Coordinates and Dynamic Pressure of High-Speed Magnetosheath Jets Measured by THEMIS Satellites, 2012-2018

This dataset adds satellite parameters and dynamic pressure calculations to the event list at https://osf.io/7rjs4/

You can reference this data set as follows: Plaschke, F., Hietala, H., & LaMoury, A. T. (2020, October 27). THEMIS magnetosheath jet data set 2012-2018. Retrieved from osf.io/7rjs4.

The description of the identification processes of the THEMIS data sets is published in: Plaschke, F., Hietala, H., and Angelopoulos, V.: Anti-sunward high-speed jets in the subsolar magnetosheath, Ann. Geophys., 31, 1877–1889, https://doi.org/10.5194/angeo-31-1877-2013, 2013.

The original THEMIS list should be downloaded locally from the link above before running the python code in the Jupyter notebook. The columns contain:

   column 1: jet number

   column 2: observing spacecraft (A: THEMIS-A, ..., E: THEMIS-E)

   column 3: start of identified jet interval in UT

   column 4: time of maximum dynamic pressure ratio in UT

   column 5: end of identified jet interval in UT

In the final version of the list, the column is indexed by "Max" (col 4 in original), and the other columns are named 'Jet Number', 'Ref Spacecraft', 'Start', and 'End'.

From CDAWeb, we add the following columns: 

   SM_LAT, SM_LON: Latitude and longitude in GSM coordinates

   SM_X, SM_Y, SM_Z: Cartesian position in GSM coordinates

   GEO_X_1, GEO_Y_1, GEO_Z_1: Cartesian position in geographic coordinates

   DIST_FROM_P93_BOW_SHOCK: Distance from the P93 Bow Shock

   DIST_FROM_MAGNETOPAUSE: Distance from the RS93 Magnetopause

   DIST_FROM_T95_NS: Distance to the Tsyganenko 1995 model Neutral Sheet

   L_VALUE: Dipole L value

   INVAR_LAT: Dipole Invariant Latitude

   MAGNETIC_STRENGTH: Magnetic Field Strength

   Dynamic Pressure (nPa): Peak dynamic pressure of jet 

   Electron density: 

   Vx, Vy, Vz: Cartesian coordinates of ion velocity. Used in computing dynamic pressure

These are pulled from orbit parameters and on-board moment data, using ai.cdas and the second using pyspedas respectively.