The quantification and correction of wind-induced precipitation measurement errors

Abstract

Abstract. Hydrologic measurements are important for both the short- and long-term management of water resources. Of the terms in the hydrologic budget, precipitation is typically the most important input; however, measurements of precipitation are subject to large errors and biases. For example, an all-weather unshielded weighing precipitation gauge can collect less than 50 % of the actual amount of solid precipitation when wind speeds exceed 5 m s−1. Using results from two different precipitation test beds, such errors have been assessed for unshielded weighing gauges and for weighing gauges employing four of the most common windshields currently in use. Functions to correct wind-induced undercatch were developed and tested. In addition, corrections for the single-Alter weighing gauge were developed using the combined results of two separate sites in Norway and the USA. In general, the results indicate that the functions effectively correct the undercatch bias that affects such precipitation measurements. In addition, a single function developed for the single-Alter gauges effectively decreased the bias at both sites, with the bias at the US site improving from −12 to 0 %, and the bias at the Norwegian site improving from −27 to −4 %. These correction functions require only wind speed and air temperature as inputs, and were developed for use in national and local precipitation networks, hydrological monitoring, roadway and airport safety work, and climate change research. The techniques used to develop and test these transfer functions at more than one site can also be used for other more comprehensive studies, such as the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE).

Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE

“…The precipitation detector measurements were included to provide independent verification of the occurrence of precipitation and to help accurately identify periods of precipitation. The rationale behind the use of 30 min intervals and a 0.25 mm accumulation threshold for precipitation events is detailed elsewhere (Kochendorfer et al, 2017). To summarize this rationale, the 0.25 mm threshold was found to reduce the effects of measurement noise on the selection of events, while the 30 min interval provided a large sample size of events.…”

“…At Marshall, the sonic anemometer measurements at a height of 10 m, which was one of two anemometers used to determine the 10 m wind speed, reported erratic measurements below 0.9 m s −1 and were removed. Following Kochendorfer et al (2017), the 10 m sonic anemometer measurements at Marshall were used only when the propeller anemometer measurements were affected by the wind shadow of the sonic anemometer.…”

“…Measurements from all sites, spanning two winter seasons from 2013-2015, were combined to develop robust multi-site transfer functions, marking a significant departure and improvement over past work, in which typically only one (Folland, 1988;Smith, 2009;Wolff et al, 2015) or sometimes two (Kochendorfer et al, 2017) sites were used to develop transfer functions. In addition, the resultant multi-site or "universal" transfer functions were tested on each site individually, revealing the magnitude of site-specific errors and biases for the different types of transfer functions developed.…”

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

“…Error in precipitation measurement at a gauge may be caused by various factors including, but not limited to, wind, splash, evaporation, mechanical failure, improper instrument calibration, a plugged gauge orifice, isolated objects (e.g., trees, buildings, and fences), observation errors, and other factors [41,42]. Because point measurement is fraught with complications, it is important to examine the quality of precipitation data before undertaking hydrologic analyses [41,42].…”

The Peñalara Mountain Meteorological Network (1999–2014): Description, Preliminary Results and Lessons Learned

“…This effect is even more pronounced if wind shields are not used. In past years, there has been some relevant investigations regarding the methods of measurement that will be able to achieve a lower uncertainty for snow precipitation and mountain environments like the WMO SPICE project [72][73][74] and also newer methods to decrease uncertainty of gridded mountain precipitation data sets [75].…”