Improvement of Snow Gauge Collection Efficiency through a Knowledge of Solid Precipitation Fall Speed

Abstract

The collection efficiency (CE) of a typical gauge-shield configuration decreases with increasing wind speed, with a high scatter for a given wind speed. The scatter in the CE for a given wind speed arises in part from the variability in the characteristics of falling snow and atmospheric turbulence. This study uses weighing gauge data collected at the Marshall Field Site near Boulder, Colorado during the WMO Solid Precipitation InterComparison Experiment (SPICE) to show that the scatter in the collection efficiency can be reduced by considering the fallspeed of solid precipitation particle types. Particle diameter and fallspeed data from a laser disdrometer were used to arrive at this conclusion. In particular, the scatter in the CE of an unshielded snow gauge and a single Alter shield snow gauge is shown to be largely produced by the variation in measured particle fallspeed. The CE was divided into two classes depending on the measured mean-event particle fallspeed. Slower-falling particles were associated with a lower CE. A new transfer function (i.e. the relationship between CE and other meteorological variables, such as wind speed or air temperature) that includes the fallspeed of the hydrometeors was developed. The RMSE of the adjusted precipitation with respect to a weighing gauge placed in a Double Fence Intercomparison Reference was lower than using previously developed transfer functions. This shows that the measured fallspeed of solid precipitation with a laser disdrometer accounts for a large amount of the observed scatter in weighing gauge collection efficiency.

The Overall Collection Efficiency of Catching‐Type Precipitation Gauges in Windy Conditions

“…This result was later confirmed by Leroux et al. (2021) and Hoover et al. (2021), who found a dependence of the undercatch bias on the hydrometeor fall velocity, being the fall velocity in the atmosphere basically controlled by the size (and shape) of the falling particles, therefore by the PI itself.…”

“…More recently, the physical dependence of the undercatch bias on the measured PI was demonstrated by Colli et al (2020) for solid precipitation and by for liquid precipitation. This result was later confirmed by Leroux et al (2021) and Hoover et al (2021), who found a dependence of the undercatch bias on the hydrometeor fall velocity, being the fall velocity in the atmosphere basically controlled by the size (and shape) of the falling particles, therefore by the PI itself.…”

Experimental Study on the Morphology of Snow Crystal Particles and Its Influence on Compacted Snow Hardness

“…For example, X-ray tomography can measure the porosity and specific surface area of snow samples, ignoring the fact that significant differences in the morphology of snow crystals also affect their physical properties [29]. Laser raindrop spectrometry can only measure the size of snow crystals falling naturally but cannot observe the metamorphic process of snow in the long term [30]. A uniform standard for defining snow crystal shape parameters is currently nonexistent.…”

Adjustment of one-minute rain gauge time series using co-located drop size distribution and wind speed measurements

“…The wind-induced bias of precipitation measurements obtained using catching type gauges dependsfurther than the instrument geometry and the wind speedon the size of individual particles and the associated fall velocity (Leroux et al, 2021). Meickle (1819) and Jevons (1861) were the first authors to highlight the aerodynamic behaviour of precipitation measurement instruments.…”