Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study

Abstract

Abstract. Precipitation measurements exhibit large cold-season biases due to under-catch in windy conditions. These uncertainties affect water balance calculations, snowpack monitoring and calibrations of remote sensing algorithms and land surface models. More accurate data would improve the ability to predict future changes in water resources and mountain hazards in snow-dominated regions. In 2010, an extensive test-site for precipitation measurements was established at a mountain plateau in Southern Norway. Precipitation data of automatic gauges were compared with a precipitation gauge in a Double Fence Intercomparison Reference (DFIR) wind shield construction which served as the reference. Additionally, a large number of sensors were monitoring supportive meteorological parameters. In this paper, data from three winters were used to study and determine the wind-induced under-catch of solid precipitation. Qualitative analyses and Bayesian statistics were used to evaluate and objectively choose the model that is describing the data best. A continuous adjustment function and its uncertainty were derived for measurements of all types of winter precipitation (from rain to dry snow). A regression analysis did not reveal any significant misspecifications for the adjustment function, but showed that the chosen model uncertainty is slightly insufficient and can be further optimized. The adjustment function is operationally usable based only on data available at standard automatic weather stations. Our results show a non-linear relationship between under-catch and wind speed during winter precipitation events and there is a clear temperature dependency, mainly reflecting the precipitation type. The results allowed for the first time to derive an adjustment function with a data-tested validity beyond 7 m s−1 and proved a stabilisation of the wind-induced precipitation loss for higher wind speeds.

Numerical simulations of snowfall events: Sensitivity analysis of physical parameterizations

“…Underestimation of ∼20%–30% of total precipitation is typical for tipping‐bucket precipitation gauges, with wind‐induced undercatch the major source of error [ Villarini et al , ]. For solid precipitation, Wolff et al [] found a nonlinear relationship between wind speed and undercatch, with strong temperature dependence. There is also undercatch caused by evaporation and sublimation [ Rasmussen et al , ].…”

An Improved Trajectory Model to Evaluate the Collection Performance of Snow Gauges

“…measurements have been the subject of many studies (e.g., Alter 1937;Sevruk and Klemm 1989;Sevruk et al 1991;Larson 1993;Yang et al 1993;Goodison et al 1998;Sugiura et al 2003Sugiura et al , 2006, there have been only a limited number of coordinated assessments of the accuracy, reliability, and repeatability of automatic precipitation measurements (e.g., Tumbusch 2003;Duchon 2008;Smith 2009;Rasmussen et al 2012;Savina et al 2012;Wolff et al 2014).…”

“…This is comparable to the results found in Thériault et al (2012). Wolff et al (2014) found nonlinear mathematical relationships between collection efficiency and wind speed based on dry snow, mixed precipitation, and rain measurements made at the Haukeliseter (Norway) field site. In this work, a sigmoid law has been adopted to fit the simulated CE values for different particle size distributions in the following form:…”

On downscaling probabilities for heavy 24-hour precipitation events at seasonal-to-decadal scales

“…Another question is whether the low skill for m for Bjørnholt in winter can be attributed to more snow and less liquid rain, for instance under-catch (Wolff et al, 2014). Another explanation is that low values of m during winter means that the calibration with its annually aggregated value emphasises the warmer seasons with typically higher values.…”