Wind Tunnel Validation of a Particle Tracking Model to Evaluate the Wind‐Induced Bias of Precipitation Measurements

Cauteruccio, Arianna; Brambilla, Elia; Stagnaro, Mattia; Lanza, Luca G.; Rocchi, Daniele

doi:10.1029/2020wr028766

Cited by 13 publications

(11 citation statements)

References 38 publications

(85 reference statements)

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“…The gauge body, immersed in a wind field, behaves like a bluff-body obstacle to the undisturbed airflow, producing strong velocity gradients, vertical components, and the development of turbulence close to the gauge surface, see, e.g., [4,5]. The hydrometeor trajectories are diverted by the velocity field around the instrument [6] depending on their diameter, the gauge shape, wind speed, and wind direction. The induced change in the number of hydrometeors that cross the sensing volume/collecting area of the gauge (for NCGs and CGs, respectively) can lead, in windy conditions, to an over or under estimation of the precipitation amount and intensity (see, e.g., [7]).…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Wind-Induced Airflow Pattern Near the Thies LPM Precipitation Gauge

Chinchella

Cauteruccio

Stagnaro

et al. 2021

Sensors

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The airflow velocity pattern generated by a widely used non-catching precipitation gauge (the Thies laser precipitation monitor or LPM) when immersed in a wind field is investigated using computational fluid dynamics (CFD). The simulation numerically solves the unsteady Reynolds-averaged Navier–Stokes (URANS) equations and the setup is validated against dedicated wind tunnel measurements. The adopted k-ω shear stress transport (SST) turbulence model closely reproduces the flow pattern generated by the complex, non-axisymmetric outer geometry of the instrument. The airflow pattern near the measuring area varies with the wind direction, the most intense recirculating flow and turbulence being observed when the wind blows from the back of the instrument. Quantitative parameters are used to discuss the magnitude of the airflow perturbations with respect to the ideal configuration where the instrument is transparent to the wind. The generated airflow pattern is expected to induce some bias in operational measurements, especially in strong wind conditions. The proposed numerical simulation framework provides a basis to develop correction curves for the wind-induced bias of non-catching gauges, as a function of the undisturbed wind speed and direction.

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Section: Introductionmentioning

confidence: 99%

Investigation of the Wind-Induced Airflow Pattern Near the Thies LPM Precipitation Gauge

Chinchella

Cauteruccio

Stagnaro

et al. 2021

Sensors

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“…Resorting to a CFD modeling approach with embedded liquid and solid particle tracking algorithms allows simulating both the aerodynamic behavior of each instrument and the resulting collection performance. The approach, originally proposed by Nešpor and Sevruk (1999), and improved 2015) is described in Cauteruccio, Chinchella, et al (2021) for solid precipitation and in Cauteruccio et al (2021b) for liquid precipitation.…”

Section: Methodsmentioning

confidence: 99%

“…A new model was added to the source code, with drag coefficient equations implemented for various ranges of the particle Reynolds number, as established a priori among those proposed in the literature by Folland (1988) and formulated starting from data published by Beard and Chuang (1987) and Khvorostyanov and Curry (2005). The model already adopted and validated in the work of Cauteruccio et al (2021b) is here slightly improved by modifying the Reynolds number intervals (changing the threshold value from 400 to 320) to adjust the continuity of the drag function over the entire range.…”

Section: Methodsmentioning

confidence: 99%

“…The terminal velocity is obtained once the gravitational force and the aerodynamic force (due to drag) are in equilibrium, meaning that the particle cannot accelerate further. To maintain consistency with the numerical model, the terminal velocity was computed by using the drag coefficient previously shown and derived from the work of Cauteruccio et al (2021b).…”

Section: Particle Tracking Setupmentioning

confidence: 99%

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The Overall Collection Efficiency of Catching‐Type Precipitation Gauges in Windy Conditions

Cauteruccio,

Chinchella,

Lanza

2024

Water Resources Research

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The wind‐induced bias of catching‐type precipitation measurement instruments is quantified using Computational Fluid Dynamics with embedded liquid (raindrops) and solid (snowflakes) particle tracking. The performance of six common commercial gauges having different outer geometry is compared under a range of Precipitation Intensity (PI) and wind speed conditions by means of the numerically calculated catch ratios. Validation of the simulated aerodynamic behavior and its effect on water drop trajectories is provided by previous wind tunnel experiments. The functional dependence of the overall collection efficiency on the PI is derived as a quantitative measure of the instrument performance under a wind climatology with a uniform probability density function. Instruments with aerodynamic design produce a less pronounced diversion of hydrometeor trajectories—at any given size—than those having more traditional geometry. For liquid precipitation measurements, chimney‐shaped instruments rank low, while inverted conical and Nipher shielded instruments are quite performant solutions. The investigated quasi‐cylindrical gauges have intermediate behavior depending on their detailed aerodynamic features. For solid precipitation, all instruments rank low at light to moderate PI, except the Nipher shielded gauge. Results allow selecting the appropriate instrument for the local precipitation climatology at the measurement site and can be used to apply suitable adjustments to the measured precipitation.

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“…The cylindrical shape is typical of most tipping-bucket rain gauges that are employed operationally. Cauteruccio and Lanza (2020), based on the CFD velocity fields provided by Colli et al (2018), obtained the catch ratios of a typical cylindrical gauge using the Lagrangian Particle Tracking model already validated by Cauteruccio et al (2021a), and provided sample CE curves as a function of wind speed and rainfall intensity by using the PSD measured at an Italian test site by Caracciolo et al (2008).…”

Section: Introductionmentioning

confidence: 99%

Adjustment of one-minute rain gauge time series using co-located drop size distribution and wind speed measurements

Cauteruccio

Stagnaro

Lanza

et al. 2023

Preprint

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Abstract. 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. 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) 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.

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Wind Tunnel Validation of a Particle Tracking Model to Evaluate the Wind‐Induced Bias of Precipitation Measurements

Cited by 13 publications

References 38 publications

Investigation of the Wind-Induced Airflow Pattern Near the Thies LPM Precipitation Gauge

Investigation of the Wind-Induced Airflow Pattern Near the Thies LPM Precipitation Gauge

The Overall Collection Efficiency of Catching‐Type Precipitation Gauges in Windy Conditions

Adjustment of one-minute rain gauge time series using co-located drop size distribution and wind speed measurements

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