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: 0.12.0
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

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

5408 rows × 12 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

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

1657 rows × 12 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

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

82 rows × 8 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