Polytope time series

First, we retrieve ENS time series data using polytope.

import earthkit.data

# lat, lon
location = [38.78655345978706, -9.109280931080349]

request = {
    "class": "od",
    "stream" : "enfo",
    "type" : "pf",
    "date" : -1,
    "time" : "0000",
    "levtype" : "sfc",
    "expver" : 1,
    "domain" : "g",
    "param" : "164/167/169",
    "number" : "1/to/50",
    "step": "0/to/360",
    "feature" : {
        "type" : "timeseries",
        "points": [location],
        "axes": "step",
    },
}

ds = earthkit.data.from_source("polytope", "ecmwf-mars", request, stream=False, address='polytope.ecmwf.int')

2024-12-18 09:11:07 - INFO - Key read from /Users/cgr/.polytopeapirc
2024-12-18 09:11:07 - INFO - Sending request...
{'request': 'class: od\n'
            'date: -1\n'
            'domain: g\n'
            'expver: 1\n'
            'feature:\n'
            '  axes: step\n'
            '  points:\n'
            '  - - 38.78655345978706\n'
            '    - -9.109280931080349\n'
            '  type: timeseries\n'
            'levtype: sfc\n'
            'number: 1/to/50\n'
            'param: 164/167/169\n'
            'step: 0/to/360\n'
            'stream: enfo\n'
            "time: '0000'\n"
            'type: pf\n',
 'verb': 'retrieve'}
2024-12-18 09:11:07 - INFO - Polytope user key found in session cache for user cgr
2024-12-18 09:11:07 - INFO - Request accepted. Please poll ./96d99675-364f-45cb-b3f4-e834db746625 for status
2024-12-18 09:11:07 - INFO - Polytope user key found in session cache for user cgr
2024-12-18 09:11:07 - INFO - Checking request status (./96d99675-364f-45cb-b3f4-e834db746625)...
2024-12-18 09:11:07 - INFO - The current status of the request is 'processing'
2024-12-18 09:11:19 - INFO - The current status of the request is 'processed'

The retrieved CoverageJSON data can be converted Xarray.

da = ds.to_xarray()
da

<xarray.Dataset> Size: 176kB
Dimensions:   (x: 1, y: 1, z: 1, number: 50, datetime: 1, t: 145)
Coordinates:
  * x         (x) float64 8B 38.77
  * y         (y) float64 8B 350.9
  * z         (z) int64 8B 0
  * number    (number) int64 400B 1 2 3 4 5 6 7 8 9 ... 43 44 45 46 47 48 49 50
  * datetime  (datetime) <U20 80B '2024-12-17T00:00:00Z'
  * t         (t) datetime64[ns] 1kB 2024-12-17 ... 2025-01-01
Data variables:
    tcc       (x, y, z, number, datetime, t) float64 58kB 1.0 0.9854 ... 0.999
    2t        (x, y, z, number, datetime, t) float64 58kB 286.7 285.8 ... 286.3
    ssrd      (x, y, z, number, datetime, t) float64 58kB 0.0 0.0 ... 1.304e+08
Attributes:
    class:          od
    Forecast date:  2024-12-17T00:00:00Z
    domain:         g
    expver:         0001
    levtype:        sfc
    number:         1
    stream:         enfo
    type:           pf

xarray.Dataset

• Dimensions:
  • x: 1
  • y: 1
  • z: 1
  • number: 50
  • datetime: 1
  • t: 145
• Coordinates: (6)
  • x
    (x)
    float64
    38.77

    array([38.769771])

  • y
    (y)
    float64
    350.9

    array([350.914052])

  • z
    (z)
    int64
    0

    array([0])

  • number
    (number)
    int64
    1 2 3 4 5 6 7 ... 45 46 47 48 49 50

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
           19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])

  • datetime
    (datetime)
    <U20
    '2024-12-17T00:00:00Z'

    array(['2024-12-17T00:00:00Z'], dtype='<U20')

  • t
    (t)
    datetime64[ns]
    2024-12-17 ... 2025-01-01

    array(['2024-12-17T00:00:00.000000000', '2024-12-17T01:00:00.000000000',
           '2024-12-17T02:00:00.000000000', '2024-12-17T03:00:00.000000000',
           '2024-12-17T04:00:00.000000000', '2024-12-17T05:00:00.000000000',
           '2024-12-17T06:00:00.000000000', '2024-12-17T07:00:00.000000000',
           '2024-12-17T08:00:00.000000000', '2024-12-17T09:00:00.000000000',
           '2024-12-17T10:00:00.000000000', '2024-12-17T11:00:00.000000000',
           '2024-12-17T12:00:00.000000000', '2024-12-17T13:00:00.000000000',
           '2024-12-17T14:00:00.000000000', '2024-12-17T15:00:00.000000000',
           '2024-12-17T16:00:00.000000000', '2024-12-17T17:00:00.000000000',
           '2024-12-17T18:00:00.000000000', '2024-12-17T19:00:00.000000000',
           '2024-12-17T20:00:00.000000000', '2024-12-17T21:00:00.000000000',
           '2024-12-17T22:00:00.000000000', '2024-12-17T23:00:00.000000000',
           '2024-12-18T00:00:00.000000000', '2024-12-18T01:00:00.000000000',
           '2024-12-18T02:00:00.000000000', '2024-12-18T03:00:00.000000000',
           '2024-12-18T04:00:00.000000000', '2024-12-18T05:00:00.000000000',
           '2024-12-18T06:00:00.000000000', '2024-12-18T07:00:00.000000000',
           '2024-12-18T08:00:00.000000000', '2024-12-18T09:00:00.000000000',
           '2024-12-18T10:00:00.000000000', '2024-12-18T11:00:00.000000000',
           '2024-12-18T12:00:00.000000000', '2024-12-18T13:00:00.000000000',
           '2024-12-18T14:00:00.000000000', '2024-12-18T15:00:00.000000000',
           '2024-12-18T16:00:00.000000000', '2024-12-18T17:00:00.000000000',
           '2024-12-18T18:00:00.000000000', '2024-12-18T19:00:00.000000000',
           '2024-12-18T20:00:00.000000000', '2024-12-18T21:00:00.000000000',
           '2024-12-18T22:00:00.000000000', '2024-12-18T23:00:00.000000000',
           '2024-12-19T00:00:00.000000000', '2024-12-19T01:00:00.000000000',
           '2024-12-19T02:00:00.000000000', '2024-12-19T03:00:00.000000000',
           '2024-12-19T04:00:00.000000000', '2024-12-19T05:00:00.000000000',
           '2024-12-19T06:00:00.000000000', '2024-12-19T07:00:00.000000000',
           '2024-12-19T08:00:00.000000000', '2024-12-19T09:00:00.000000000',
           '2024-12-19T10:00:00.000000000', '2024-12-19T11:00:00.000000000',
           '2024-12-19T12:00:00.000000000', '2024-12-19T13:00:00.000000000',
           '2024-12-19T14:00:00.000000000', '2024-12-19T15:00:00.000000000',
           '2024-12-19T16:00:00.000000000', '2024-12-19T17:00:00.000000000',
           '2024-12-19T18:00:00.000000000', '2024-12-19T19:00:00.000000000',
           '2024-12-19T20:00:00.000000000', '2024-12-19T21:00:00.000000000',
           '2024-12-19T22:00:00.000000000', '2024-12-19T23:00:00.000000000',
           '2024-12-20T00:00:00.000000000', '2024-12-20T01:00:00.000000000',
           '2024-12-20T02:00:00.000000000', '2024-12-20T03:00:00.000000000',
           '2024-12-20T04:00:00.000000000', '2024-12-20T05:00:00.000000000',
           '2024-12-20T06:00:00.000000000', '2024-12-20T07:00:00.000000000',
           '2024-12-20T08:00:00.000000000', '2024-12-20T09:00:00.000000000',
           '2024-12-20T10:00:00.000000000', '2024-12-20T11:00:00.000000000',
           '2024-12-20T12:00:00.000000000', '2024-12-20T13:00:00.000000000',
           '2024-12-20T14:00:00.000000000', '2024-12-20T15:00:00.000000000',
           '2024-12-20T16:00:00.000000000', '2024-12-20T17:00:00.000000000',
           '2024-12-20T18:00:00.000000000', '2024-12-20T21:00:00.000000000',
           '2024-12-21T00:00:00.000000000', '2024-12-21T03:00:00.000000000',
           '2024-12-21T06:00:00.000000000', '2024-12-21T09:00:00.000000000',
           '2024-12-21T12:00:00.000000000', '2024-12-21T15:00:00.000000000',
           '2024-12-21T18:00:00.000000000', '2024-12-21T21:00:00.000000000',
           '2024-12-22T00:00:00.000000000', '2024-12-22T03:00:00.000000000',
           '2024-12-22T06:00:00.000000000', '2024-12-22T09:00:00.000000000',
           '2024-12-22T12:00:00.000000000', '2024-12-22T15:00:00.000000000',
           '2024-12-22T18:00:00.000000000', '2024-12-22T21:00:00.000000000',
           '2024-12-23T00:00:00.000000000', '2024-12-23T06:00:00.000000000',
           '2024-12-23T12:00:00.000000000', '2024-12-23T18:00:00.000000000',
           '2024-12-24T00:00:00.000000000', '2024-12-24T06:00:00.000000000',
           '2024-12-24T12:00:00.000000000', '2024-12-24T18:00:00.000000000',
           '2024-12-25T00:00:00.000000000', '2024-12-25T06:00:00.000000000',
           '2024-12-25T12:00:00.000000000', '2024-12-25T18:00:00.000000000',
           '2024-12-26T00:00:00.000000000', '2024-12-26T06:00:00.000000000',
           '2024-12-26T12:00:00.000000000', '2024-12-26T18:00:00.000000000',
           '2024-12-27T00:00:00.000000000', '2024-12-27T06:00:00.000000000',
           '2024-12-27T12:00:00.000000000', '2024-12-27T18:00:00.000000000',
           '2024-12-28T00:00:00.000000000', '2024-12-28T06:00:00.000000000',
           '2024-12-28T12:00:00.000000000', '2024-12-28T18:00:00.000000000',
           '2024-12-29T00:00:00.000000000', '2024-12-29T06:00:00.000000000',
           '2024-12-29T12:00:00.000000000', '2024-12-29T18:00:00.000000000',
           '2024-12-30T00:00:00.000000000', '2024-12-30T06:00:00.000000000',
           '2024-12-30T12:00:00.000000000', '2024-12-30T18:00:00.000000000',
           '2024-12-31T00:00:00.000000000', '2024-12-31T06:00:00.000000000',
           '2024-12-31T12:00:00.000000000', '2024-12-31T18:00:00.000000000',
           '2025-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (3)
  • tcc
    (x, y, z, number, datetime, t)
    float64
    1.0 0.9854 0.9939 ... 0.9796 0.999

    type :

    Parameter

    units :

    (0 - 1)

    long_name :

    tcc

    array([[[[[[1.        , 0.98535156, 0.993927  , ..., 1.        ,
                1.        , 1.        ]],
    
              [[1.        , 0.99475098, 0.91552734, ..., 0.99752808,
                0.54925537, 0.32910156]],
    
              [[1.        , 0.95419312, 0.74545288, ..., 0.59609985,
                0.99768066, 0.        ]],
    
              ...,
    
              [[1.        , 0.98416138, 0.99508667, ..., 0.97714233,
                0.99075317, 1.        ]],
    
              [[1.        , 0.98184204, 0.96191406, ..., 0.00689697,
                0.02987671, 0.43838501]],
    
              [[0.9999944 , 0.99972534, 0.9395752 , ..., 1.        ,
                0.97955322, 0.9989624 ]]]]]])

  • 2t
    (x, y, z, number, datetime, t)
    float64
    286.7 285.8 285.3 ... 288.6 286.3

    type :

    Parameter

    units :

    K

    long_name :

    2t

    array([[[[[[286.73101807, 285.76107788, 285.27377319, ...,
                285.19616699, 288.09014893, 287.5226593 ]],
    
              [[285.7322998 , 284.87342834, 284.7387085 , ...,
                289.28723145, 289.23092651, 286.56121826]],
    
              [[286.16796875, 285.56687927, 285.24578857, ...,
                282.4445343 , 287.60627747, 283.97680664]],
    
              ...,
    
              [[286.31903076, 285.7197876 , 285.59840393, ...,
                288.16993713, 289.18731689, 289.3782196 ]],
    
              [[286.3019104 , 286.04798889, 285.91436768, ...,
                287.05700684, 286.11146545, 284.87690735]],
    
              [[286.80801392, 286.38212585, 285.89639282, ...,
                285.1051178 , 288.62742615, 286.3348999 ]]]]]])

  • ssrd
    (x, y, z, number, datetime, t)
    float64
    0.0 0.0 0.0 ... 1.304e+08 1.304e+08

    type :

    Parameter

    units :

    J m**-2

    long_name :

    ssrd

    array([[[[[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
                1.25304832e+08, 1.27926272e+08, 1.27926272e+08]],
    
              [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
                1.22257408e+08, 1.27139840e+08, 1.27139840e+08]],
    
              [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
                1.31137536e+08, 1.36429568e+08, 1.36429568e+08]],
    
              ...,
    
              [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
                1.25714432e+08, 1.28974848e+08, 1.28974848e+08]],
    
              [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
                1.20848384e+08, 1.25763584e+08, 1.25763584e+08]],
    
              [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
                1.24698624e+08, 1.30367488e+08, 1.30367488e+08]]]]]])

• Indexes: (6)
  • x
    PandasIndex

    PandasIndex(Index([38.769770651632], dtype='float64', name='x'))

  • y
    PandasIndex

    PandasIndex(Index([350.914051841746], dtype='float64', name='y'))

  • z
    PandasIndex

    PandasIndex(Index([0], dtype='int64', name='z'))

  • number
    PandasIndex

    PandasIndex(Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
           19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],
          dtype='int64', name='number'))

  • datetime
    PandasIndex

    PandasIndex(Index(['2024-12-17T00:00:00Z'], dtype='object', name='datetime'))

  • t
    PandasIndex

    PandasIndex(DatetimeIndex(['2024-12-17 00:00:00', '2024-12-17 01:00:00',
                   '2024-12-17 02:00:00', '2024-12-17 03:00:00',
                   '2024-12-17 04:00:00', '2024-12-17 05:00:00',
                   '2024-12-17 06:00:00', '2024-12-17 07:00:00',
                   '2024-12-17 08:00:00', '2024-12-17 09:00:00',
                   ...
                   '2024-12-29 18:00:00', '2024-12-30 00:00:00',
                   '2024-12-30 06:00:00', '2024-12-30 12:00:00',
                   '2024-12-30 18:00:00', '2024-12-31 00:00:00',
                   '2024-12-31 06:00:00', '2024-12-31 12:00:00',
                   '2024-12-31 18:00:00', '2025-01-01 00:00:00'],
                  dtype='datetime64[ns]', name='t', length=145, freq=None))

• Attributes: (8)

  class :

  od

  Forecast date :

  2024-12-17T00:00:00Z

  domain :

  g

  expver :

  0001

  levtype :

  sfc

  number :

  1

  stream :

  enfo

  type :

  pf

We can directly plot the retrieved data as an ENS meteogram with earthkit-plots.

from earthkit.plots.interactive import Chart

def location_to_string(location):
    """
    Converts latitude and longitude to a string representation with degrees
    and N/S/E/W.
    """
    (lat, lon) = location
    lat_dir = "N" if lat >= 0 else "S"
    lon_dir = "E" if lon >= 0 else "W"
    return f"{abs(lat):.2f}°{lat_dir}, {abs(lon):.2f}°{lon_dir}"

TIME_FREQUENCY = "6h"
QUANTILES = [0, 0.1, 0.25, 0.5, 0.75, 0.9, 1]

chart = Chart()
chart.title(f"ECMWF ensemble meteogram at {location_to_string(location)}")
chart.box(ds, time_frequency=TIME_FREQUENCY, quantiles=QUANTILES)
chart.line(ds,aggregation='mean', line_color='grey', time_frequency=TIME_FREQUENCY)
chart.show(renderer="png")  # Replace with chart.show() in an interactive session!