PWS100 Present Weather Sensor - Campbell Scientific
PWS100 Present Weather Sensor - Campbell Scientific
PWS100 Present Weather Sensor - Campbell Scientific
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Section 8. Functional Description<br />
8.6 Algorithm Description<br />
8.6.1 Detecting and Classifying Precipitation<br />
The <strong>PWS100</strong> has a structured detection volume consisting of four sheets of<br />
light each 0.4 mm in depth with 0.4 mm spacing. The area of detection is<br />
approximately 40 cm 2 as defined by the overlap of the two detectors each of<br />
which is 20° off of the light sheet propagation axis.<br />
Detection of precipitation is carried out in real-time. Time and frequency<br />
domain analysis of the signals from the two sensor heads is carried out to<br />
ensure all particles are detected. Time domain analysis allows quick stripping<br />
of larger particles while the frequency domain analysis picks out smaller<br />
particles using a slightly more computationally intensive routine.<br />
Using a series of thresholds and checks precipitation signals from particles as<br />
small as 0.1 mm diameter passing through the area of detection are identified<br />
while any sources of noise or particles not passing through the defined area are<br />
discounted. A routine then strips the signal from the incoming data for each<br />
real particle passing through the defined area in order to minimize the data that<br />
is to be further processed.<br />
When a particle passes through the four light sheets it provides a signal<br />
characteristic of the particle type and provides information on the velocity of<br />
the particle, by means of auto correlation, and size, by means of cross<br />
correlation of the two channels of data. Particles for which cross correlation<br />
could be poor (e.g., snow) may have their size measured by the analysis of<br />
particle transit time or signal amplitude (in the same way that present weather<br />
sensors without structured detection volumes measure particle size). Due to the<br />
Gaussian nature of the light sheet intensity there is more error involved in these<br />
transit time or signal amplitude measurements than those using the cross<br />
correlation which is independent of the signal intensity.<br />
The structure of the particle is to some degree given by the analysis of the<br />
signal peak to signal pedestal ratio. The signal pedestal is found through<br />
stripping higher frequencies from the particle signal (i.e., those produced by the<br />
four light sheets) and is higher for particles with higher crystallinity and<br />
therefore more scattering sites. Particles of water have almost no pedestal<br />
compared to the peak signal value whereas snow can have large pedestals<br />
compared to the peak signal value.<br />
Analysis of separate snow and rain/drizzle events has shown that there is very<br />
little overlap in the signal peak to pedestal peak ratio for the solid and liquid<br />
particles, though drizzle values extend further towards the snow values and<br />
snow grains extend further towards the liquid precipitation values, while the<br />
larger rain and snowflakes remain almost entirely separate. This is shown<br />
graphically in Figure 8-6 which represents two separate events one comprising<br />
snowflakes and snow grains (particle count on the left vertical axis) and one<br />
representing a mixed drizzle and rain event (particle count on the right vertical<br />
axis). This particular differentiator, only obtainable because the <strong>PWS100</strong> has a<br />
structured detection volume, is extremely useful in the fuzzy logic processing<br />
as described below and further in Section 8.6.4.1.<br />
8-6