TECxTMS_2F (Total electron content)

Abstract: Access to the total electric contents (level 2 product).

%load_ext watermark
%watermark -i -v -p viresclient,pandas,xarray,matplotlib

Python implementation: CPython
Python version       : 3.11.6
IPython version      : 8.18.0

viresclient: 0.14.1
pandas     : 2.1.3
xarray     : 2023.12.0
matplotlib : 3.8.2

from viresclient import SwarmRequest
import datetime as dt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

request = SwarmRequest()

TECxTMS_2F product information

Derived total electron content (TEC)

Documentation:

• https://earth.esa.int/web/guest/missions/esa-eo-missions/swarm/data-handbook/level-2-product-definitions#TECxTMS_2F

Check what “TEC” data variables are available

request.available_collections("IPD", details=False)

{'IPD': ['SW_OPER_IPDAIRR_2F', 'SW_OPER_IPDBIRR_2F', 'SW_OPER_IPDCIRR_2F']}

request.available_measurements("TEC")

['GPS_Position',
 'LEO_Position',
 'PRN',
 'L1',
 'L2',
 'P1',
 'P2',
 'S1',
 'S2',
 'Elevation_Angle',
 'Absolute_VTEC',
 'Absolute_STEC',
 'Relative_STEC',
 'Relative_STEC_RMS',
 'DCB',
 'DCB_Error']

Fetch one day of TEC data

request.set_collection("SW_OPER_TECATMS_2F")
request.set_products(measurements=request.available_measurements("TEC"))
data = request.get_between(dt.datetime(2014,1,1),
                           dt.datetime(2014,1,2))

Loading as pandas

df = data.as_dataframe()
df.head()

	PRN	S1	DCB	S2	Absolute_STEC	DCB_Error	GPS_Position	Relative_STEC	Longitude	Radius	Relative_STEC_RMS	Absolute_VTEC	L2	Spacecraft	Latitude	P1	LEO_Position	Elevation_Angle	P2	L1
Timestamp
2014-01-01 00:00:04	15	36.83	-11.446853	36.83	16.429938	0.832346	[22448765.690377887, 5421379.431197803, -13409...	24.041127	-14.122572	6.878338e+06	0.555675	10.894886	-6.261989e+06	A	-1.482419	2.182913e+07	[6668214.552999999, -1677732.678, -177943.998]	39.683333	2.182914e+07	-6.261986e+06
2014-01-01 00:00:04	18	34.75	-11.446853	34.75	16.456409	0.832346	[16113499.491062254, -16306172.004347403, -126...	22.754795	-14.122572	6.878338e+06	0.488000	10.461554	-1.466568e+07	A	-1.482419	2.135749e+07	[6668214.552999999, -1677732.678, -177943.998]	37.478137	2.135749e+07	-1.466568e+07
2014-01-01 00:00:04	22	30.90	-11.446853	30.90	20.434286	0.832346	[10823457.339250825, -24014739.352816023, -248...	15.585271	-14.122572	6.878338e+06	1.313984	9.529846	-5.455454e+06	A	-1.482419	2.277638e+07	[6668214.552999999, -1677732.678, -177943.998]	24.681787	2.277638e+07	-5.455452e+06
2014-01-01 00:00:04	24	29.87	-11.446853	29.87	20.085094	0.832346	[20631539.59055339, 13441368.439225309, 100505...	52.291341	-14.122572	6.878338e+06	0.899036	9.357587	-3.402821e+06	A	-1.482419	2.298846e+07	[6668214.552999999, -1677732.678, -177943.998]	24.647445	2.298847e+07	-3.402816e+06
2014-01-01 00:00:04	25	30.88	-11.446853	30.88	15.676947	0.832346	[16637723.905075422, -10692759.977004562, 1761...	52.672067	-14.122572	6.878338e+06	0.546729	8.597559	-2.285986e+06	A	-1.482419	2.229899e+07	[6668214.552999999, -1677732.678, -177943.998]	30.753636	2.229900e+07	-2.285981e+06

NB: The time interval is not always the same:

times = df.index
np.unique(np.sort(np.diff(times.to_pydatetime())))

array([datetime.timedelta(0), datetime.timedelta(seconds=10)],
      dtype=object)

len(df), 60*60*24

(49738, 86400)

Loading and plotting as xarray

ds = data.as_xarray()
ds

<xarray.Dataset>
Dimensions:            (Timestamp: 49738, WGS84: 3)
Coordinates:
  * Timestamp          (Timestamp) datetime64[ns] 2014-01-01T00:00:04 ... 201...
  * WGS84              (WGS84) <U1 'X' 'Y' 'Z'
Data variables: (12/20)
    Spacecraft         (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    PRN                (Timestamp) uint16 15 18 22 24 25 29 ... 7 15 16 18 21 26
    S1                 (Timestamp) float64 36.83 34.75 30.9 ... 23.03 37.73 37.5
    DCB                (Timestamp) float64 -11.45 -11.45 ... -11.45 -11.45
    S2                 (Timestamp) float64 36.83 34.75 30.9 ... 23.03 37.73 37.5
    Absolute_STEC      (Timestamp) float64 16.43 16.46 20.43 ... 10.77 10.78
    ...                 ...
    Latitude           (Timestamp) float64 -1.482 -1.482 -1.482 ... -81.7 -81.7
    P1                 (Timestamp) float64 2.183e+07 2.136e+07 ... 2.171e+07
    LEO_Position       (Timestamp, WGS84) float64 6.668e+06 ... -6.808e+06
    Elevation_Angle    (Timestamp) float64 39.68 37.48 24.68 ... 49.64 45.71
    P2                 (Timestamp) float64 2.183e+07 2.136e+07 ... 2.171e+07
    L1                 (Timestamp) float64 -6.262e+06 -1.467e+07 ... -3.41e+06
Attributes:
    Sources:         ['SW_OPER_TECATMS_2F_20140101T000000_20140101T235959_0401']
    MagneticModels:  []
    AppliedFilters:  []

xarray.Dataset

• Dimensions:
  • Timestamp: 49738
  • WGS84: 3
• Coordinates: (2)
  • Timestamp
    (Timestamp)
    datetime64[ns]
    2014-01-01T00:00:04 ... 2014-01-...

    description :

    Time stamp in UTC

    array(['2014-01-01T00:00:04.000000000', '2014-01-01T00:00:04.000000000',
           '2014-01-01T00:00:04.000000000', ..., '2014-01-01T23:59:54.000000000',
           '2014-01-01T23:59:54.000000000', '2014-01-01T23:59:54.000000000'],
          dtype='datetime64[ns]')

  • WGS84
    (WGS84)
    <U1
    'X' 'Y' 'Z'

    units :

    description :

    array(['X', 'Y', 'Z'], dtype='<U1')

• Data variables: (20)
  • Spacecraft
    (Timestamp)
    object
    'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'

    units :

    -

    description :

    Spacecraft identifier (values: 'A', 'B', 'C' or '-' if not available).

    array(['A', 'A', 'A', ..., 'A', 'A', 'A'], dtype=object)

  • PRN
    (Timestamp)
    uint16
    15 18 22 24 25 ... 15 16 18 21 26

    units :

    -

    description :

    GPS satellite PRN

    array([15, 18, 22, ..., 18, 21, 26], dtype=uint16)

  • S1
    (Timestamp)
    float64
    36.83 34.75 30.9 ... 37.73 37.5

    units :

    -

    description :

    GPS signal-to-noise ratio or raw signal strength on L1

    array([36.83, 34.75, 30.9 , ..., 23.03, 37.73, 37.5 ])

  • DCB
    (Timestamp)
    float64
    -11.45 -11.45 ... -11.45 -11.45

    units :

    TECU

    description :

    GPS receiver differential code bias

    array([-11.44685322, -11.44685322, -11.44685322, ..., -11.44685322,
           -11.44685322, -11.44685322])

  • S2
    (Timestamp)
    float64
    36.83 34.75 30.9 ... 37.73 37.5

    units :

    -

    description :

    GPS signal-to-noise ratio or raw signal strength on L2

    array([36.83, 34.75, 30.9 , ..., 23.03, 37.73, 37.5 ])

  • Absolute_STEC
    (Timestamp)
    float64
    16.43 16.46 20.43 ... 10.77 10.78

    units :

    TECU

    description :

    Absolute slant TEC

    array([16.42993842, 16.45640896, 20.43428564, ..., 15.04078951,
           10.76856032, 10.7787789 ])

  • DCB_Error
    (Timestamp)
    float64
    0.8323 0.8323 ... 0.8323 0.8323

    units :

    TECU

    description :

    Error of the GPS receiver differential code bias

    array([0.8323464, 0.8323464, 0.8323464, ..., 0.8323464, 0.8323464,
           0.8323464])

  • GPS_Position
    (Timestamp, WGS84)
    float64
    2.245e+07 5.421e+06 ... -2.111e+07

    units :

    km

    description :

    X-,Y-,Z-coordinates (WGS84) of the GPS satellite

    array([[ 22448765.69037789,   5421379.4311978 , -13409005.03373806],
           [ 16113499.49106225, -16306172.0043474 , -12696105.82349821],
           [ 10823457.33925083, -24014739.35281602,  -2489805.34342444],
           ...,
           [ 16054288.68371381, -15887374.66491947, -13277728.87342025],
           [ 14701446.22848775,  -6425961.18406759, -20621358.53418011],
           [ 10582217.79725513,  13354402.4890859 , -21105215.93033323]])

  • Relative_STEC
    (Timestamp)
    float64
    24.04 22.75 15.59 ... 16.92 26.51

    units :

    TECU

    description :

    Relative slant TEC

    array([24.04112684, 22.75479544, 15.5852713 , ..., 21.33917599,
           16.92425093, 26.51221585])

  • Longitude
    (Timestamp)
    float64
    -14.12 -14.12 ... 1.559 1.559

    units :

    degree

    description :

    Geographic longitude

    array([-14.12257161, -14.12257161, -14.12257161, ...,   1.55883202,
             1.55883202,   1.55883202])

  • Radius
    (Timestamp)
    float64
    6.878e+06 6.878e+06 ... 6.88e+06

    units :

    km

    description :

    Geographic radius

    array([6878338.19538515, 6878338.19538515, 6878338.19538515, ...,
           6879775.51647578, 6879775.51647578, 6879775.51647578])

  • Relative_STEC_RMS
    (Timestamp)
    float64
    0.5557 0.488 1.314 ... 0.6458 3.041

    units :

    TECU

    description :

    Root mean square error of relative slant TEC

    array([0.55567535, 0.48799986, 1.31398367, ..., 2.36515552, 0.64584663,
           3.04059419])

  • Absolute_VTEC
    (Timestamp)
    float64
    10.89 10.46 9.53 ... 8.365 7.912

    units :

    TECU

    description :

    Absolute vertical TEC

    array([10.89488628, 10.46155356,  9.52984558, ...,  6.4390055 ,
            8.36484899,  7.91239674])

  • L2
    (Timestamp)
    float64
    -6.262e+06 -1.467e+07 ... -3.41e+06

    units :

    m

    description :

    GPS L2 carrier phase observation

    array([ -6261988.84861192, -14665684.95888977,  -5455453.62361699, ...,
            -2412606.97951387,  -2181780.66116872,  -3409544.29364486])

  • Latitude
    (Timestamp)
    float64
    -1.482 -1.482 ... -81.7 -81.7

    units :

    degree

    description :

    Geographic latitude

    array([ -1.48241877,  -1.48241877,  -1.48241877, ..., -81.69798193,
           -81.69798193, -81.69798193])

  • P1
    (Timestamp)
    float64
    2.183e+07 2.136e+07 ... 2.171e+07

    units :

    m

    description :

    GPS P1 code phase observation

    array([21829134.2291129 , 21357488.2597339 , 22776381.83959784, ...,
           22758693.83510586, 20603857.70801323, 21709693.35444565])

  • LEO_Position
    (Timestamp, WGS84)
    float64
    6.668e+06 -1.678e+06 ... -6.808e+06

    units :

    m

    description :

    X-,Y-,Z-coordinates (WGS84) of the LEO satellite

    array([[ 6668214.553, -1677732.678,  -177943.998],
           [ 6668214.553, -1677732.678,  -177943.998],
           [ 6668214.553, -1677732.678,  -177943.998],
           ...,
           [  993010.408,    27023.255, -6807680.312],
           [  993010.408,    27023.255, -6807680.312],
           [  993010.408,    27023.255, -6807680.312]])

  • Elevation_Angle
    (Timestamp)
    float64
    39.68 37.48 24.68 ... 49.64 45.71

    units :

    degree

    description :

    Elevation Angle

    array([39.68333303, 37.47813725, 24.68178657, ..., 21.87866375,
           49.63566339, 45.70959615])

  • P2
    (Timestamp)
    float64
    2.183e+07 2.136e+07 ... 2.171e+07

    units :

    m

    description :

    GPS P2 code phase observation

    array([21829136.64816922, 21357490.77084316, 22776383.39149728, ...,
           22758695.72573735, 20603859.47857912, 21709696.16704626])

  • L1
    (Timestamp)
    float64
    -6.262e+06 -1.467e+07 ... -3.41e+06

    units :

    m

    description :

    GPS L1 carrier phase observation

    array([ -6261986.32318884, -14665682.56859061,  -5455451.98644732, ...,
            -2412604.73791979,  -2181778.88334466,  -3409541.50864388])

• Indexes: (2)
  • Timestamp
    PandasIndex

    PandasIndex(DatetimeIndex(['2014-01-01 00:00:04', '2014-01-01 00:00:04',
                   '2014-01-01 00:00:04', '2014-01-01 00:00:04',
                   '2014-01-01 00:00:04', '2014-01-01 00:00:04',
                   '2014-01-01 00:00:14', '2014-01-01 00:00:14',
                   '2014-01-01 00:00:14', '2014-01-01 00:00:14',
                   ...
                   '2014-01-01 23:59:44', '2014-01-01 23:59:44',
                   '2014-01-01 23:59:54', '2014-01-01 23:59:54',
                   '2014-01-01 23:59:54', '2014-01-01 23:59:54',
                   '2014-01-01 23:59:54', '2014-01-01 23:59:54',
                   '2014-01-01 23:59:54', '2014-01-01 23:59:54'],
                  dtype='datetime64[ns]', name='Timestamp', length=49738, freq=None))

  • WGS84
    PandasIndex

    PandasIndex(Index(['X', 'Y', 'Z'], dtype='object', name='WGS84'))

• Attributes: (3)

  Sources :

  ['SW_OPER_TECATMS_2F_20140101T000000_20140101T235959_0401']

  MagneticModels :

  []

  AppliedFilters :

  []

fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(15,5), sharex=True)
ds["Absolute_VTEC"].plot.line(x="Timestamp", ax=axes[0])
ds["Absolute_STEC"].plot.line(x="Timestamp", ax=axes[1]);
fig.subplots_adjust(hspace=0)