EFIxTIE (Ion temperatures)

Contents

EFIxTIE (Ion temperatures)#

Abstract: Access to the EFIxTIE data product containing estimates of the O+ ion temperature in the upper F region along Swarm orbits.

For more information about this product, see the SITE project.

SERVER_URL = 'https://vires.services/ows'

# Display important package versions used
%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: 0.12.0
pandas     : 2.1.3
xarray     : 2023.12.0
matplotlib : 3.8.2

import datetime as dt
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
# Control the HTML display of the datasets
xr.set_options(display_expand_attrs=False, display_expand_coords=True, display_expand_data=True)

from viresclient import SwarmRequest

request = SwarmRequest(SERVER_URL)

What data is available?#

There are three collections available, one for each Swarm spacecraft.

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

{'EFI_TIE': ['SW_OPER_EFIATIE_2_', 'SW_OPER_EFIBTIE_2_', 'SW_OPER_EFICTIE_2_']}

print(request.available_measurements("EFI_TIE"))

['Latitude_GD', 'Longitude_GD', 'Height_GD', 'Radius_GC', 'Latitude_QD', 'MLT_QD', 'Tn_msis', 'Te_adj_LP', 'Ti_meas_drift', 'Ti_model_drift', 'Flag_ti_meas', 'Flag_ti_model']

Notes for the listed available variables:

tie_vars = [
    # Positional information in geodetic (GD) and geocentric (GC) frames
    #  redundant with VirES variables Latitude, Longitude, Radius (in geocentric frame)
    "Latitude_GD", "Longitude_GD", "Height_GD", "Radius_GC",
    # Quasi-dipole magnetic latitude and local time
    #  redundant with VirES auxiliaries, QDLat & MLT
    "Latitude_QD", "MLT_QD",
    # Neutral temperature from NRLMSISE-00 model
    "Tn_msis",
    # Corrected Swarm LP electron temperature
    "Te_adj_LP",
    # Estimated ion temperature from TII drift at high latitudes
    "Ti_meas_drift",
    # Estimated Ion temperature from Weimer 2005 model drifts at high latitudes
    "Ti_model_drift",
    # Bitwise flags with process information
    #  See the Product definition document for details
    "Flag_ti_meas", "Flag_ti_model"
]

Fetching and plotting data#

Hint: you can identify start/end times of specific orbit numbers using .get_times_for_orbits:

request = SwarmRequest(SERVER_URL)
request.get_times_for_orbits(3740, 3742, mission="Swarm", spacecraft="Alpha")

(datetime.datetime(2014, 7, 25, 1, 9, 37, 149985),
 datetime.datetime(2014, 7, 25, 5, 51, 25, 595024))

For demonstration, we will fetch some data from Swarm Alpha (SW_OPER_EFIATIE_2_). We also ask for magnetic local time, MLT, and orbital information: OrbitNumber, OrbitDirection which can be useful for some plots.

start = dt.datetime(2014, 7, 25, 1, 9, 38)
end = dt.datetime(2014, 7, 25, 5, 51, 25)

request = SwarmRequest(SERVER_URL)
request.set_collection("SW_OPER_EFIATIE_2_")
request.set_products(
    measurements=tie_vars,
    auxiliaries=["OrbitNumber", "OrbitDirection", "MLT"]
)
data = request.get_between(start, end)

Data can be loaded as either a pandas datframe or a xarray dataset.

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

	Longitude_GD	Latitude_QD	Ti_model_drift	Flag_ti_model	Flag_ti_meas	Height_GD	OrbitNumber	Radius	OrbitDirection	Spacecraft	Radius_GC	MLT	Latitude	Longitude	MLT_QD	Ti_meas_drift	Latitude_GD	Tn_msis	Te_adj_LP
Timestamp
2014-07-25 01:09:38.196999936	-126.081803	4.363832	1308.57	3	3	467186.573954	3740	6.845324e+06	1	A	6.845324e+06	16.776039	-0.012119	-126.081803	16.734770	1308.57	-0.012195	1048.83	1486.15
2014-07-25 01:09:38.696000000	-126.082416	4.395916	1308.26	3	3	467180.926784	3740	6.845318e+06	1	A	6.845318e+06	16.775776	0.019726	-126.082416	16.734495	1308.26	0.019850	1048.90	1485.50
2014-07-25 01:09:39.196992256	-126.083031	4.428128	1308.20	3	3	467175.265855	3740	6.845312e+06	1	A	6.845312e+06	16.775513	0.051698	-126.083031	16.734220	1308.20	0.052023	1048.98	1484.76
2014-07-25 01:09:39.696000000	-126.083644	4.460213	1308.21	3	3	467169.641168	3740	6.845307e+06	1	A	6.845307e+06	16.775248	0.083543	-126.083644	16.733945	1308.21	0.084067	1049.05	1484.88
2014-07-25 01:09:40.196999936	-126.084259	4.492427	1309.25	3	3	467164.002862	3740	6.845301e+06	1	A	6.845301e+06	16.774982	0.115515	-126.084259	16.733669	1309.25	0.116240	1049.14	1486.79

# Set up a time series plot where each row is an orbit
nrows = len(np.unique(ds["OrbitNumber"]))
fig, axes = plt.subplots(nrows=nrows, ncols=1, figsize=(10, 7))
axes_r = [ax.twinx() for ax in axes]
for i, (orbitnumber, ds_orbit) in enumerate(ds.groupby("OrbitNumber")):
    # Plot electron & ion temperatures, measured and modelled
    ds_orbit.plot.scatter(x="Timestamp", y="Ti_meas_drift", s=0.1, ax=axes[i], color="tab:red")
    ds_orbit.plot.scatter(x="Timestamp", y="Te_adj_LP", s=0.1, ax=axes[i], color="tab:purple")
    ds_orbit.plot.scatter(x="Timestamp", y="Tn_msis", s=0.1, ax=axes[i], color="tab:green")
    ds_orbit.plot.scatter(x="Timestamp", y="MLT", s=0.05, ax=axes_r[i], color="tab:gray", alpha=0.5)
    # Identify times closest to North and South pole
    t_NP = ds_orbit["Timestamp"].isel(Timestamp=ds_orbit["Latitude"].argmax()).values
    t_SP = ds_orbit["Timestamp"].isel(Timestamp=ds_orbit["Latitude"].argmin()).values
    axes[i].axvline(mpl.dates.date2num(t_NP), color="gray")
    axes[i].axvline(mpl.dates.date2num(t_SP), color="gray")
    # Tidy up labelling
    axes[i].xaxis.set_major_formatter(mpl.dates.DateFormatter("%H:%M"))
    axes[i].set_xlabel("")
# Add legend manually
red = mpl.patches.Patch(color="tab:red", label="Ti_meas_drift")
purple = mpl.patches.Patch(color="tab:purple", label="Te_adj_LP")
green = mpl.patches.Patch(color="tab:green", label="Tn_msis")
gray = mpl.patches.Patch(color="tab:gray", label="MLT / Poles")
axes[0].legend(handles=[red, purple, green, gray])
# Tidy up axes and labelling
for ax in axes:
    ax.set_ylim(500, 4000)
axes[-1].set_xlabel("Time")
title = "".join([
    f"Swarm {ds['Spacecraft'].data[0]} ion temperatures, ",
    ds["Timestamp"].dt.date.data[0].isoformat(),
    f"\n{[s for s in ds.attrs['Sources'] if 'TIE' in s]}"
])
fig.suptitle(title);

../_images/5cff392e3358df542360ab3be3e1775b348b9fafea6ed7f16397a248dd5a7b41.png