Work Description

Date: 16 September, 2021

Dataset Title: Statistical Characterization of GITM Thermospheric Horizontal Winds in Comparison to GOCE Estimations Dataset

Dataset Creators: D. A. Brandt, A. J. Ridley

Dataset Contact: Daniel Brandt, daabrand@mtu.edu

Funding: NSF-NASA Space Weather with Quantified Uncertainties Program, NASA Grant Number 80NSSC20K1581

Key Points:
-Cross-track horizontal winds from GOCE are compared to GITM for Jan-Oct 2013.
-GITM captured the overall distribution of horizontal winds, but with lower means than GOCE.
-GITM overpredicts horizontal wind in the equatorial ionization anomaly regions.

Research Overview:
Characterizing the thermospheric horizontal winds is an important challenge in atmospheric modeling due to role that horizontal winds play in atmospheric circulation, transporting densities and energy, associations with the diurnal tide, and interplaying with vertical winds to drive changes in thermospheric neutral composition. The mechanisms and drivers that underlie the physics of the thermospheric horizontal winds remain under investigation, and to date, no comprehensive statistical study between thermospheric winds generated by a physics-based atmospheric model and those retrieved from satellite measurements has been performed. Comparisons between cross-track horizontal winds from a 10-month run of GITM and those derived from the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite showed that GITM's modeled horizontal winds best in the polar zone and overestimated them at midlatitudes in the equatorial ionization anomaly region. GITM's wind response to geomagnetic activity represtend by the auroral electrojet index (AE) was best at polar noon and worst in the midnight auroral zone, its ability to capture seasonality was best in the northern high latitudes and worst in the southern high latitudes, and GITM displayed less wind variability as a function of solar radio flux proxied by F10.7 than GOCE, matching it best for F10.7>~150 sfu. Discrepancies in GITM's performance may be explained by inaccurate modeling of ion drift, ion drag, and electron densities.

Methodology:
The data are outputs from the Global Ionosphere-Thermosphere Model (GITM), along with the most recent version of the Python scripts that were used to generate figures and perform statistical analysis on the GITM outputs.

Instrument and/or Software Specifications: N/A

Files contained here:
- wind_data_splitter.py (loads in GITM and GOCE data and segments it according to different levels of geomagnetic activity)
- wind_plotter.py (plots the GITM and GOCE horizontal wind speeds over time and by magnetic latitude)
- gitm_goce_plotter.py (contains code that is called by 'wind_plotter.py' in order to display winds in time and magnetic latitude)
- wind_prob_maker.py (makes probability distributions and histograms of GITM and GOCE wind data)
- Results.2021_07_27.zip (a .zip file containing the GITM and GOCE data between 2013-01-01 and 2013-10-20)
- WWW_aeasy00005338.dat (a .dat file containing the Auroral Electroject Index data throughout 2013).
- time_axis.py (formats the x-axis to display ticks for a time interval in a uniform manner).
- ApexFileProcLinux.tar.gz (contains Fortran code for ApexPy to run, in order to convert coordinates from geographic to magnetic).

Several prerequisites are necessary before using any of the scripts:
- GNU Emacs
- Python 3.0+, Numpy 1.16
- apexpy (Python package for converting to magnetic latitude; pip install apexpy)
- ApexFileProcLinux must be extracted locally in order for apexpy to function.

## GENERATING DATA AND FIGURES ##

1. Reading in GITM and GOCE data:
- Open a terminal.
- Type 'ipython' and hit enter.
- Type 'run wind_data_splitter' and hit enter.
- The folder 'Results.2021_07_27_2013-01-01_2013-10-20' is created and files containing all of the GITM and GOCE data spit up by AE, MLAT, MLT, DOY, and F10.7 are saved there.
NOTE 1: Inputs for this script are on lines 152 through 158 and lines 166 and 167. They should not be changed at all. Because, however, the data this script loads in was in a different location than any other user will likely be aware of or have access to at the time of using this script, the following lines should be commented out to facilitate loading in of data in the same working directory: 15, 16, 17, 18, and 20, and the following lines should be modified to refer to the current working directory: 332.
NOTE 2: The default script splits the data up for the entire time between 2013-01-01 and 2013-10-20. To segment data for figures corresponding to the first week in March, the variable month_lim in line 154 should be set to a value of '10', the variable idx_date_start should be set to run_year+'-03-01' and the variable idx_date_end' should be set to run_year+'-10-20'.

2. Making wind speed over time and MLAT plots:
- Open a terminal.
- Type 'ipython' and hit enter.
- Type 'run wind_plotter' and hit enter.
- The file in line 28 of the script is read, and horizontal wind plots are made for the ascending and descending nodes for both GITM and GOCE, as well as the difference between to the two. The lines where os.system commands are executed to make the horizontal wind plots are: 970, 980, 992, and 1002. The lines where os.system commands are executed to make the vertical wind plots (should the user care to do so) are: 873, 883, 896, and 906.
NOTE: The changeable lines of this script are lines 22 through 25. The default values of lines 22 and 24 is '2013-01-01' and may be changed to '2013-03-01' to display data for March, if and only if lines 23 and 26 (for which the default value is '2013-10-20' is changed to '2013-03-07'. To make wind over time plots, the plot_basic variable in line 44 should be set to 1, but this should only occur when idx_date_start and idx_date_end correspond to start and end times (respectively) between the 1st day of March 2013 and the 7th day of March 2013.

3. Making probability distributions and histograms:
- Open a terminal.
- Type 'ipython' and hit enter.
- Type 'run wind_prob_maker' and hit enter.
- Within the folder 'Results.2021_07_27_2013-01-01_2013-10-20', probability distribution plots and histograms of GITM and GOCE horizontal winds are generated. The explicit commands for the probability distribution plots are lines 1094, 1104, 1114, 1126, 1136, 1146, 1158, 1168, and 1178. They must be uncommented in order to run. BE AWARE that inputs to this script are on lines 1067 through 1073, and should not be changed.
NOTE: There is a 'sys.exit()' command in line 1183 that must be commented out for the remainder of the code to execute, as the histograms are generated after line 1454, with commands that must be uncommented in lines 1463, 1474, 1482, 1492, 1501, 1507, 1511, 1523, 1535, 1547, 1555, 1563, 1571, 1579, 1587, 1591, 1595, and 1599.
##--##

Related Publications(s):
Brandt, D. A. and Ridley, A. J. (2020). Statistical Characterization of GITM Thermospheric Horizontal Winds in Comparison to GOCE Estimations. [Under Review]

Use and Access:
This data set is made available under an Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

To Cite Data:
Brandt, D. A., and Ridley, A. J. (2020), Statistical Characterization of GITM Thermospheric Horizontal Winds in Comparison to GOCE Estimations [Data set]. University of Michigan - Deep Blue.