.kwargs.get(label_namer))

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/matplotlib/axes/_axes.py:6465, in Axes.contourf(self, *args, **kwargs)
   6456 """
   6457 Plot filled contours.
   6458 
   (...)
   6462 %(contour_doc)s
   6463 """
   6464 kwargs['filled'] = True
-> 6465 contours = mcontour.QuadContourSet(self, *args, **kwargs)
   6466 self._request_autoscale_view()
   6467 return contours

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/matplotlib/contour.py:769, in ContourSet.__init__(self, ax, levels, filled, linewidths, linestyles, hatches, alpha, origin, extent, cmap, colors, norm, vmin, vmax, extend, antialiased, nchunk, locator, transform, negative_linestyles, *args, **kwargs)
    765 if self.negative_linestyles is None:
    766     self.negative_linestyles = \
    767         mpl.rcParams['contour.negative_linestyle']
--> 769 kwargs = self._process_args(*args, **kwargs)
    770 self._process_levels()
    772 self._extend_min = self.extend in ['min', 'both']

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/matplotlib/contour.py:1411, in QuadContourSet._process_args(self, corner_mask, algorithm, *args, **kwargs)
   1408         corner_mask = mpl.rcParams['contour.corner_mask']
   1409 self._corner_mask = corner_mask
-> 1411 x, y, z = self._contour_args(args, kwargs)
   1413 contour_generator = contourpy.contour_generator(
   1414     x, y, z, name=self._algorithm, corner_mask=self._corner_mask,
   1415     line_type=contourpy.LineType.SeparateCode,
   1416     fill_type=contourpy.FillType.OuterCode,
   1417     chunk_size=self.nchunk)
   1419 t = self.get_transform()

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/matplotlib/contour.py:1450, in QuadContourSet._contour_args(self, args, kwargs)
   1448 elif nargs <= 4:
   1449     x, y, z_orig, *args = args
-> 1450     x, y, z = self._check_xyz(x, y, z_orig, kwargs)
   1451 else:
   1452     raise _api.nargs_error(fn, takes="from 1 to 4", given=nargs)

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/matplotlib/contour.py:1475, in QuadContourSet._check_xyz(self, x, y, z, kwargs)
   1472 z = ma.asarray(z)
   1474 if z.ndim != 2:
-> 1475     raise TypeError(f"Input z must be 2D, not {z.ndim}D")
   1476 if z.shape[0] < 2 or z.shape[1] < 2:
   1477     raise TypeError(f"Input z must be at least a (2, 2) shaped array, "
   1478                     f"but has shape {z.shape}")

TypeError: Input z must be 2D, not 3D

res = '110m'
dpi = 100
fig = plt.figure(figsize=(2048/dpi, 1024/dpi))
ax = plt.subplot(1,1,1,projection=projData, frameon=False)

ax.add_feature(cfeature.COASTLINE.with_scale(res), edgecolor='brown', linewidth=2.5)
ax.add_feature(cfeature.BORDERS.with_scale(res), edgecolor='brown', linewidth=2.5)
ax.add_feature(cfeature.STATES.with_scale(res), edgecolor='brown')

# Temperature (T) contour fills

# Note we don't need the transform argument since the map/data projections are the same, but we'll leave it in
CF = ax.contourf(lons,lats,T.isel(time=0),levels=tLevels,cmap=plt.get_cmap('coolwarm'), extend='both', transform=projData) 

# Height (Z) contour lines
CL = ax.contour(lons,lats,Z.isel(time=0),zLevels,linewidths=1.25,colors='yellow', transform=projData)
ax.clabel(CL, inline_spacing=0.2, fontsize=8, fmt='%.0f')
fig.tight_layout(pad=.01)

lonW, lonE, latS, latN = -130, -60, 20, 55 

lonRange = np.arange(lonW, lonE + 0.1, 0.25)
latRange = np.arange(latS, latN + 0.1, 0.25)

ZSub2 = Z.sel(latitude = latRange, longitude = lonRange)
TSub2 = T.sel(latitude = latRange, longitude = lonRange)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[18], line 1
----> 1 ZSub2 = Z.sel(latitude = latRange, longitude = lonRange)
      2 TSub2 = T.sel(latitude = latRange, longitude = lonRange)

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/xarray/core/dataarray.py:1582, in DataArray.sel(self, indexers, method, tolerance, drop, **indexers_kwargs)
   1472 def sel(
   1473     self: T_DataArray,
   1474     indexers: Mapping[Any, Any] | None = None,
   (...)
   1478     **indexers_kwargs: Any,
   1479 ) -> T_DataArray:
   1480     """Return a new DataArray whose data is given by selecting index
   1481     labels along the specified dimension(s).
   1482 
   (...)
   1580     Dimensions without coordinates: points
   1581     """
-> 1582     ds = self._to_temp_dataset().sel(
   1583         indexers=indexers,
   1584         drop=drop,
   1585         method=method,
   1586         tolerance=tolerance,
   1587         **indexers_kwargs,
   1588     )
   1589     return self._from_temp_dataset(ds)

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/xarray/core/dataset.py:3020, in Dataset.sel(self, indexers, method, tolerance, drop, **indexers_kwargs)
   2959 """Returns a new dataset with each array indexed by tick labels
   2960 along the specified dimension(s).
   2961 
   (...)
   3017 DataArray.sel
   3018 """
   3019 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "sel")
-> 3020 query_results = map_index_queries(
   3021     self, indexers=indexers, method=method, tolerance=tolerance
   3022 )
   3024 if drop:
   3025     no_scalar_variables = {}

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/xarray/core/indexing.py:190, in map_index_queries(obj, indexers, method, tolerance, **indexers_kwargs)
    188         results.append(IndexSelResult(labels))
    189     else:
--> 190         results.append(index.sel(labels, **options))
    192 merged = merge_sel_results(results)
    194 # drop dimension coordinates found in dimension indexers
    195 # (also drop multi-index if any)
    196 # (.sel() already ensures alignment)

File /knight/mamba_aug23/envs/sep23_env/lib/python3.11/site-packages/xarray/core/indexes.py:781, in PandasIndex.sel(self, labels, method, tolerance)
    779     indexer = get_indexer_nd(self.index, label_array, method, tolerance)
    780     if np.any(indexer < 0):
--> 781         raise KeyError(f"not all values found in index {coord_name!r}")
    783 # attach dimension names and/or coordinates to positional indexer
    784 if isinstance(label, Variable):

KeyError: "not all values found in index 'longitude'"

projMap = ccrs.

  Cell In[19], line 1
    projMap = ccrs.
                   ^
SyntaxError: invalid syntax

res = '50m'
dpi = 100
fig = plt.figure(figsize=(2048/dpi, 1024/dpi))
ax = plt.subplot(1,1,1,projection=, frameon=False)

ax.add_feature(cfeature.COASTLINE.with_scale(res), edgecolor='brown', linewidth=2.5)
ax.add_feature(cfeature.BORDERS.with_scale(res), edgecolor='brown', linewidth=2.5)
ax.add_feature(cfeature.STATES.with_scale(res), edgecolor='brown')

# Temperature (T) contour fills

# Note we don't need the transform argument since the map/data projections are the same, but we'll leave it in
CF = ax.contourf(lons,lats,T.isel(time=0),levels=tLevels,cmap=plt.get_cmap('coolwarm'), extend='both', transform=projData) 

# Height (Z) contour lines
CL = ax.contour(lons,lats,Z.isel(time=0),zLevels,linewidths=1.25,colors='yellow', transform=projData)
ax.clabel(CL, inline_spacing=0.2, fontsize=8, fmt='%.0f')
fig.tight_layout(pad=.01)

  Cell In[20], line 4
    ax = plt.subplot(1,1,1,projection=, frameon=False)
                                      ^
SyntaxError: invalid syntax

previous

Week 7: 10/2 - 10/8

next

Week 8: 10/9 - 10/15