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Data Analysis: <br />Data recorded as text files on a personal computer were analyzed using an algorithm built <br />into an Excel spreadsheet shared with us by Vidal Salazar of NCAR. Peak voltages <br />recorded from the photodetector in the instrument were identified for each cloud-forming <br />sampling cycle, and converted to a peak droplet concentration using an empirical <br />calibration performed earlier in 2005 by the University of Wyoming. An example of a <br />time sequence of the photodetector voltage through one day’s sampling period is shown <br />in Figure 2. <br />Figure 2: Photodetector voltage as a function of time during the mid-morning sampling <br />period on 3 July 2005. <br />There are two sampling cycles at each supersaturation, with supersaturation cycling <br />through 0.3, 0.5., 0.7, and 1.0%. In this 8459 sec (~ 2 hr 22 min) period the voltage <br />sequence can be seen to rise and fall with each cycle, reaching 2 peaks at the end of each <br />8 sample cycle before returning to low values for the next cycle beginning at 0.3% <br />supersaturation. The baseline voltage typically increased with time during a day, but the <br />analysis algorithm determined the base-to-peak voltage difference before applying the <br />empirical calibration equation for converting from peak voltage to CCN concentration. <br />The CCN concentrations derived from the data shown in Figure 2 are contained in Figure <br />3. It can be seen that there is significant scatter over this time period for each <br />supersaturation, with the maximum and minimum values being roughly 50% of the mean <br />greater and less than the mean at each supersaturation. The concentration of CCN yields <br />increases with increasing supersaturation. <br />5 <br />