Using the method described in section 2, we determine daily mean temperatures from the echo fading times measured by NSMR and subsequent ambipolar diffusion coefficients for all available days since 1 October 2001, these being then regarded as a “first estimate” or “raw.” Between 14 August 2004 and 22 April 2008 there were 1097 days when Aura MLS measurements coincided with NSMR measurements; both NSMR raw temperatures and Aura temperatures are shown in Figure 2 whence we can easily identify the clear systematic offset. A scatterplot of Aura determinations versus coincident NSMR ones is shown in Figure 3 together with a linear regression yielding the relation
where TNSMR and TAuraMLS are the NSMR and Aura MLS determined temperatures respectively, all values in Kelvin. In addition we separate out winter (November, December and January) and summer (May, June and July) days (shown by blue and orange in Figure 2) to illustrate qualitatively how the discrepancy between methods is seasonally dependent. As seen in equation (3), the slope of the linear fit was found to be 1.44 ± 0.02 KK−1 and the intercept found to be −9 ± 3 K and the next step is to apply these coefficients to the original NSMR data thus normalizing them to the Aura values. These results are not shown here because, as we shall see forthwith, they are only an intermediate step in the calibration process.
 So far we know little of the accuracy of the new NSMR temperatures; however, it is reasonable to assume that periodicities are real and that phases associated with them can be determined. Next, therefore, taking full advantage of the temporal resolution of the radar, 30 min, we examine the intraday variability of temperature relative to the corresponding mean for the day. These are then assembled into monthly average daily variations to yield the total tidal perturbation phase and amplitude (the latter to within the constraints imposed by the accuracy of equation (3)). Recalling now Figure 1, we see that the Aura measurements are not daily means as are the NSMR values, but are representative of the period 0200–1100 UT only and therefore the daily mean plus the tidal perturbation corresponding to the measurement period). Figure 4 shows monthly tidal perturbations as a function of time of day (applying the correction from equation (3) to the NSMR data in Figure 2) and we have also indicated 0200–1100 UT where Aura measured. The differences between monthly averages of the 0200–1100 UT (i.e., Aura measurement period) and 0000–2400 UT (i.e., NSMR measurement period) temperatures are given in Figure 5. Next we correct the Aura values by subtracting these measurement-period induced biases in order to arrive at daily mean temperatures that are indeed representative of the entire day, and perform a revised linear regression, akin to Figure 3. We wish to stress that in the absence of measurements of temperature at latitudes around 80°N, semiempirical models of the temperature tides are sparse. We therefore resort to a purely empirical approach consisting of determining biases solely due to sampling differences; although we intuitively know these are related to tides, we do not include any a priori assumptions as to tidal modes and their phases. The intercept now becomes zero (±2.7K) and the gradient reduces to 1.40 ± 0.02 KK−1 with a mean absolute deviation of 7 K. Calibrating the NSMR raw data now a second time, using the coefficients from this new iteration, yields the time series shown in Figure 6 where we have again included the Aura MLS values, this time after applying the seasonally varying correction indicated by Figure 5. In Figure 7 we now show a scatterplot of coincident NSMR and Aura values to give some appreciation of uncertainties. Although difficult to see, the points fall nearer the regression line following the adjustment for Aura observation time. The linear regression is indistinguishable from the line of zero intercept and gradient unity and we show the mean absolute deviations on either side of this. Superimposed are the K-Lidar temperatures where available [Höffner and Lübken, 2007]. Corresponding rotational OH(6–2) [e.g., Sigernes et al., 2003; Hall et al., 2006] measurements, necessarily from winter days only explaining their grouping in the top right-hand part of the distribution, show systematically higher values however. As stated earlier, Hall et al.  had presupposed the OH layer to be at a fixed height of 87 km and adjusted the temperatures to be representative of 90 km using gradients given by Picone et al. . Mulligan et al.  and Dyrland et al.  show this not to be the case, however, and that furthermore the height for which the OH-derived temperatures is representative is determined, at least in part, by the meridional wind. Other studies of OH temperatures and heights [e.g., Azeem et al., 2007; Viereck and Deehr, 1989] confirm that calibration of NSMR by OH is not a simple task and that our strategy here, viz. using Aura MLS is an improvement on Hall et al. .