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The measurement accuracy of an electroacoustic precipitation sensor, the Vaisala WXT520, is investigated to quantify the associated wind-induced bias. The device is widely used as a noncatching tool for measuring the integral features of liquid precipitation, specifically rainfall amount and intensity. A numerical simulation using computational fluid dynamics is used to determine the bluff-body behavior of the instrument when exposed to wind. The obtained airflow velocity patterns near the sensor are initially validated in a wind tunnel. Then, the wind-induced deviation and acceleration/deceleration of individual raindrop trajectories and the resulting impact on the measured precipitation are replicated using a Lagrangian particle tracking model. The sensor's specific measurement principle necessitates redefining catch ratios and the collection efficiency in terms of the resulting kinetic energy and quantifying them as a function of particle Reynolds number and precipitation intensity, respectively. Wind speed and direction and drop size distribution have been simulated across various combinations. The results show that the measured precipitation is overestimated by up to 400% under the influence of wind. The presented adjustment curves can be used to correct raw rainfall measurements taken by the Vaisala WXT520 in windy conditions, either in real time or as a postprocessing function. The magnitude of the adjustment at any operational aggregation level largely depends on the local rainfall and wind regimes at the site of measurement and may have a strong impact on applications in regions where wind is frequent during low- to medium-intensity precipitation. [ABSTRACT FROM AUTHOR]

A procedure to adjust rainfall intensity (RI) measurements to account for the wind-induced measurement bias of traditional catching-type gauges is proposed and demonstrated with an application to a suitable case study. The objective is to demonstrate that adjustment curves derived from numerical simulations and disdrometer measurements allow for a posteriori correction of rainfall time series based on the wind velocity measurements alone. To quantify the impact of wind on long-term records, 1 min RI measurements from a cylindrical tipping-bucket rain gauge installed at the Hong Kong Observatory are adjusted. Catch ratios derived from the instrument's aerodynamic behaviour under varying wind speed and drop size combinations are obtained by fitting computational fluid-dynamic simulation results already available in the literature. Co-located high-resolution wind speed measurements from a cup and vane sensor and drop size distribution measurements from an optical video disdrometer (the 2DVD or two-dimensional video disdrometer) are used to infer the collection efficiency of the gauge as a function of wind speed and RI alone and to adjust raw data from a 4-year dataset (2018–2021) of 1 min RI measurements. Due to the specific local climatology, where strong wind is often associated with intense precipitation, 80 % of the dataset adjustments are limited to 4 % of the observed RI values. This, however, results in a significant amount of available freshwater resources that would be missing from the calculated hydrological and water management budget of the region should the adjustments be neglected. This work raises the need to quantify the impact of the wind-induced bias at other sites where disdrometer data support a characterisation of the relationship between the drop size distribution and the measured RI. Depending on the local rain and wind climatology, the correction may account for a significant portion of the annual rainfall amount. [ABSTRACT FROM AUTHOR]

A procedure to adjust rainfall intensity (RI) measurements to account for the wind-induced measurement bias of traditional catching-type gauges is proposed and demonstrated with application to a suitable case study. The objective is to demonstrate that adjustment curves derived from numerical simulation and disdrometer measurements allows for a-posteriori correction of rainfall time series based on the wind velocity measurements alone. One-minute RI measurements from a cylindrical tipping-bucket rain gauge installed at the Hong Kong Observatory are adjusted to quantify the impact of wind on long-term records. Catch ratios deriving from the instrument aerodynamic behaviour under varying wind speed and drop size combinations are obtained by fitting computational fluid-dynamic simulation results already available in the literature. Colocated high-resolution wind speed measurements from a cup and vane sensor and drop size distribution measurements from an optical video disdrometer (the 2DVD) are used to infer the collection efficiency of the gauge as a function of wind speed and RI alone, and to adjust raw data from a four-year dataset (2018-2021) of one-minute RI measurements. Due to the specific local climatology, where strong wind is often associated with intense precipitation, adjustments are limited to 4% of the RI values in 80% of the dataset. This however results in a significant amount of available freshwater resources that would be missing from the calculated hydrological and water management budget of the region should the adjustments be neglected. This work raises the need of quantifying the impact of the wind-induced bias in other sites where disdrometer data support characterizing the relationship between the drop size distribution and the measured RI. Depending on the local rain and wind climatology the correction may account for a significant portion of the annual rainfall amount. [ABSTRACT FROM AUTHOR]

Numerical studies of the wind-induced bias of precipitation measurements assume that turbulence is generated by the interaction of the airflow with the gauge body, while steady and uniform free-stream conditions are imposed. However, wind is turbulent in nature due to the roughness of the site and the presence of obstacles, therefore precipitation gauges are immersed in a turbulent flow. Further to the turbulence generated by the flow-gauge interaction, we investigated the natural free-stream turbulence and its influence on precipitation measurement biases. Realistic turbulence intensity values at the gauge collector height were derived from 3D sonic anemometer measurements. Large Eddy Simulations of the turbulent flow around a chimney-shaped gauge were performed under uniform and turbulent free-stream conditions, using geometrical obstacles upstream of the gauge to provide the desired turbulence intensity. Catch ratios for dry snow particles were obtained using a Lagrangian particle tracking model, and the collection efficiency was calculated based on a suitable particle size distribution. The collection efficiency in turbulent conditions showed stronger undercatch at the investigated wind velocity and snowfall intensity below 10 mm h−1, demonstrating that adjustment curves based on the simplifying assumption of uniform free-stream conditions do not accurately portray the wind-induced bias of snow measurements. [ABSTRACT FROM AUTHOR]