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 />
8.6.3 Precipitation Accumulation<br />
Precipitation accumulation is calculated in millimeters over a specified time<br />
period by summing the volume of all precipitation particles falling through the<br />
defined volume. As mentioned above, as with most other similar optical<br />
detectors, the <strong>PWS100</strong> will be subjected to increased error and bias in windy<br />
conditions.<br />
Accumulations of snow are based on the water content of those particles. The<br />
snow water content is a user definable parameter in the instrument, see Section<br />
7.4.5. The accumulation given will be the water equivalent depth and not the<br />
snow depth which requires further knowledge of packing structures, wind<br />
effects, ground temperature, ground type and a myriad of other parameters<br />
related to snow depth. The ratio of water accumulation to snow depth will be<br />
lower than the snow water content figure and is typically in the order of 0.1<br />
(i.e., the snow pack is 10 times deeper than the water accumulation of the<br />
melted snow pack). Local conditions will dictate the values to use and since<br />
these will be different for every location it is not possible to give accurate snow<br />
depth figures with the <strong>PWS100</strong>. Thus only accurate snow water content values<br />
for the particles falling through the detection volume are given.<br />
8.6.4 <strong>Present</strong> <strong>Weather</strong><br />
<strong>Present</strong> weather covers precipitation type analysis and visibility in the <strong>PWS100</strong><br />
algorithms. The PWS has separate routines for these two functions along with<br />
various housekeeping tasks to ensure that the output is as accurate as possible.<br />
8.6.4.1 Precipitation Types<br />
The precipitation types identified are drizzle, freezing drizzle, rain, freezing<br />
rain, snow grains, snow flakes, ice pellets, hail and graupel. A mixture of these<br />
types and intensity of these types gives an array of outputs that have been<br />
assigned codes by the WMO. These are defined as the WMO SYNOP codes<br />
(4680, W a W a ). See Appendix A for the code table. Each particle is assigned a<br />
type from analysis of particle size, velocity, signal structure and inclusion of<br />
any other weather parameters from auxiliary instruments connected to the<br />
<strong>PWS100</strong>. The CS215-PWS provides three additional parameters, temperature,<br />
relative humidity and wetbulb temperature. Fuzzy logic is used to define<br />
particle type from these values as this provides the best estimate of a particle<br />
type, allowing for grey boundaries in terms of size and velocity measurements<br />
for example, which may help to determine particle types during windy<br />
conditions. Standard logic can be flawed when incorporating a number of<br />
different parameters from the signal and auxiliary instruments as the<br />
boundaries have to be effectively black and white allowing for no margin of<br />
error, this is highlighted by the use of temperature matrices on certain<br />
instruments which have fixed boundaries between snow and rain. With such<br />
non-fuzzy logic instruments all particles above a set temperature are classified<br />
as rain, drizzle or unknown and below that temperature have to be snow or<br />
unknown (the unknown classification sometimes being used if other sensor<br />
values are contradictory to the temperature measurement – for example a<br />
wetness grid on the instrument remains dry). The <strong>PWS100</strong> clearly does not<br />
have such limitations and can cope with a wider variation in meteorological<br />
parameters within its classification routines.<br />
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