AN EVALUATION OF TWO ULTRASONIC SNOW DEPTH SENSORS FOR POTENTIAL USE AT AUTOMATED SURFACE WEATHER OBSERVING SITES

The measurement of snowfall and its water content has yet to be perfected and continues to be a constant challenge to the National Weather Service and others involved in climate monitoring. Since the early 1980s, a technology utilizing sound waves to bounce off the snow surface to estimate changes in snow depth has been tested. Depth sensors have been manufactured and sold for several years. The U.S. Department of Agriculture Natural Resources Conservation Service has successfully deployed hundreds of these depth sensors over the past few years to measure and report snowdepth from remote mountainous areas in the western U.S. A similar instrument has also been deployed recently at many weather stations in Canada. These sensors provide very useful information on snowdepth at remote weather stations especially in areas with deep and continuous snow cover. However, up until now, these depth sensors have not been tested or used extensively to measure snow depth at any of the National Weather Service's U.S. weather stations.

In 2003 the National Weather Service funded a study to evaluate snow depth sensor technology for possible adaptation at ASOS and cooperative weather stations. A major collaborative intercomparison study was undertaken during the winter of 2004-2005 to quantitatively compare the results of ultrasonic depth sensors to traditional manual observations. This paper presents preliminary results of this study and includes results from cooperative test sites from Maine to Minnesota and Arizona to Indiana. Both deep, continuous and shallow intermittent snowcover areas are being monitored. In addition to comparing site observations of total depth of snow on the ground, we are also developing algorithms to estimate new snowfall from incremental changes in total depth of snow on ground. Data are currently being analyzed, and preliminary results will be presented. The situations where the sensors perform best and worst will be described and compared. While there are some limitations, considerable progress has been made and this technology is showing good promise as an objective tool for measuring snow accumulation where no human observers are available.