Evaporation from weighing precipitation gauges: impacts on automated gauge measurements and quality assurance methods
- 1Cooperative Institute for Climate and Satellites (CICS) North Carolina State University and NOAA's National Climatic Data Center (NCDC), 151 Patton Ave., Asheville, NC 28801, USA
- 2NOAA's Atmospheric Turbulence Diffusion Division (ATDD), 456 S. Illinois Ave., Oak Ridge, TN 37830, USA
Abstract. Evaporation from a precipitation gauge can cause errors in the amount of measured precipitation. For automated weighing-bucket gauges, the World Meteorological Organization (WMO) suggests the use of evaporative suppressants and frequent observations to limit these biases. However, the use of evaporation suppressants is not always feasible due to environmental hazards and the added cost of maintenance, transport, and disposal of the gauge additive. In addition, research has suggested that evaporation prior to precipitation may affect precipitation measurements from auto-recording gauges operating at sub-hourly frequencies. For further evaluation, a field campaign was conducted to monitor evaporation and its impacts on the quality of precipitation measurements from gauges used at U.S. Climate Reference Network (USCRN) stations. Two Geonor gauges were collocated, with one gauge using an evaporative suppressant (referred to as Geonor-NonEvap) and the other with no suppressant (referred to as Geonor-Evap) to evaluate evaporative losses and evaporation biases on precipitation measurements. From June to August, evaporative losses from the Geonor-Evap gauge exceeded accumulated precipitation, with an average loss of 0.12 mm h−1. The impact of evaporation on precipitation measurements was sensitive to the choice of calculation method. In general, the pairwise method that utilized a longer time series to smooth out sensor noise was more sensitive to gauge evaporation (−4.6% bias with respect to control) than the weighted-average method that calculated depth change over a smaller window (<+1% bias). These results indicate that while climate and gauge design affect gauge evaporation rates, computational methods also influence the magnitude of evaporation biases on precipitation measurements. This study can be used to advance quality insurance (QA) techniques used in other automated networks to mitigate the impact of evaporation biases on precipitation measurements.