Despite the apparent simplicity, it is notoriously difficult to measure rainfall accurately because of the challenging environment within which it is measured. Systematic bias caused by wind is inherent in rainfall measurement and introduces an inconvenient unknown into hydrological science that is generally ignored. This paper examines the role of rain gauge shape and mounting height on catch efficiency (CE), where CE is defined as the ratio between nonreference and reference rainfall measurements. Using a pit gauge as a reference, we have demonstrated that rainfall measurements from an exposed upland site, recorded by an adjacent conventional cylinder rain gauge mounted at 0.5 m, were underestimated by more than 23% on average. At an exposed lowland site, with lower wind speeds on average, the equivalent mean undercatch was 9.4% for an equivalent gauge pairing. An improved‐aerodynamic gauge shape enhanced CE when compared to a conventional cylinder gauge shape. For an improved‐aerodynamic gauge mounted at 0.5 m above the ground, the mean undercatch was 11.2% at the upland site and 3.4% at the lowland site. The mounting height of a rain gauge above the ground also affected CE due to the vertical wind gradient near to the ground. Identical rain gauges mounted at 0.5 and 1.5 m were compared at an upland site, resulting in a mean undercatch of 11.2% and 17.5%, respectively. By selecting three large rainfall events and splitting them into shorter‐duration intervals, a relationship explaining 81% of the variance was established between CE and wind speed.

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The airflow surrounding any catching‐type rain gauge when impacted by wind is deformed by the presence of the gauge body, resulting in the acceleration of wind above the orifice of the gauge, which deflects raindrops and snowflakes away from the collector (the wind‐induced undercatch). The method of mounting a gauge with the collector at or below the level of the ground, or the use of windshields to mitigate this effect, is often not practicable. The physical shape of a gauge has a significant impact on its collection efficiency. In this study, we show that appropriate “aerodynamic” shapes are able to reduce the deformation of the airflow, which can reduce undercatch. We have employed computational fluid‐dynamic simulations to evaluate the time‐averaged airflow realized around “aerodynamic” rain gauge shapes when impacted by wind. Terms of comparison are provided by the results obtained for two standard “conventional” rain gauge shapes. The simulations have been run for different wind speeds and are based on a time‐averaged Reynolds‐Averaged Navier‐Stokes model. The shape of the aerodynamic gauges is shown to have a positive impact on the time‐averaged airflow patterns observed around the orifice compared to the conventional shapes. Furthermore, the turbulent air velocity fields for the aerodynamic shapes present “recirculating” structures, which may improve the particle‐catching capabilities of the gauge collector.

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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.