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Chapter 3 WINDII Data

In Chapter 2 we have explained the detail description of the ICON and the MIGHTI instrument, geometry of MIGHTI observation, MIGHTI data structure etc. However, ICON is yet to launch so we need a set of real observational data set from a similar instrument like MIGHTI to validate our methods and approach. Here, in this section we will discuss a different instrument which is similar to MIGHTI called Wind Imaging Interferometer (WINDII), from a different mission called Upper Atmosphere Research Satellite (UARS). We will use the data from WINDII to test as much of the code we developed for MIGHTI as is possible, but we note that as WINDII is not identical, and the orbits are different between the two spacecraft, a complete test will not be possible.

 Upper Atmosphere Research Satellite (UARS):

The Upper Atmosphere Research Satellite (UARS) was an Earth Observing System (EOS) NASA mission launched in September 12, 1991 from Cape Canaveral from (https://uars.gsfc.nasa.gov),
Florida. The main purpose of the mission was to study the earth atmospheric from 50 mi and 180 mi which the same altitude ranges of the earth upper atmosphere as ICON. UARS was launched into a 585 km, 57-degree inclination orbit with an orbital period of 96 minutes from (https://uars.gsfc.nasa.gov). UARS was active until September 24, 2011, orbiting the Earth more than 78,000 times over an entire 11-year solar cycle.
UARS was equipped with 10 onboard instruments from (https://uars.gsfc.nasa.gov),

 WINDII Instrument:

WINDII (Wind Imaging Interferometer) is one of 10 instruments aboard the (UARS) Satellite. Like MIGHTI it also measures Doppler shifts from visible region airglow. The instrument employed was a Doppler Michelson Interferometer that takes measurements during daylight and night conditions in the upper atmosphere mostly from 80 to 300 km (from https://uars.gsfc.nasa.gov) which covers the altitude range of what ICON is observing .WINDII helped in determining a number of atmospheric aspects among which, influence of dynamics on the transport of atmospheric species, non-migrating tides in the thermosphere both in equatorial and high latitudes, solar and geomagnetic influences, temperatures from atmospheric‐scale heights nitric oxide concentrations, and the occurrence of polar mesospheric clouds (Shepherd et al, 2012 provides a complete review). WINDII measures zonal and meridian winds, temperature, and VER in the upper mesosphere and lower thermosphere (80 to 300 km) from observations of the Earth’s airglow, for our research we are mostly interested in the change of the wavelength and the brightness of atomic oxygen O(1D) redline (630.0 nm).

 WINDII data set:

For the purpose of this work we need time, latitude longitude, local solar time, solar zenith angles from the WINDII data set. We have taken a particular Day of the Year (1992 Year 015 Day) for our work, because it has both Zonal and Meridian wind from the redline, with good data availability. We have got data for Red line airglow for this day and our analysis for WINDII data set will only confined to Red line. For this particular day all the variables like time, latitude longitude, local solar time, solar zenith angles are stored in an array size of [89,686] (from https://sscweb.gsfc.nasa.gov). This array represents the 89 vertical points 686 times.

Missing data set:

In this section we will discuss the variables we are missing from the WINDII data sets

  • Volume Emission Rate (VER)
  • Spacecraft individual tangent locations
  • Line of Sight (LOS) Velocities
  • Most importantly for WINDII there is no data available at sunset and sunrise.

WINDII only gives us data at some local times – actually none near sunrise or sunset (from Figures 47 and Figures 48) where we expect asymmetry to be a big problem – this is the reason we have no data. We expect MIGHTI to work better for Green than Red line airglow except day/night boundaries. (Harding et al., 2017) suggests there is a lot of asymmetry at night rather than during the day.

Latitude and Longitude:

WINDII data files do not contain the spacecraft location. To get this information we use data from https://acdisc.gesdisc.eosdis.nasa.gov, which provides the spacecraft location at time intervals of 65.536 seconds, or about 495 km intervals along the LOS tangent track. We then interpolate these onto the times of the WINDII data to give us a good estimate of where the spacecraft was when the data were taken.

WINDII data in Latitude and Local Time

The figures below show the locations where the WINDII data exist for 1992 day 015.
For both the plots the empty spaces mean the time interval of the instrument taking the data or the times there is no data. Please note, we can see we don’t have any data near sunrise or sunset, which is where Harding et al expected the largest asymmetry. We do, however, have plenty of data at low to mid latitudes in both day and nighttime.
Zonal and Meridian Wind:
For the day in 1992, day 015,from WINDII we have vertical profiles of wind (meridional and zonal components) are also saved in an array size of [89,686] ( https://sscweb.gsfc.nasa.gov). This array represents the 89 vertical points 686 times.
Spacecraft Location:
We will need the Spacecraft positions (X, Y, Z) in ECEF coordinate system to calculate vertical profile of view directions (unit vectors) for each vertical pixel (from 180 to 300 km for red). We have collected the locations of the spacecraft for the particular Day of the Year (1992 Year 015 Day) in (X, Y,Z ) in ECEF coordinate system from (https://spdf.gsfc.nasa.gov/) for UARS (Upper Atmosphere Research Satellite (UARS)).
From that particular day data we have 1440 points (Time, X, Y, Z, Latitude, Longitude,Altitude).
Ray Geometry:
Using spacecraft location and our altitude for Redline, the wind locations we get the ray geometry. Once we have the view directions, we will compute LOS rays along each view direction at all steps out to 6860 km along each ray (all 48), in ECEF and lat-lon-alt. This should be an array of about 48 x 3 x 686 (where 3 is x, y, z or latitude, longitude, altitude, and 686 is the steps along the ray). This is exactly the same process as for MIGHTI discussed in Chapter 2. One example is shown below.

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TIEGCM data Interpolation:

We have run the TIEGCM for 1992, Day 015, using the conditions present on that day (solar F10.7 etc.). We know that WINDII [89 (Altitude) ,686 (Times)] and TIEGCM [144 (Longitude), 72(Latitude), 57(Altitude), 24(Hour)] data set are different, to be able to use the same approach and methods they must have the dimension. We will interpolate the TIEGCM data set to fit into the WINDII grid to allow a 1:1 comparison.

Results

Figure 50 taken from “Physics of the Earth’s Space Environment”- Prölss 2004 shows the global pressure and East and North wind distribution for an altitude of 300 km during the spring equinox with Local Time.

Eastward wind comparison:

Here we will compare WINDII eastward winds with TIEGCM model eastward winds at the equator. From TIEGCM wind color contour plot we can see at midnight the wind speed is eastward and around [+10, to +15 ms-1]. However, at noon, the wind is northward and ranges from [-50, to -65 ms-1].
Again, at the before midnight the wind is eastward and varies from around [+12, to +23 ms-1].We will begin this section by comparing the WINDII data sets with TIEGCM model and textbook (from “Physics of the Earth’s Space Environment”- Prölss 2004).

Northward wind comparison:

Here we will compare WINDII northward winds with TIEGCM model northward winds at the equator. From TIEGCM wind color contour plot we can see at midnight the wind speed is northward and around [+10, to +15 ms-1]. However, at noon the wind is going northward to eastward and ranges from ranges from [+10, to -15 ms-1]. Again, at the before midnight the wind is northward and varies from around [+12, to +23 ms-1].
Similarly, for the northward winds from WINDII shows at midnight the wind speed is northward and around [+10, to +15 ms-1]. At noon the wind is going from northward to eastward and ranges from [+10, to -15 ms-1]. Again, at the before midnight the wind is northward and ranges from around [+12 to +23 ms-1]. So, we can conclude that the WINDII northward wind patterns matches both the TIEGCM model and textbook northward wind patterns.

Mean of WINDII and TIEGCM east and north winds:

Figure 53 shows the eastward mean winds vs local time where the blue points represent WINDII east mean winds and the yellow points TIEGCM east mean winds, we can see that there is visible similarity. The best match of the TIEGCM model east mean winds is seen, at night-time with the WINDII east mean winds. However, at nighttime the WINDII east winds are more spread compared to TIEGCM east winds, otherwise we can say the standard deviation of the WINDII east winds are higher than the TIEGCM east winds. There is a visible pattern in the night-time east winds, after midnight we see the east winds varies from positive to negative, for the WINDII east mean winds move from positive to negative (i.e. +110 ms-1 to -90 ms-1), for the TIEGCM east mean winds move from positive to negative (i.e. +60 ms-1 to -40 ms-1).
In the Day-time both WINDII and TIEGCM east mean winds are mostly negative with WINDII east mean winds vary from around [-90 ms-1 to +100 ms-1] and TIEGCM east mean winds vary from around [-120 ms-1 to +80 ms-1], again there is a clear similarity in the winds pattern between them. However, winds in the afternoon are more spread than the winds in the noon, otherwise we can say the standard deviation of the east winds are higher in the afternoon than the east winds at noon.
Figure 55 shows the Northward mean winds vs local time where the blue points represent WINDII north mean winds and the yellow points TIEGCM north mean winds, we can see that there is visible similarity. The best match of the TIEGCM model north mean winds is seen, at night-time with the WINDII north mean winds. However, at nighttime the WINDII north winds are more spread compared to TIEGCM north winds, otherwise we can say the standard deviation of the WINDII north winds are higher than the TIEGCM north winds. There is a visible pattern in the night-time north winds, after midnight we see the north winds varies from positive to negative, for the WINDII north mean winds move from positive to negative (i.e. +120 ms-1 to -80 ms-1), for the TIEGCM north mean winds move from positive to negative (i.e. +40 ms-1 to -50 ms-1).
In the Daytime both winds vary from positive to negative, for the WINDII north mean winds move from positive to negative (i.e. +130 ms-1 to -110 ms-1), for the TIEGCM north mean winds move from positive to negative (i.e. +50 ms-1 to -90 ms-1). However, winds in the afternoon are more spread than the winds in the noon, otherwise we can say the standard deviation of the north winds are higher at the afternoon than the north winds at the noon.
Figure 56 shows the northward mean winds with standard deviation vs local time where the blue points represent WINDII east mean winds with standard deviation and the yellow points TIEGCM east mean winds with standard deviation. We can see from the figure that there is no visible similarity. However, compared to eastward mean winds WINDII northward mean winds standard deviation is less and shows better agreement to the TIEGCM northward mean winds both in Day and Nighttime.

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Mean East and North wind comparison with Latitude:

Figure 57 shows the eastward mean winds vs Latitude where the blue points represent WINDII east mean winds and the yellow points TIEGCM east mean winds, we can see that there is no visible similarity, where most of the TIEGCM model east mean winds have negative values that vary from around [-210 ms-1 to 40 ms-1], in contrast to the WINDII east mean winds have both negative and positive values that vary from around [+110 ms-1 to -50 ms-1]. At low-mid-high latitudes WINDII mean winds have both positive and negative values where TIEGCM model east mean winds have negative values and again there is no similarity between them.
Figure 58 shows the eastward mean winds with standard deviation vs Latitude where the blue points represent WINDII east mean winds with standard deviation and the yellow points TIEGCM east mean winds with standard deviation. We can see that there is no visible similarity and further the standard deviation of the WINDII eastward mean winds is bigger compared to the TIEGCM eastward mean winds at all (low-mid-high) latitudes.
Figure 59 shows the northward mean winds vs Latitude where the blue points represent WINDII north mean winds and the yellow points TIEGCM north mean winds. From the figure it is clear that at low latitude WINDII north mean winds have a visible similarity though direction of the WINDII and TIEGCM north mean winds are different. WINDII north mean winds move from positive to negative where the TIEGCM north mean winds move from negative to positive. At high latitude WINDII and TIEGCM north mean winds show a similar pattern both move from positive from negative. Overall, we can state that WINDII northward mean winds agree more with TIEGCM northward mean winds compared to eastward mean winds.
Figure 60 shows the northward mean winds with standard deviation vs Latitude where the blue points represent WINDII north mean winds with standard deviation and the yellow points TIEGCM north mean winds with standard deviation. We can see from the figure that there is no visible similarity. However, compared to eastward mean winds WINDII northward mean winds standard deviation is less and shows better agreement to the TIEGCM northward mean winds at all (low-mid-high) latitudes.

 Distribution of Asymmetry:

In this section we will calculate the asymmetry along each ray as discussed in chapter 2. Though we know from (Harding et al., 2017) there is a lot of asymmetry at night rather than at day and the greatest asymmetry at sunset and sunrise, due to no data at sunset and sunrise we will only calculate asymmetry at some other local times. We will also have a detailed look at one ray, and the variation of the altitude, latitude, longitude along the ray. This will help us understand how we calculated asymmetry along each ray.

1. Chapter 1: Introduction
1.1 Earth Atmosphere
1.2 The Layers of the Atmosphere
1.3 Upper Atmosphere
1.4 Dynamics of E-region and F-region
1.5 ICON Mission
1.6 MIGHTI Wind and Airglow
2. Chapter 2 Methodology 
2.1 Airglow
2.2 MIGHTI Viewing Geometry
2.3 Geometry of MIGHTI observation
2.4 Approach (Ray Tracing)
2.5 TIEGCM Model
2.6 MSIS Model
2.7 Chapter 3 WINDII Data
3. Results
3.1 Eastward wind comparison
3.2 Northward wind comparison
3.3 Mean of WINDII and TIEGCM east and north winds
3.4 Mean East and North wind comparison with Latitude
3.5 Distribution of Asymmetry
3.6 Discussion
4. Chapter 4 Conclusions
Bibiliography

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