Location:
Coordinates:
Elevation:
Coordinates:
Elevation:
Note: There may be discontinuity in the plotted data due to sensor calibration issues.
Note: It is helpful to view multiple time-scale plots when assessing snow depth.
Snow Data Interpretation and Quality Plots
- Plot of historical sensor snow depth., Many factors such as wind direction/speed, air temperature, and solar radiation influence the spatial distribution of snow on the landscape as well as the depth of the snow. Snow depth is automatically measured with an ultrasonic snow depth sensor. Blowing and drifting snow interferes with the sensor's ability to measure distance to the snow surface accurately. Field efforts include verification of sensor measurement accuracy and the measurement’s representation of the surrounding landscape. Automated measurements are verified by manually measuring the actual snow depth underneath the sensor. A snow course, making many manual measurements in an orderly pattern over a large spatial extent, is done to show how the snow depth reported by the sensor represents the surrounding area. A snow course consists of a total of 50 depth measurements, one measurement every 1-meter in an "L" shape pattern, 25 measurements on each leg.
The following links show static snow depth plots for educational, interpretative, and data quality purposes.
Sensor Photos
Sensor Operation
The SR50 snow depth sensor has an ultrasonic pulse measurement cone of 22 degrees from the bottom of the sensor to the target. The sensor measures the distance from the sensor to the target (in this case it is the ground surface or snow surface). To calculate snow depth, this distance to target measurement is subtracted from the field measured distance to ground surface. Currently, the sensor records measurements at one minute intervals and reports hourly averages. Potential inherent errors exist; since the speed of sound is affected by the temperature of the air it is traveling in, an air temperature measurement is required to correct the distance reading. Physically related errors include wind and blowing snow creating spurious data readings, difficulty in establishing a zero point due to tussocks, low shrubs, grass, etc., ground heave altering sensor height and angle, as well as cable breakages due to wildlife. Research is underway to improve the measurement with moving averages and weighting schemes.