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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]

The hotplate precipitation gauge operates by means of a thermodynamic principle. It is composed of a small disk with two thin aluminum heated plates on the upper and lower faces. Each plate has three concentric rings to prevent the hydrometeors from sliding off in strong wind. As for the more widely used tipping-bucket and weighing gauges, measurements are affected by the wind-induced bias due to the bluff-body aerodynamics of the instrument outer shape. Unsteady Reynolds-averaged Navier–Stokes equations were numerically solved, using a k–ω shear stress transport closure model, to simulate the aerodynamic influence of the gauge body on the airflow. Wind tunnel tests were conducted to validate simulation results. Solid particle trajectories were modeled using a Lagrangian particle tracking model to evaluate the influence of the airflow modification on the ability of the instrument to collect the incoming hydrometeors. A suitable parameterization of the particle size distribution, as a function of the snowfall intensity, was employed to calculate the collection efficiency (CE) under different wind conditions. Results reveal a relevant role of the three rings in enhancing the collection performance of the gauge. Below 7.5 m s−1, the CE curves linearly decrease with increasing the wind speed, while beyond that threshold, the blocking caused by the rings counter effects the aerodynamic induced undercatch, and the CE curves quadratically increase with the wind speed. At high wind speed, the undercatch vanishes and the instrument exhibits a rapidly increasing overcatch. For operational purposes, adjustment curves were formulated as a function of the wind speed and the measured snowfall intensity. [ABSTRACT FROM AUTHOR]

The aerodynamic response of snow gauges when exposed to the wind is responsible for a significant reduction of their collection performance. The modifications induced by the gauge and the windshield onto the space-time patterns of the undisturbed airflow deviate the snowflake trajectories. In Part I, the disturbed air velocity field in the vicinity of shielded and unshielded gauge configurations is investigated. In Part II, the airflow is the basis for a particle tracking model of snowflake trajectories to estimate the collection efficiency. A Geonor T-200B gauge inside a single Alter shield is simulated for wind speeds varying from 1 to 8 m s−1. Both time-averaged and time-dependent computational fluid dynamics simulations are performed, based on Reynolds-averaged Navier-Stokes (RANS) and large-eddy simulation (LES) models, respectively. A shear stress tensor k-Ω model (where k is the turbulent kinetic energy and Ω is the turbulent specific dissipation rate) is used for the RANS formulation and solved within a finite-volume method. The LES is implemented with a Smagorinsky subgrid-scale method that models the subgrid stresses as a gradient-diffusion process. The RANS simulations confirm the attenuation of the airflow velocity above the gauge when using a single Alter shield, but the generated turbulence above the orifice rim is underestimated. The intensity and spatial extension of the LES-resolved turbulent region show a dependency on the wind speed that was not detected by the RANS. The time-dependent analysis showed the propagation of turbulent structures and the impact on the turbulent kinetic energy above the gauge collecting section. [ABSTRACT FROM AUTHOR]

Recent studies have used numerical models to estimate the collection efficiency of solid precipitation gauges when exposed to the wind in both shielded and unshielded configurations. The models used computational fluid dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic response to the gauge-shield geometry. These are used as initial conditions to perform Lagrangian tracking of solid precipitation particles. Validation of the results against field observations yielded similarities in the overall behavior, but the model output only approximately reproduced the dependence of the experimental collection efficiency on wind speed. This paper presents an improved snowflake trajectory modeling scheme due to the inclusion of a dynamically determined drag coefficient. The drag coefficient was estimated using the local Reynolds number as derived from CFD simulations within a time-independent Reynolds-averaged Navier-Stokes approach. The proposed dynamic model greatly improves the consistency of results with the field observations recently obtained at the Marshall Field winter precipitation test bed in Boulder, Colorado. [ABSTRACT FROM AUTHOR]