Results of the project for the first year (2015) are presented in Figure 1. In addition to data collected this year, the figure contains one set of points collected at the end of June last year (2014). Apparent in the data is the transition from clearer Winter conditions (with lower k values) to Spring and Summer conditions characterized by an atmosphere with increasing humidity and aerosols (dust) and higher k values, and then a return to clearer conditions in the late Fall. The large gaps in the data set during August and September and again in November and December are the result of unusually cloudy conditions. The Fall of 2015 was characterized by "El Nino" conditions, which are usually associated with increased storminess in the American Southwest.

So, what in the atmosphere is controlling the trend in k values? The results in Figure 2 give some indication. Here, k values from Figure 1 are plotted versus corresponding values of average dew point temperature (T_d). The average dew point temperature was calculated for the period over which stellar G2V observations were made using data from a nearby weather station (Slaton, Texas), which is part of the West Texas Mesonet. Dew point temperature is a conservative measure of the amount of water vapor in the atmosphere. The data in Figure 2 suggest that the magnitude of k in all three spectral bands (red, green and blue) is significantly related to atmospheric moisture content.

The data for the red, green and blue spectral bands in Figure 2 were fit with simple linear regressions. These regressions explained 59.6, 77.4, and 64.0 percent of the variance in the red, green and blue data sets, respectively. The slopes of the three regressions were significantly different from zero (at the 95% confidence level). This indicates that k values needed in processing astronomical imagery can be estimated with some degree of accuracy directly from measurements of T_d obtained over the course of the imaging session, although the scatter in the points around each regression line suggests that possibly one or more as-yet undetermined atmospheric factors (such as aerosol content) may also be influencing the value of k.

A similar analysis can be performed for the weight factors used in processing imagery. Figure 3 shows the results of plotting the values of weight factors (W) for the red, green and blue spectral bands versus corresponding values of dew point temperature. These values were calculated using the procedure described for calculated weight factors assuming their equality at an air mass of 1 (i.e., X = 1). In this case, there was no apparent relationship between the weight factors for the red and green spectral bands and T_d. However, the weight factor for the blue spectral band was observed to be weakly related to T_d. We can summarize these results as follows:

W_1,r = 1
W_1,g = 0.9925
W_1,b = 0.8717 - 0.001686 T_d

For Exoatmospheric G2V Correction, where the values of the red, green and blue weight factors are assumed equal outside the atmosphere (i.e., X = 0), the following results are provided:

W_0,r = 0.9577
W_0,g = 1
W_0,b = 0.9654 - 0.001870 T_d

If dew point data are not available, an average value for the blue weight factor W_0,b = 0.8953 should be adequate for most image processing situations.