Adjustments for Wind-Induced Undercatch in Snowfall Measurements Based on Precipitation Intensity

Abstract

Adjustments for the wind-induced undercatch of snowfall measurements use transfer functions to account for the expected reduction of the collection efficiency with increasing the wind speed for a particular catching-type gauge. Based on field experiments or numerical simulation, collection efficiency curves as a function of wind speed also involve further explanatory variables such as surface air temperature and/or precipitation type. However, while the wind speed or wind speed and temperature approach is generally effective at reducing the measurement bias, it does not significantly reduce the root-mean-square error (RMSE) of the residuals, implying that part of the variance is still unexplained. In this study, we show that using precipitation intensity as the explanatory variable significantly reduces the scatter of the residuals. This is achieved by optimized curve fitting of field measurements from the Marshall Field Site (Colorado, United States), using a nongradient optimization algorithm to ensure optimal binning of experimental data. The analysis of a recent quality-controlled dataset from the Solid Precipitation Intercomparison Experiment (SPICE) campaign of the World Meteorological Organization confirms the scatter reduction, showing that this approach is suitable to a variety of locations and catching-type gauges. Using computational fluid dynamics simulations, we demonstrate that the physical basis of the reduction in RMSE is the correlation of precipitation intensity with the particle size distribution. Overall, these findings could be relevant in operational conditions since the proposed adjustment of precipitation measurements only requires wind sensor and precipitation gauge data.

Calibration of non‐catching precipitation measurement instruments: A review

“…The gauge body, immersed in a wind field, behaves like a bluff‐body obstacle in the free flow, and produces strong velocity gradients, upwards or downwards components and turbulence close to the gauge (Cauteruccio et al, 2020). The hydrometeors trajectories are diverted by the velocity field around the instrument (Cauteruccio et al, 2021a; Cauteruccio et al, 2021b; Cauteruccio et al, 2021c; Colli et al, 2020; Folland, 1988; Jevons, 1861; Nešpor & Sevruk, 1999) and, depending on the gauge shape and wind speed, the number of hydrometeors that cross the sensing volume is affected, leading to an over‐ or under‐estimation of the precipitation intensity. The exposure effect therefore introduces an error, common to all precipitation gauges, that is simply due to the presence of the instrument itself (invasive measurement) and varies with the gauge shape, wind speed and direction, and the PSD.…”

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

“…Air temperature, which is assumed to be a good proxy for precipitation type diagnostic (e.g., Wolff et al 2013), is often added as another variable in transfer functions to further reduce the scatter in the data for a given wind speed (e.g., Chubb et al 2015;Wolff et al 2015;Kochendorfer et al 2017aKochendorfer et al ,b, 2018. Chubb et al (2015) and Colli et al (2020) used precipitation intensity in addition to wind speed and air temperature to correct precipitation measurements from weighing gauges. Including precipitation intensity further improves both the bias and the scatter in the data compared to using the wind speed and the air temperature alone to correct precipitation measurements.…”

On Neglecting Free-Stream Turbulence in Numerical Simulation of the Wind-Induced Bias of Snow Gauges

“…Additionally, we implemented in the modelling scheme the turbulence intensity levels obtained from field measurements, using a 3D sonic anemometer. Finally, we used a Lagrangian Particle Tracking (LPT) model to quantify the impact of the free-stream turbulence on the CE of the gauge, to demonstrate that the role of free-stream turbulence is modulated by the particle size distribution (PSD) of the specific precipitation event, and thus depends on the precipitation intensity, consistently with the latest literature developments [16,17].…”