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6.6 an Algorithm Deriving Snowfall from an Ensemble of Sonic Ranging Snow Depth Sensors

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6.6 an Algorithm Deriving Snowfall from an Ensemble of Sonic Ranging Snow Depth Sensors 6.6 AN ALGORITHM DERIVING SNOWFALL FROM AN ENSEMBLE OF SONIC RANGING SNOW DEPTH SENSORS * Alexandre Fischer and Yves Durocher Environment Canada, Toronto, Ontario, Canada 1. INTRODUCTION Because of the desire to save money, The operating principle behind using as well as the need to take measurements in USDS is that they take snow on ground remote locations and difficult terrain, ultrasonic readings as a point-oriented distance to target snow depth ranging sensors (USDS) have measurement. The USDS chosen by traditionally been one of the most popular Environment Canada (EC) is the Campbell methods for taking automated snowfall (SF) Scientific Sonic Ranging Sensor (SR50; CSD measurements in lieu of manual observations. 2003). The SR50 consists of a These sensors have been used extensively transmitter/receiver which emits/receives a 50 with regards to avalanche forecasting kHz ultrasonic pulse. The time it takes for the (Ferguson et al. 1990), and for snow removal pulse to return to the receiver (after reflecting operations triggered by exceeding specified off a targeted surface) divided by two gives the snow-depth thresholds (Gray and Male 1981). distance to the target in metres. The more These sensors have also be used to quality snow there is on the ground beneath the control automatic recording gauge sensor, the less time it takes for the sound to measurements by providing additional details return the receiver. Subtracting this number on the type, amount, and timing of from a fixed reference point creates a “Snow precipitation (Goodison et al. 1988). on Ground” (SOG) measurement. The change While this technology is not the only in SOG levels over time gives, in theory, a SF in-situ method currently used to measure SF measurement. (weighing snow gauges, pulse light source Because SF measurements are detectors, hotplate total precipitation gauge, constructed from the derivation of SOG laser snow gauge, and infrared triangulation measurements over time, other meteorological sensors are other commonly used phenomena; such as melting, settling, and technologies all of which have advantages and redistribution of snow; can influence the ability disadvantages of using in comparison to to derive a SF statistic. Additionally, the snow USDS technology), USDS have the advantage surface structure (low density snow) can of being “relatively” cheaper to install, easier to cause problems with the SR50’s ability to maintain, safer to use, and use less power in report snow depth (Goodison et al. 1984). comparison to some of the other technologies. Problems have also been identified with the Technologies which measure snowfall rates ultrasonic pulse being attenuated owing to using the principle of optical attenuation have intense snowfall or low density snowfall been shown to be misleading in many events, thus resulting in less reliable return instances due to the wide variety of snow signals (Brazenec and Doesken 2005). crystal types (Rasmussen et al. 1999). It is because of these concerns that Meanwhile, technologies which calculate total EC has been working on an algorithm to precipitation, and not SF, are not always able improve upon the derivation of automated SF to correctly identify the phase of the measurements (Fischer and Durocher 2006; precipitation it measured. hereafter referred to as FD06). The idea behind their work was that by using an ensemble of three SR50’s, as well as a Geonor Total Precipitation Gauge with single * Corresponding author address: Alexandre Fischer, Environment Canada, Toronto, Alter-shield, a more accurate SF Ontario, Canada, M3H 5T4; measurement is produced if there is a E-mail: [email protected] consensus amongst the instruments that SF occurred (increase in Snow on Ground levels metres to the northeast of the other two under the three SR50 sensors, and sensors. The remaining two of SR50’s Precipitation in the case of the Geonor). The (hereafter referred to as SR50b and SR50c, reason for using two different types of sensors respectively) were oriented 180 degrees apart is that both the SR50 and Geonor were not from each other at the southwest end of the developed to measure actual SF. From this trestle, and faced southeast and northwest point forward the algorithm used will be respectively. The concrete posts on which the denoted as S3-1 (i.e. three SR50’s and one trestle was mounted also comprised Geonor sensor algorithm). somewhat this experiment, because snow The objective of this study is to could occasionally pile up near the posts. continue the evaluation of the S3-1 algorithm The SR50’s were configured to detect which was first introduced in FD06. The new three target echoes and send serial ASCII case studies are from St. John’s, messages with distances and quality Newfoundland. St John’s is located on the numbers. Sensors were polled once a minute Avalon Peninsula, a site which experiences and output data was recorded in daily files strong winter storms, as well as warm and with a time stamp. Any missing return signals cold spells which should greatly influence or data of low quality were replaced by the SOG measurements. Because of St. John’s value recorded by that SR50 one minute vicinity to the Atlantic Ocean, the area earlier. The data was then filtered to retain experiences heavy snowfall events (often values for the last 4 minutes of each quarter blowing snow occurs simultaneously), as well hour (each quarter hour ended at zero, fifteen, as heavy rain and mixed precipitation events. thirty, and forty-five minutes, respectively). Frequent periods of freezing rain and freezing The four minutes of each quarter hour were drizzle are also common. then checked between each other to see how The authors of this paper strongly many of the SOG values were within 2.5 cm of recognize the triple configuration precipitation each other. All the target echoes which met algorithm developed by NOAA’s National these criteria where then were averaged to Climatic Data Center for the Geonor Weighing produce an averaged quarter-hour SOG value. Precipitation Gauge (Baker et al. 2005). The In cases where none of the four SOG SF algorithm which will be presented in this measurements were within 2.5 cm of each paper is an adaptation of this algorithm. The other, the SOG value constructed 15 minutes SF value presented in this paper will be earlier was used. compared to the “measured 24 hour SF totals” Because the speed of sound is (MSF) owing to the fact the manual dependent on the density of air (primarily as a observations of SF were measured at this time function of temperature), the distance to target frequency. measurements have to be corrected by the following equation: 2. TEST DATA SETS 0.5 CDT = RDT*(TKELVIN/273.15) , (1) Raw data was collected from three SR50’s and a Geonor at St. John’s where CDT = Corrected Distance to Target International Airport Newfoundland, Canada Reading in metres (CYYT) from 06 UTC 23 January 2006 to 06 RDT = Raw Distance to Target UTC 1 April 2006. The ground surface of the Reading in metres test-site slopped downwards towards the TKELVIN = Air Temperature in Kelvin north, so any SR50 readings made were somewhat comprised by this fact. The uneven The Geonor Total Precipitation Gauge ground surface also made it more likely that was sited approximately 28 metres to the drifting snow would become an issue for this southwest of the SR50 array. It had one experimental data set. transducer, and an Alter-Shield. The The SR50’s were attached onto posts instrument took readings every five seconds of approximately 2 metres high off the ground. the weight of liquid water captured by the The posts were connected onto a trestle instrument (change of liquid water over time oriented in the northeast-southwest direction. yields a “precipitation” statistic). Each The first SR50 sensor (hereafter referred to measured reading was inserted into the SR50a), faced northwest and was placed 7 following recursive equation (a low-pass filter): F(Xi) = W*Xi + F(Xi-1)*(1-W), (2) the three SR50 sensors (a, b, and c), and the measured weight of water collected by the where F is the value of the Geonor at any Geonor are put into place-holder reference given Xi and is denoted by F(Xi) levels. For each subsequent time step (every 15 minutes), new SOG measurements are Xi is the current output value of the recorded and then subtracted from the Geonor reference levels as denoted in equation 3. W is the weighting function which in ∆ (SOG for the SRa) if (3) this case is 10 percent (0.1) (SRa – SRahold) ≥ ST or (SRa – SRahold) ≤ ST Using this equation ensures that anomalous readings are filtered out, and that no missing ∆ (SOG for the SRb) if values in the time series would be recorded. (SRb – SRbhold) ≥ ST or Finally, daily 24 hour MSF at 06 UTC, (SRb – SRbhold) ≤ ST and hourly aviation Metars taken by a NAV CANADA contract weather observer were ∆ (SOG for the SRc) if recorded. The 24 hour MSF, which is defined (SRc – SRchold) ≥ ST or as the “True Value” in this study, will be (SRc – SRchold) ≤ ST compared to the SF values outputted by the algorithm in order to validate the algorithm’s When at least 2 of the 3 SR-50’s meet “goodness”. It should be noted that the SF these criteria, the following procedure is measurements taken by the observer at a performed. If the ∆ SOG levels are negative, different part of the airport compound, so the the three SRholds and the Geonorhold are answers may be comprised by this fact. reset to the new values measured at the last time step. The process then begins again with step 3.
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