IPDxIRR_2F (Ionospheric plasma densities)

Abstract: Access to the derived plasma characteristics at 1Hz (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: 0.11.6
pandas     : 2.1.3
xarray     : 2023.12.0
matplotlib : 3.8.2

from viresclient import SwarmRequest
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter

request = SwarmRequest()

IPDxIRR_2F product information

Derived plasma characteristics at 1Hz, for each Swarm spacecraft.

Documentation:

• https://earth.esa.int/web/guest/missions/esa-eo-missions/swarm/data-handbook/level-2-product-definitions#IPDxIRR_2F

Check what “IPD” 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("IPD")

['Ne',
 'Te',
 'Background_Ne',
 'Foreground_Ne',
 'PCP_flag',
 'Grad_Ne_at_100km',
 'Grad_Ne_at_50km',
 'Grad_Ne_at_20km',
 'Grad_Ne_at_PCP_edge',
 'ROD',
 'RODI10s',
 'RODI20s',
 'delta_Ne10s',
 'delta_Ne20s',
 'delta_Ne40s',
 'Num_GPS_satellites',
 'mVTEC',
 'mROT',
 'mROTI10s',
 'mROTI20s',
 'IBI_flag',
 'Ionosphere_region_flag',
 'IPIR_index',
 'Ne_quality_flag',
 'TEC_STD']

Fetch three hours of IPD data

request.set_collection("SW_OPER_IPDAIRR_2F")
request.set_products(measurements=request.available_measurements("IPD"))
data = request.get_between(
    dt.datetime(2014,12,21, 0),
    dt.datetime(2014,12,21, 3)
)

Load and plot using pandas/matplotlib

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

	Grad_Ne_at_100km	mVTEC	IPIR_index	mROT	IBI_flag	ROD	Ionosphere_region_flag	mROTI10s	Longitude	mROTI20s	...	Grad_Ne_at_20km	Grad_Ne_at_PCP_edge	RODI20s	Background_Ne	Ne	TEC_STD	delta_Ne10s	RODI10s	Grad_Ne_at_50km	Ne_quality_flag
Timestamp
2014-12-21 00:00:00.196999936	-0.084919	51.786934	7	-0.011013	-1	0.0	0	0.001472	-128.771412	0.002676	...	-1.047788	0.0	7764.002532	1343599.375	1255163.2	3.131451	67.875	10238.517220	-0.403940	20000
2014-12-21 00:00:01.196999936	-0.144009	51.768982	6	-0.011013	-1	0.0	0	0.001386	-128.772618	0.002732	...	0.338403	0.0	7181.496228	1343599.375	1250357.7	3.122494	12961.600	3263.138721	0.144877	20000
2014-12-21 00:00:02.196999936	-0.058276	51.746898	6	-0.008833	-1	0.0	0	0.001310	-128.773822	0.002750	...	0.133643	0.0	7181.496228	1343599.375	1265851.3	3.113830	0.000	3263.138721	-0.123734	20000
2014-12-21 00:00:03.196999936	-0.144613	51.728759	6	-0.008151	-1	0.0	0	0.001930	-128.775026	0.003277	...	1.443077	0.0	7390.308480	1343599.375	1312436.8	3.104259	12393.550	3263.138721	-0.131441	20000
2014-12-21 00:00:04.196999936	-0.039358	51.711313	6	-0.007051	-1	0.0	0	0.002434	-128.776229	0.003744	...	-1.948789	0.0	7554.331699	1343599.375	1253999.0	3.097484	21700.700	3263.138721	-0.403369	20000

5 rows × 29 columns

df.columns

Index(['Grad_Ne_at_100km', 'mVTEC', 'IPIR_index', 'mROT', 'IBI_flag', 'ROD',
       'Ionosphere_region_flag', 'mROTI10s', 'Longitude', 'mROTI20s',
       'Num_GPS_satellites', 'Latitude', 'PCP_flag', 'Spacecraft', 'Radius',
       'Te', 'delta_Ne20s', 'Foreground_Ne', 'delta_Ne40s', 'Grad_Ne_at_20km',
       'Grad_Ne_at_PCP_edge', 'RODI20s', 'Background_Ne', 'Ne', 'TEC_STD',
       'delta_Ne10s', 'RODI10s', 'Grad_Ne_at_50km', 'Ne_quality_flag'],
      dtype='object')

fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(20,18), sharex=True)
df.plot(ax=axes[0], y=['Background_Ne', 'Foreground_Ne', 'Ne'], alpha=0.8)
df.plot(ax=axes[1], y=['Grad_Ne_at_100km', 'Grad_Ne_at_50km', 'Grad_Ne_at_20km'])
df.plot(ax=axes[2], y=['RODI10s', 'RODI20s'])
df.plot(ax=axes[3], y=['ROD'])
df.plot(ax=axes[4], y=['mROT'])
df.plot(ax=axes[5], y=['delta_Ne10s', 'delta_Ne20s', 'delta_Ne40s'])
df.plot(ax=axes[6], y=['mROTI20s', 'mROTI10s'])
axes[0].set_ylabel("[cm$^{-3}$]")
axes[1].set_ylabel("[cm$^{-3}$m$^{-1}$]")
axes[2].set_ylabel("[cm$^{-3}$s$^{-1}$]")
axes[3].set_ylabel("[cm$^{-3}$m$^{-1}$]")
axes[4].set_ylabel("[TECU s$^{-1}$]")
axes[5].set_ylabel("[cm$^{-3}$m$^{-1}$]")
axes[6].set_ylabel("[TECU s$^{-1}$]")
axes[6].set_xlabel("Timestamp")

for ax in axes:
    # Reformat time axis
    # https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/customize-dates--matplotlib-plots-python/
    ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d\n%H:%M:%S"))
    ax.legend(loc="upper right")
    ax.grid()
fig.subplots_adjust(hspace=0)

Load as xarray

ds = data.as_xarray()
ds

<xarray.Dataset>
Dimensions:                 (Timestamp: 10800)
Coordinates:
  * Timestamp               (Timestamp) datetime64[ns] 2014-12-21T00:00:00.19...
Data variables: (12/29)
    Spacecraft              (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A'
    Grad_Ne_at_100km        (Timestamp) float64 -0.08492 -0.144 ... 0.9621
    mVTEC                   (Timestamp) float64 51.79 51.77 ... 20.84 20.94
    IPIR_index              (Timestamp) int32 7 6 6 6 6 6 6 6 ... 4 4 4 4 4 4 4
    mROT                    (Timestamp) float64 -0.01101 -0.01101 ... 0.09621
    IBI_flag                (Timestamp) int32 -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1
    ...                      ...
    Ne                      (Timestamp) float64 1.255e+06 1.25e+06 ... 6.468e+05
    TEC_STD                 (Timestamp) float64 3.131 3.122 ... 2.866 2.891
    delta_Ne10s             (Timestamp) float64 67.88 1.296e+04 ... 1.702e+03
    RODI10s                 (Timestamp) float64 1.024e+04 3.263e+03 ... 503.7
    Grad_Ne_at_50km         (Timestamp) float64 -0.4039 0.1449 ... 0.9347 0.9775
    Ne_quality_flag         (Timestamp) int32 20000 20000 20000 ... 10000 10000
Attributes:
    Sources:         ['SW_OPER_IPDAIRR_2F_20141221T000000_20141221T235959_0302']
    MagneticModels:  []
    AppliedFilters:  []

xarray.Dataset

• Dimensions:
  • Timestamp: 10800
• Coordinates: (1)
  • Timestamp
    (Timestamp)
    datetime64[ns]
    2014-12-21T00:00:00.196999936 .....

    description :

    CDF_EPOCH of the measurement.

    array(['2014-12-21T00:00:00.196999936', '2014-12-21T00:00:01.196999936',
           '2014-12-21T00:00:02.196999936', ..., '2014-12-21T02:59:57.196999936',
           '2014-12-21T02:59:58.196999936', '2014-12-21T02:59:59.196999936'],
          dtype='datetime64[ns]')

• Data variables: (29)
  • 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)

  • Grad_Ne_at_100km
    (Timestamp)
    float64
    -0.08492 -0.144 ... 0.9369 0.9621

    units :

    cm^-3/m

    description :

    The electron density gradient in a running window calculated via linear regression over 27 data points for the 2 Hz electron density data.

    array([-0.08491926, -0.14400869, -0.05827591, ...,  0.91132596,
            0.93688094,  0.96208904])

  • mVTEC
    (Timestamp)
    float64
    51.79 51.77 51.75 ... 20.84 20.94

    units :

    TECU

    description :

    Median of VTEC from all available GPS satellites above 30 degrees.

    array([51.78693356, 51.76898156, 51.74689813, ..., 20.73870507,
           20.8393645 , 20.94156382])

  • IPIR_index
    (Timestamp)
    int32
    7 6 6 6 6 6 6 6 ... 4 4 4 4 4 4 4 4

    units :

    -

    description :

    The numeric index for plasma fluctuations and irregularities: 0-3 low, 4-5 medium, and > 6 high level of ionosphericplasma irregularities.

    array([7, 6, 6, ..., 4, 4, 4], dtype=int32)

  • mROT
    (Timestamp)
    float64
    -0.01101 -0.01101 ... 0.09621

    units :

    TECU/s

    description :

    Median of Rate Of change of TEC (ROT) from all available GPS satellites above 30 degrees.

    array([-0.01101323, -0.01101323, -0.00883297, ...,  0.09318886,
            0.09159881,  0.0962132 ])

  • IBI_flag
    (Timestamp)
    int32
    -1 -1 -1 -1 -1 ... -1 -1 -1 -1 -1

    units :

    -

    description :

    Plasma Bubble Index, copied from the level-2 Ionospheric Bubble Index product, IBIxTMS_2F.

    array([-1, -1, -1, ..., -1, -1, -1], dtype=int32)

  • ROD
    (Timestamp)
    float64
    0.0 0.0 0.0 ... 7.28e+03 7.28e+03

    units :

    cm^-3/s

    description :

    Rate Of change of Density

    array([   0. ,    0. ,    0. , ..., 7201.8, 7279.6, 7279.6])

  • Ionosphere_region_flag
    (Timestamp)
    int32
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    description :

    0: equator, 1: mid-latitudes; 2: auroral oval; 3: polar cap.

    array([0, 0, 0, ..., 0, 0, 0], dtype=int32)

  • mROTI10s
    (Timestamp)
    float64
    0.001472 0.001386 ... 0.005609

    units :

    TECU/s

    description :

    Median of Rate Of change of TEC Index (ROTI) from all available GPS satellites above 30 degrees. The ROTI of each satellite is the standard deviation of ROT over 10 seconds.

    array([0.0014718 , 0.00138551, 0.00131007, ..., 0.00471655, 0.00559606,
           0.00560861])

  • Longitude
    (Timestamp)
    float64
    -128.8 -128.8 ... -175.4 -175.4

    units :

    deg

    description :

    Position in ITRF - Longitude.

    array([-128.77141179, -128.77261789, -128.77382227, ..., -175.36866165,
           -175.3692556 , -175.36985325])

  • mROTI20s
    (Timestamp)
    float64
    0.002676 0.002732 ... 0.0114

    units :

    TECU/s

    description :

    Median of Rate Of change of TEC Index (ROTI) from all available GPS satellites above 30 degrees. The ROTI of each satellite is the standard deviation of ROT over 20 seconds.

    array([0.00267555, 0.00273238, 0.00274996, ..., 0.01015663, 0.01085673,
           0.01140045])

  • Num_GPS_satellites
    (Timestamp)
    int32
    4 4 4 4 4 4 4 4 ... 6 6 6 6 6 6 6 6

    units :

    -

    description :

    Total number of tracked GPS satellites above 20 degrees.

    array([4, 4, 4, ..., 6, 6, 6], dtype=int32)

  • Latitude
    (Timestamp)
    float64
    -4.694 -4.757 ... 24.74 24.68

    units :

    deg

    description :

    Position in ITRF - Latitude.

    array([-4.69353277, -4.75741624, -4.82129755, ..., 24.80658022,
           24.7426353 , 24.67869043])

  • PCP_flag
    (Timestamp)
    int32
    0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0

    units :

    -

    description :

    The polar cap patch flag: 0 if the plasma density measurement occurred OUTSIDE a polar cap patch. 1 if the plasma density measurement occurred at one of the edges of a polar cap patch (no plasma velocity measurements are available). 2 if the plasma density measurement occurred at the LEADING edge of a polar cap patch. 3 if the plasma density measurement occurred at the TRAILING edge of a polar cap patch. 4 if the plasma density measurement occurred INSIDE a polar cap patch proper. When no ion drift data is available, the leading and trailing edges cannot be distinguished. In this case the polar cap patch flag is set to 4 throughout the patch proper and to 1 throughout both edges.

    array([0, 0, 0, ..., 0, 0, 0], dtype=int32)

  • Radius
    (Timestamp)
    float64
    6.84e+06 6.84e+06 ... 6.835e+06

    units :

    m

    description :

    Position in ITRF - Radius.

    array([6840394.97965252, 6840403.88651707, 6840412.74948835, ...,
           6835430.75082371, 6835442.26034348, 6835453.76761761])

  • Te
    (Timestamp)
    float64
    2.212e+03 2.165e+03 ... 1.723e+03

    units :

    K

    description :

    Electron temperature, directly copied from the Langmuir probe files.

    array([2212.27835299, 2165.19472943, 1544.8741945 , ..., 1730.92749345,
           1727.82763148, 1722.59162917])

  • delta_Ne20s
    (Timestamp)
    float64
    1.027e+04 2.831e+03 ... 1.702e+03

    units :

    cm^-3

    description :

    Derived by subtracting Ne by its median filtered value in 20 seconds. As a result, delta_Ne20s indicates the electron density fluctuations smaller than 150 km.

    array([10266.5 ,  2830.85,     0.  , ...,  1800.45,  1819.9 ,  1701.65])

  • Foreground_Ne
    (Timestamp)
    float64
    1.305e+06 1.292e+06 ... 6.488e+05

    units :

    cm^-3

    description :

    Foreground density, as calculated from ndens using a percentile filter of 7 datapoints (2 Hz raw data) and 50 as the percentile.

    array([1305371.   , 1292046.   , 1312436.75 , ...,  635649.   ,
            643381.5  ,  648826.625])

  • delta_Ne40s
    (Timestamp)
    float64
    3.811e+03 1.634e+04 ... 1.702e+03

    units :

    cm^-3

    description :

    Derived by subtracting Ne by its median filtered value in 40 seconds. As a result, delta_Ne40s indicates the electron density fluctuations smaller than 300 km.

    array([ 3811.1 , 16343.3 ,  3246.4 , ...,  1800.45,  1819.9 ,  1701.65])

  • Grad_Ne_at_20km
    (Timestamp)
    float64
    -1.048 0.3384 ... 1.002 0.9148

    units :

    cm^-3/m

    description :

    The electron density gradient in a running window calculated via linear regression over 5 data points for the 2 Hz electron density data.

    array([-1.047788  ,  0.33840267,  0.13364267, ...,  0.91515733,
            1.00201333,  0.91482933])

  • Grad_Ne_at_PCP_edge
    (Timestamp)
    float64
    0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0

    units :

    cm^-3/m

    description :

    The linear electron density gradient calculated over the edges of a patch. This variable is non-zero only at the edges of polar cap patches.

    array([0., 0., 0., ..., 0., 0., 0.])

  • RODI20s
    (Timestamp)
    float64
    7.764e+03 7.181e+03 ... 943.2 907.9

    units :

    cm^-3/s

    description :

    Rate Of change of Density Index (RODI) is the standard deviation of ROD over 20 seconds.

    array([7764.00253246, 7181.49622793, 7181.49622793, ...,  923.42601747,
            943.21446108,  907.90485614])

  • Background_Ne
    (Timestamp)
    float64
    1.344e+06 1.344e+06 ... 4.29e+05

    units :

    cm^-3

    description :

    Background density, as calculated from Ne using a percentile filter of 551 datapoints (2 Hz raw data) and 35 as the percentile.

    array([1343599.375  , 1343599.375  , 1343599.375  , ...,  422318.09375,
            425527.09375,  429034.6875 ])

  • Ne
    (Timestamp)
    float64
    1.255e+06 1.25e+06 ... 6.468e+05

    units :

    cm^-3

    description :

    Plasma density, directly copied from the Langmuir probe files.

    array([1255163.2, 1250357.7, 1265851.3, ...,  631863.3,  639288.8,
            646784.8])

  • TEC_STD
    (Timestamp)
    float64
    3.131 3.122 3.114 ... 2.866 2.891

    units :

    TECU

    description :

    STD of VTEC from all GPS satellites.

    array([3.13145112, 3.12249362, 3.11383   , ..., 2.8407127 , 2.86645736,
           2.89068334])

  • delta_Ne10s
    (Timestamp)
    float64
    67.88 1.296e+04 ... 1.702e+03

    units :

    cm^-3

    description :

    Derived by subtracting Ne by its median filtered value in 10 seconds. As a result, delta_Ne10s indicates the electron density fluctuations smaller than 75 km.

    array([   67.875, 12961.6  ,     0.   , ...,  1800.45 ,  1819.9  ,
            1701.65 ])

  • RODI10s
    (Timestamp)
    float64
    1.024e+04 3.263e+03 ... 654.8 503.7

    units :

    cm^-3/s

    description :

    Rate Of change of Density Index (RODI) is the standard deviation of ROD over 10 seconds.

    array([10238.51721984,  3263.13872093,  3263.13872093, ...,
             744.15587296,   654.82993341,   503.71589016])

  • Grad_Ne_at_50km
    (Timestamp)
    float64
    -0.4039 0.1449 ... 0.9347 0.9775

    units :

    cm^-3/m

    description :

    The electron density gradient in a running window calculated via linear regression over 13 data points for the 2 Hz electron density data.

    array([-0.40394   ,  0.1448767 , -0.1237337 , ...,  0.89959927,
            0.93474374,  0.97747502])

  • Ne_quality_flag
    (Timestamp)
    int32
    20000 20000 20000 ... 10000 10000

    units :

    -

    description :

    Quality flag for the Ne data and the derived data from Ne, e.g., background density, foreground density etc.

    array([20000, 20000, 20000, ..., 10000, 10000, 10000], dtype=int32)

• Indexes: (1)
  • Timestamp
    PandasIndex

    PandasIndex(DatetimeIndex(['2014-12-21 00:00:00.196999936',
                   '2014-12-21 00:00:01.196999936',
                   '2014-12-21 00:00:02.196999936',
                   '2014-12-21 00:00:03.196999936',
                   '2014-12-21 00:00:04.196999936',
                   '2014-12-21 00:00:05.197007872',
                   '2014-12-21 00:00:06.196999936',
                   '2014-12-21 00:00:07.197007872',
                   '2014-12-21 00:00:08.196999936',
                   '2014-12-21 00:00:09.197007872',
                   ...
                   '2014-12-21 02:59:50.196999936',
                   '2014-12-21 02:59:51.196999936',
                   '2014-12-21 02:59:52.197007872',
                   '2014-12-21 02:59:53.196999936',
                   '2014-12-21 02:59:54.197007872',
                   '2014-12-21 02:59:55.196999936',
                   '2014-12-21 02:59:56.197007872',
                   '2014-12-21 02:59:57.196999936',
                   '2014-12-21 02:59:58.196999936',
                   '2014-12-21 02:59:59.196999936'],
                  dtype='datetime64[ns]', name='Timestamp', length=10800, freq=None))

• Attributes: (3)

  Sources :

  ['SW_OPER_IPDAIRR_2F_20141221T000000_20141221T235959_0302']

  MagneticModels :

  []

  AppliedFilters :

  []

Alternative plot setup

To plot the data from xarray, we need a different plotting setup. This does however give us more control over the plot. The units are extracted directly from the xarray object.

fig, axes = plt.subplots(nrows=7, ncols=1, figsize=(20,18), sharex=True)
def subplot(ax=None, y=None, **kwargs):
    """Plot combination of variables onto a given axis"""
    units = ds[y[0]].units
    for var in y:
        ax.plot(ds["Timestamp"], ds[var], label=var, **kwargs)
        if units != ds[var].units:
            raise ValueError(f"Units mismatch for {var}")
    ax.set_ylabel(f"[{units}]")
    # Reformat time axis
    # https://www.earthdatascience.org/courses/earth-analytics-python/use-time-series-data-in-python/customize-dates--matplotlib-plots-python/
    ax.xaxis.set_major_formatter(DateFormatter("%Y-%m-%d\n%H:%M:%S"))
    ax.legend(loc="upper right")
    ax.grid()
subplot(ax=axes[0], y=['Background_Ne', 'Foreground_Ne', 'Ne'])
subplot(ax=axes[1], y=['Grad_Ne_at_100km', 'Grad_Ne_at_50km', 'Grad_Ne_at_20km'])
subplot(ax=axes[2], y=['RODI10s', 'RODI20s'])
subplot(ax=axes[3], y=['ROD'])
subplot(ax=axes[4], y=['mROT'])
subplot(ax=axes[5], y=['delta_Ne10s', 'delta_Ne20s', 'delta_Ne40s'])
subplot(ax=axes[6], y=['mROTI20s', 'mROTI10s'])
fig.subplots_adjust(hspace=0)