Observations on 23 December 2018

The meteor radar, Rayleigh lidar, and SRWTL simultaneously collected data on Decem­ ber 23, 2018 from 2:20 to 15:55 UT. Figure 4.2 shows an average temperature profile as estimated by the SRWTL, along with a temperature estimate from the meteor radar. The temperature was estimated from meteor radar data using the method discussed in section 4.1.1 of this thesis using a temperature gradient of -2.48 K/km calculated from MSIS. Only underdense meteors (as determined by the classification algorithm discussed in Chapter 2) were used, and only those between 80 to 100 km and between 2:20 and 15:55 UT. This average temperature estimate from the radar was placed at 91 km, the height of peak me­ teor activity for that day. There is slight disagreement between the SRWTL temperature estimate at 91 km, 199.95 K, and the meteor radar average temperature, 211.59 K, a 6% over-estimation. However, note that the uncertainty on the radar estimate is ±7.45 K and the uncertainty on the SRWTL estimate is ±9.89 K, so there is some overlap.

Figure 4.2: Average temperature profile from the SRWTL (black) and their uncertainty (blue) are shown alongside the radar estimate for temperature using the method discussed in section 4.1.1, along with its uncertainty (red).

Given simultaneous measurements between three instruments, it is possible to test the hypothesis that Equation 4.14 can be used to calculate temperature given the ambipolar diffusion coefficient and density. Figure 4.3 shows an ambipolar diffusion coefficient profile estimated by the meteor radar, as well as a neutral density profile estimated by the Rayleigh lidar for 2:20 to 15:55 UT on December 23. The ambipolar diffusion coefficient was calculated using the decay times of the meteors and is an average over the time the lidars were operating, binned at 1 km. It generally follows an expected trend by increasing sort of exponentially as altitude increases [McKinley , 1961]. The neutral density profile is also an average over the running time of the lidars and binned at 1 km. It increases exponentially as altitude decreases, as expected.

Figure 4.3: The ambipolar diffusion coefficient estimated by the meteor radar is shown on the left side of the figure. The neutral density estimated by the Rayleigh lidar is shown on the right side of the figure.

It is important to note that while the ambipolar diffusion coefficient average profile was used in the following calculations, there is quite a bit of variation in ambipolar diffusion

coefficients in each height bin. Figure 4.4 shows a scatter plot of the ambipolar diffusion coefficient with height for the 4142 underdense meteors used in these calculations.

Figure 4.4: Ambipolar diffusion coefficient as calculated for each of the 4142 underdense meteors detected during the observation window is plotted against height of the detection. There is significant variability in each height bin.

The ambipolar diffusion coefficient estimates from the meteor radar and the neutral den­ sity estimates from the Rayleigh lidar were used to calculate a temperature profile using the method shown in section 4.1.2 of this thesis. Equation 4.14 was used to calculate the temperature profile, using a K0 value of 2.5×10-4m2/sV, as suggested in Younger et al. [2015]. Figure 4.5 shows the result of this calculation compared to the average radar temperature calculated using the method discusses earlier in this section, along with the SRWTL tem­ perature profile. The radar average temperature of 211.59 K is 23% lower than the new temperature estimate at 91 km of 261.08 K. It is clear that using Equation 4.14 with the values provided does not result in accurate temperature calculations. It may be that there is something missing in the model that needs to be accounted for, such as chemistry or mixing due to turbulence.

Figure 4.5: The temperature estimate from the radar using the method shown in section 4.1.1 (red) is compared to the temperature estimate using the ambipolar diffusion coefficient and the neutral density, as shown in section 4.1.2 (purple). The SRWTL temperature profile is shown in black with blue error bars.

A temperature profile was also calculated using 4.13. The ambipolar diffusion and density profiles were taken from the radar and Rayleigh lidar systems. The values for mean mass of an air molecule, m, and mean mass of a diffusing particle, μ, were taken from Greenhow and Hal l [1960] as 26 amu and 35 amu, respectively. The collision cross section, A, was calculated using an expression from Strelnikova et al. [2007] and collision frequencies from Hill and Bowhill [1977]. Equations 4.15 and 4.16 are for collision cross section, A, and are from Strelnikova et al. [2007]. In these equations, mp is the mass of a diffusing particle

in kilograms, mn is the mass of an atmospheric particle in kilograms, k is the Boltzmann

constant, T is the temperature in K, rp is the radius of a diffusing particle in meters, rn is

the radius of an atmospheric particle in meters, and νpn is the total collision frequency in

s-1. Equation 4.17 is for collision frequency, νpn, and is from Hill and Bowhill [1977]. In this

in cm-3 . A was calculated at 1 km height intervals using neutral number densities from the Rayleigh lidar using these equations.

The temperature profile calculated using Equation 4.13 was compared to the temper­ ature profile calculated using Equation 4.14, and the radar average temperature estimate. The results are shown in Figure 4.6. The radar average temperature is 211.6 K (red), the temperature at 91 km from Equation 4.14 is 261.1 K (purple), and the temperature at 91 km from Equation 4.13 is much higher at 3172.4 K.

Figure 4.6: A temperature profile was calculated for 23 December 2018 using Equation 4.13 (green) and compared to the other temperatures calculated using the radar data (red and purple).