Conjunctions (TOLEOS)

Conjunctions (TOLEOS)#

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

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

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

viresclient: 0.12.3
pandas     : 2.1.3
xarray     : 2023.12.0
matplotlib : 3.8.2
cartopy    : 0.22.0

import datetime as dt

import matplotlib.pyplot as plt
import numpy as np
from viresclient import SwarmRequest

Product information#

The MM_OPER_CON_EPH_2_ product contains conjunction information between Swarm (A, B, C), CHAMP, GOCE, GRACE (1, 2), and GRACE-FO (1, 2).

The product is implemented in VirES as two collections, each available as a single flat time series.

MM_OPER_CON_EPH_2_:crossover contains the list of times where satellite ground-tracks overlap within a ~7 hour window.

MM_OPER_CON_EPH_2_:plane_alignment contains much rarer events, where the planes of different spacecraft are aligned

request = SwarmRequest(SERVER_URL)
for collection in ("MM_OPER_CON_EPH_2_:crossover", "MM_OPER_CON_EPH_2_:plane_alignment"):
    print(f"{collection}:\n{request.available_measurements(collection)}\n")

MM_OPER_CON_EPH_2_:crossover:
['time_1', 'time_2', 'time_difference', 'satellite_1', 'satellite_2', 'latitude', 'longitude', 'altitude_1', 'altitude_2', 'magnetic_latitude', 'magnetic_longitude', 'local_solar_time_1', 'local_solar_time_2']

MM_OPER_CON_EPH_2_:plane_alignment:
['time', 'altitude_1', 'altitude_2', 'ltan_1', 'ltan_2', 'ltan_rate_1', 'ltan_rate_2', 'satellite_1', 'satellite_2']

Fetching data#

Crossovers#

Let’s fetch all the available conjunctions for a given day.

Note that the start_time and end_time specified are used for a full interval query over both time_1 and time_2 given in the outputs.

request = SwarmRequest(SERVER_URL)
request.set_collection("MM_OPER_CON_EPH_2_:crossover")
request.set_products(
    request.available_measurements("MM_OPER_CON_EPH_2_:crossover")
)
data = request.get_between(
    dt.datetime(2020, 1, 1),
    dt.datetime(2020, 1, 2),
)
df = data.as_dataframe()
df

	Spacecraft	satellite_2	satellite_1	local_solar_time_2	latitude	longitude	altitude_1	time_difference	magnetic_latitude	magnetic_longitude	time_2	altitude_2	local_solar_time_1
time_1
2019-12-31 16:45:34.507031296	-	SWC	GF1	5.517517	87.321728	-168.631028	508386.037694	26266.358365	79.949318	-164.456295	2020-01-01 00:03:20.865398528	445802.184339	12.813727
2019-12-31 16:45:34.666273280	-	SWA	GF1	5.523574	87.330964	-168.540829	508384.174138	26275.311505	79.956628	-164.497178	2020-01-01 00:03:29.977781248	445817.275119	12.822272
2019-12-31 16:45:58.498234368	-	SWC	GF2	5.518642	87.322763	-168.714116	508285.501449	26242.302943	79.946058	-164.469981	2020-01-01 00:03:20.801171712	445802.184832	12.808170
2019-12-31 16:45:58.642820352	-	SWA	GF2	5.523248	87.331274	-168.645621	508283.850034	26251.259161	79.952162	-164.509020	2020-01-01 00:03:29.901976576	445817.254541	12.815265
2019-12-31 17:32:44.852499968	-	SWC	GF1	17.518367	-87.324172	-0.411375	522002.918063	26235.665210	-79.455668	14.152966	2020-01-01 00:50:00.517710848	464173.845073	0.806052
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-01-01 23:42:29.863429888	-	GF1	SWB	2.264293	81.399341	38.339964	511607.216872	7630.688001	75.124965	146.831766	2020-01-02 01:49:40.551429632	508318.284479	4.383929
2020-01-01 23:43:26.628039168	-	SWA	SWB	2.561690	77.902092	42.564400	511293.312109	15391.897473	71.743591	142.500846	2020-01-02 03:59:58.525515520	445127.722214	6.837217
2020-01-01 23:43:39.701632768	-	SWC	SWB	2.607960	77.089844	43.203973	511208.234498	15357.778578	70.954621	141.708566	2020-01-02 03:59:37.480210944	445042.331540	6.874009
2020-01-01 23:59:39.406749952	-	SWC	GF2	3.974184	21.148917	59.698567	507422.300094	7926.374931	14.544932	134.574062	2020-01-02 02:11:45.781679616	435473.994296	6.175955
2020-01-01 23:59:59.915086080	-	SWC	GF1	3.977859	23.980608	59.668240	507614.183522	7949.709113	17.345313	134.913629	2020-01-02 02:12:29.624203264	435703.475318	6.186112

5408 rows × 13 columns

Pairs of conjunctioning spacecraft are defined with short designations in the satellite_1 and satellite_2 variables:

df["satellite_1"].unique()

array(['GF1', 'GF2', 'SWB', 'SWC', 'SWA'], dtype=object)

df["satellite_2"].unique()

array(['SWC', 'SWA', 'SWB', 'GF2', 'GF1'], dtype=object)

Each conjunction has a start and end time defined with the time_1 and time_2 variables:

df.iloc[0:5][["time_2", "satellite_1", "satellite_2"]]

	time_2	satellite_1	satellite_2
time_1
2019-12-31 16:45:34.507031296	2020-01-01 00:03:20.865398528	GF1	SWC
2019-12-31 16:45:34.666273280	2020-01-01 00:03:29.977781248	GF1	SWA
2019-12-31 16:45:58.498234368	2020-01-01 00:03:20.801171712	GF2	SWC
2019-12-31 16:45:58.642820352	2020-01-01 00:03:29.901976576	GF2	SWA
2019-12-31 17:32:44.852499968	2020-01-01 00:50:00.517710848	GF1	SWC

We can select all the conjunctions containing a given satellite:

df_SWA = df.where((df["satellite_1"] == "SWA")|(df["satellite_2"] == "SWA")).dropna()
df_SWA

	Spacecraft	satellite_2	satellite_1	local_solar_time_2	latitude	longitude	altitude_1	time_difference	magnetic_latitude	magnetic_longitude	time_2	altitude_2	local_solar_time_1
time_1
2019-12-31 16:45:34.666273280	-	SWA	GF1	5.523574	87.330964	-168.540829	508384.174138	26275.311505	79.956628	-164.497178	2020-01-01 00:03:29.977781248	445817.275119	12.822272
2019-12-31 16:45:58.642820352	-	SWA	GF2	5.523248	87.331274	-168.645621	508283.850034	26251.259161	79.952162	-164.509020	2020-01-01 00:03:29.901976576	445817.254541	12.815265
2019-12-31 17:32:44.934890496	-	SWA	GF1	17.521446	-87.328937	-0.365544	522004.159533	26244.692912	-79.460015	14.136825	2020-01-01 00:50:09.627804672	464161.845406	0.811638
2019-12-31 17:33:08.975226624	-	SWA	GF2	17.519267	-87.329375	-0.498389	522106.188666	26220.555867	-79.454945	14.116901	2020-01-01 00:50:09.531093760	464161.901037	0.802755
2019-12-31 17:42:18.655843840	-	SWA	SWB	1.733630	84.623616	120.426718	511774.213962	22790.428566	75.269785	-175.176848	2020-01-01 00:02:09.084413952	445697.680298	8.064305
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-01-01 23:31:37.199038976	-	SWA	GF2	3.136647	-85.252281	54.144711	524464.926293	2350.649018	-82.426605	30.386096	2020-01-02 00:10:47.848054528	463967.092769	3.789605
2020-01-01 23:36:50.826312448	-	SWA	SWB	15.897388	76.710613	-115.750956	511401.685124	4977.687203	80.903810	-87.255342	2020-01-02 00:59:48.513515776	445355.973591	17.280079
2020-01-01 23:41:14.676492288	-	SWA	SWB	1.111731	85.793936	21.364816	511872.512023	26854.660897	79.420521	156.361396	2020-01-02 07:08:49.337390592	445591.642678	8.571359
2020-01-01 23:41:47.813570304	-	SWA	SWB	1.824534	83.922645	31.918781	511777.132307	21188.410732	77.564465	151.398185	2020-01-02 05:34:56.224296704	445578.775959	7.710203
2020-01-01 23:43:26.628039168	-	SWA	SWB	2.561690	77.902092	42.564400	511293.312109	15391.897473	71.743591	142.500846	2020-01-02 03:59:58.525515520	445127.722214	6.837217

1657 rows × 13 columns

Plane alignments#

request = SwarmRequest(SERVER_URL)
request.set_collection("MM_OPER_CON_EPH_2_:plane_alignment")
request.set_products(
    request.available_measurements("MM_OPER_CON_EPH_2_:plane_alignment")
)
data = request.get_between(
    dt.datetime(2000, 1, 1),
    dt.datetime(2022, 1, 1),
)
df = data.as_dataframe()
df

	Spacecraft	satellite_2	ltan_2	satellite_1	ltan_rate_1	ltan_rate_2	ltan_1	altitude_1	altitude_2
time
2003-05-11 10:10:03.145992192	-	GR2	4.684153	CH	-0.091268	-0.074543	16.684153	400547.265815	488291.762308
2003-05-11 10:21:36.896242176	-	GR1	4.683406	CH	-0.091268	-0.074549	16.683406	400527.975912	487617.059444
2005-04-08 16:07:15.667320192	-	GR2	0.629215	CH	-0.091834	-0.074332	0.629215	361914.544690	469993.893995
2005-04-08 16:12:54.103234304	-	GR1	0.628855	CH	-0.091834	-0.074331	0.628855	361913.083670	470666.606845
2007-02-14 05:07:14.704273408	-	GR2	22.255276	CH	-0.092207	-0.074526	10.255276	350444.851771	481552.511743
...	...	...	...	...	...	...	...	...	...
2020-05-21 19:46:01.686648320	-	GF2	17.434235	SWC	-0.089979	-0.074638	17.434235	436300.307971	485113.048217
2020-05-21 20:03:47.512773632	-	GF1	17.433122	SWC	-0.089979	-0.074633	17.433122	436330.337154	485000.585092
2021-09-30 18:39:55.482117120	-	SWC	20.642289	SWA	-0.089902	-0.090034	20.642289	430877.890053	430878.908792
2021-10-03 13:41:11.625304576	-	SWC	20.390555	SWB	-0.085605	-0.090089	8.390555	503359.670523	431177.812782
2021-10-03 15:03:35.963726592	-	SWB	8.385697	SWA	-0.089954	-0.085605	20.385697	431054.450419	503289.814384

82 rows × 9 columns

def alignments(df, sat="SWA"):
    return df.where((df["satellite_1"] == sat)|(df["satellite_2"] == sat)).dropna()

sats = ('CH', 'GO', 'GR1', 'GR2', 'GF1', 'GF2', 'SWA', 'SWB', 'SWC')
fig, axes = plt.subplots(len(sats), 1, figsize=(10, 5), sharex=True)
empty = np.empty(df.index.shape)
empty[:] = np.nan
axes[0].plot(df.index, empty)
for sat, ax in zip(sats, axes):
    _df = alignments(df, sat=sat)
    for date in _df.index:
        ax.axvline(date)
    ax.set_yticks([])
    ax.set_ylabel(sat)
fig.subplots_adjust(hspace=0)
fig.suptitle("Plane alignments");

../_images/bb2021821a89a94b1634bae7fff572115f31c3d6fe92d3320db127854bf75385.png