Work Description

Title: Dynamical Heating in the Martian Thermosphere: Temperatures, Winds and Thermal Balances using M-GITM Open Access Deposited

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Methodology
  • A Global Climate Model is utilized to study the Martian thermosphere and interpret the MAVEN spacecraft datasets obtained from this region of the upper atmosphere. Simulated temperatures, neutral densities (CO2, CO, N2, O, Ar, O2, N, NO, including helium) and corresponding zonal, meridional and vertical neutral winds of the Mars thermosphere are calculated by the 3-D numerical model called M-GITM (Mars Global Ionosphere-Thermosphere Model), developed at the U. of Michigan. This is a climate model whose domain extends from the surface to ~250 km (Bougher et al., 2015). Neutral plus ion densities, neutral temperatures and winds are calculated on a 3-D grid (latitude, longitude, altitude) for NASA MAVEN spacecraft conditions corresponding to specific instrument measurements made. These three-dimensional (3-D) model calculations are time-marching, making use of a finite-difference code which solves the Navier-Stokes equations for temperatures, composition, and winds. Large 3-D datacubes are created of M-GITM outputs fields for deposit on this archive.
Description
  • The NASA MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft, which is currently in orbit around Mars, has been taking systematic measurements of the densities and deriving temperatures in the upper atmosphere of Mars (between about 140 to 240 km above the surface) since late 2014. Wind measurement campaigns have also been conducted once per month for 5-10 orbits since 2016. These densities, temperatures and winds change with time (e.g. solar cycle, season, local time) and location, and sometimes fluctuate quickly. Global dust storm events are also known to significantly impact these density, temperature and wind fields in the Mars thermosphere. For the current project, in-situ measured winds and corresponding argon density derived temperatures are combined to trace the circulation patterns and investigate their convergence and divergence locations and impacts throughout the Mars thermosphere. M-GITM computed thermal balance terms are subsequently extracted to investigate the processes required to maintain the temperature distribution around the planet. For this work, Mars Year #33 (MY33) Neutral Gas and Ion Mass Spectrometer (NGIMS) measurements have been obtained by the MAVEN team for this purpose (see these representative works: (Bougher et al., 2017; Stone et al., 2018; Benna et al., 2019). These temperature and wind fields are compared to simulations from a computer model of the Mars atmosphere called M-GITM (Mars Global Ionosphere-Thermosphere Model), developed at U. of Michigan. Since the global circulation plays a role in the structure, variability, and evolution of the atmosphere, understanding the processes that drive the winds in the upper atmosphere of Mars also provides the needed context for understanding temperature distributions and underlying thermal balances throughout the atmosphere. Three dimensional M-GITM simulations for three of the four Mars cardinal seasons (Ls = 0, 90, 270) for MY33 were conducted for detailed comparisons with NGIMS temperature and wind distributions (Pilinski et al. 2022). Corresponding M-GITM datacubes used to extract these temperatures (plus winds) along the trajectory of each orbit path between 140 and 240 km, are provided in this Deep Blue Data archive. A single README file is included that details the contents of each datacube file. In addition, this general README file summarizes the inputs and outputs of each M-GITM simulation interval used for this study. Finally, a basic version of the M-GITM code can be found on Github at https:/github.com/dpawlows/MGITM.
Creator
Depositor
  • bougher@umich.edu
Contact information
Discipline
Funding agency
  • National Aeronautics and Space Administration (NASA)
Keyword
Citations to related material
  • Pilinski, M. D., K. J. Roeten, S. W. Bougher and M. Benna, Dynamical Heating in the Martian Thermosphere, Journal Geophysical Res., XXX, (forthcoming - 2022). doi: .....
Resource type
Last modified
  • 11/26/2022
Published
  • 08/31/2022
Language
DOI
  • https://doi.org/10.7302/t6gg-3t89
License
To Cite this Work:
Bougher, S. W., Pilinski, M. D. (2022). Dynamical Heating in the Martian Thermosphere: Temperatures, Winds and Thermal Balances using M-GITM [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/t6gg-3t89

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Files (Count: 7; Size: 179 MB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
README.txt 2022-08-31 2022-08-31 7.08 KB Open Access
MY33.LS0.PILINSKI.150619.UT13.us...c.dat 2022-08-30 2022-08-30 35.3 MB Open Access
MY33.LS0.PILINSKI.150619.UT13.HT...c.dat 2022-08-30 2022-08-30 18.9 MB Open Access
MY33.LS0.YSGW4.PILINSKI.150619.U...c.dat 2022-08-31 2022-08-31 35.3 MB Open Access
MY33.LS0.YSGW4.PILINSKI.150619.U...c.dat 2022-08-31 2022-08-31 18.9 MB Open Access
MY33.LS90.PILINSKI.160104.UT21.u...c.dat 2022-08-31 2022-08-31 35.3 MB Open Access
MY33.LS270.PILINSKI.161129.UT17....c.dat 2022-08-31 2022-08-31 35.3 MB Open Access

***************************************************************************************

Date: 30-August-2022
Who: S. W. Bougher
Dynamical Heating of the Martian Thermosphere

*****************************************************************************************

Description:

The NASA MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft, which is currently in orbit around Mars, has been taking systematic
measurements of the densities and deriving temperatures in the upper atmosphere of Mars (between about 140 to 240 km above the surface) since late 2014.
Wind measurement campaigns have also been conducted once per month for 5-10 orbits since 2016. These densities, temperatures and winds change with time
(e.g. solar cycle, season, local time) and location, and sometimes fluctuate quickly. Global dust storm events are also known to significantly impact
these density, temperature and wind fields in the Mars thermosphere.

For the current project, in-situ measured winds and corresponding argon density derived temperatures are combined to trace the circulation patterns and
investigate their convergence and divergence locations and impacts throughout the Mars thermosphere. M-GITM computed thermal balance terms are subsequently
extracted to investigate the processes required to maintain the temperature distribution around the planet. For this work, Mars Year #33 (MY33)
Neutral Gas and Ion Mass Spectrometer (NGIMS) measurements have been obtained by the MAVEN team for this purpose (see these representative works:
(Bougher et al., 2017; Stone et al., 2018; Benna et al., 2019). These temperature and wind fields are compared to simulations from a computer
model of the Mars atmosphere called M-GITM (Mars Global Ionosphere-Thermosphere Model), developed at U. of Michigan. Since the global circulation
plays a role in the structure, variability, and evolution of the atmosphere, understanding the processes that drive the winds in the upper atmosphere
of Mars also provides the needed context for understanding temperature distributions and underlying thermal balances throughout the atmosphere. Three dimensional
M-GITM simulations for three of the four Mars cardinal seasons (Ls = 0, 90, 270) for MY33 were conducted for detailed comparisons with NGIMS temperature
and wind distributions (Pilinski et al. 2022). Corresponding M-GITM datacubes used to extract these temperatures (plus winds) along the trajectory of each orbit
path between 140 and 240 km, are provided in this Deep Blue Data archive. A single README file is included that details the contents of each datacube file.
In addition, this general README file summarizes the inputs and outputs of each M-GITM simulation interval used for this study. Finally, a basic
version of the M-GITM code can be found on Github at https:/github.com/dpawlows/MGITM.


*****************************************************************************************

Four MY33 Solar Cycle and Seasonal conditions for M-GITM simulations of this project (Pilinski et al., 2022)
--------------------------------------------------------------------------------------------
Interval Date Range Season (Ls) Narrative
--------------------------------------------------------------------------------------------
1 19-20 June, 2015 0.0 MY33 Equinox sampling period (no gravity wave formulation activated)
2 19-20 June, 2015 0.0 MY33 Equinox sampling period (standard gravity wave formulation fully activated)
3 4-5 January, 2016 90.0 MY33 Aphelion sampling period (no gravity wave formulation activated)
4 29-30 November, 2016 270.0 MY33 Perihelion sampling period (no gravity wave formulation activated)

*****************************************************************************************

MGITM datacubes are presented utilizing a GEO grid (Longitude-Latitude vs Altitude):
-----------------------------------------------------------------------------------
Local Solar Time (LST) is fixed in longitude for these datacubes, giving LST = 12.0 at LON = 0.0

3-D Grid Domain:
LONGITUDE (LON): 2.5 to 357.5, by 5.0 degrees
LATITUDE (LAT): -87.5 to +87.5, by 5.0 degrees
ALTITUDE (ALT): 98.75 to 293.75. by 2.5 km

1. Fields (12): State Fields
-- Temperatures (neutral) : Tn
-- Major neutral densites : [CO2], [O], [N2], [CO], [He], [Ar]
-- Neuyral winds : U-zonal, V-meridional, W-vertical
-- Pressure : Press
-- Solar Zenith Angle : SZA
-- Major plasma densities : none here

** Units = Temperatures (K), All neutral densities (#/m3), All Neutral winds (m/s), Pressure (Pascals), SZA (degrees)

2. Fields (5): Thermal Balances (HTBAL)
-- EUV Heating : QEUV
-- Near IR Heating : QNIR
-- CO2 15-micron cooling : Q15
-- Molecular Conduction : QCOND
-- Net Dynamical heating : QTDYN

** Units: ALL K/day

6-Files in the respository for downloading:
-----------------------------------------------------------------------------------
README.txt

Bundled Orbital File Batches:
------------------------------
Interval 1: MY33.LS0.PILINSKI.150619.UT13.userdetic.dat
MY33.LS0.PILINSKI.150619.UT13.HTBAL.userdetic.dat

Interval 2: MY33.LS0.YSGW.PILINSKI.150619.UT13.userdetic.dat
MY33.LS0.YSGW.PILINSKI.150619.UT13.HTBAL.userdetic.dat

Interval 3: MY33.LS90.PILINSKI.150619.UT13.userdetic.dat

Interval 4: MY33.LS270.PILINSKI.150619.UT13.userdetic.dat

Inputs:
-------

** FISM-Mars daily averaged solar EUV-UV fluxes (1-195 nm) used based upon MAVEN
Extreme Ultraviolet Monitor (EUVM) instrument: Thiemann et al. (2017).
Level 3 EUVM daily products used: v14_r03 (all intervals)

** Standard gravity momentum and energy deposition scheme paramaters implemented as
described in detail in Roeten et al. (2022).

*****************************************************************************************

Specific Key References pertaining to MGITM Simulations plus MAVEN NGIMS and EUVM Datasets:
--------------------------------------------------------------------------------------
Benna et al. (2019), Science, doi:10.1126/science.aax1553,(2019).
Bougher et al. (2015), J. Geophys. Res., 120, 311-342. doi:10.1002/2014JE004715.
Bougher et al. (2017), J. Geophys. Res., 122, 1296-1313. doi:10.1002/2016JA023454.
Pilinski, M. D., K. J. Roeten, S. W. Bougher and M. Benna, Dynamical Heating in the Martian Thermosphere, Journal Geophysical Res., XXX, (forthcoming - 2022). doi: .....
Roeten et al. (2022), J. Geophys. Res., 127, XXXX-XXXX. doi:10.1002/..............
Stone et al., (2018), J. Geophys. Res., 123 , 2842-2867. doi: 10.1029/2018JE005559.
Thiemann et al. (2017), J. Geophys. Res., 122, 2748-2767. doi:10.1002/2016JA023512.

Citation for this dataset:
-------------------------

Bougher, S. W., M. D. Pilinski (2022). Dynamical Heating of the Martian Thermosphere:
Temperatures, Winds and Thermal Balances using M-GITM. University of Michigan - Deep Blue Data.
https://doi.org/10.7302/t6gg-3t89

*****************************************************************************************

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