Experimental evidence of the wind-induced bias of precipitation gauges using particle image velocimetry and particle tracking in the wind tunnel

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

doi:10.1016/j.jhydrol.2021.126690

Cited by 21 publications

(28 citation statements)

References 15 publications

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“…Among the environmental sources of bias, wind is the most relevant one [2], causing the so-called exposure effect [3]. 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.…”

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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“…Comparing the precipitation measured by three calibrated tipping-bucket rain gauges with the actual rainfall depths, Shedekar et al (2016) found that the values measured by the three devices were significantly smaller, particularly for heavy rainfall. When it was windy, some raindrops (especially smaller ones) often missed the funnel or fell at an inclination because of wind-induced flow deflection, eddies, and turbulence around the gauges (Kochendorfer et al, 2017;Pollock et al, 2018;Cauteruccio et al, 2020;Colli et al, 2020). Finally, the catch efficiency of the gauge was biased from the real value, which was usually referred to as the wind-induced under-catch effect.…”

Section: Uncertaintiesmentioning

confidence: 99%

“…Previous evaluations have provided valuable information for the theoretical understanding and improvement of the IMERG algorithm and its practical applications (Tang et al, 2016;2018;Ma et al, 2020;Saouabe et al, 2020;Caloiero et al, 2021). However, there are still several gaps in the current evaluation of IMERG.…”

Section: Introductionmentioning

confidence: 99%

Systematic assessment of GPM IMERG V06 precipitation products with dense rain gauge observations over Zhejiang Province, China

Sun

Shen

et al. 2022

Intl Journal of Climatology

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This study systematically assessed the performance of the Integrated Multi-satellitE Retrievals (IMERG) for Global Precipitation Measurement V06, including the near-real-time "Late Run" (IMERG-L) and the post-real-time "Final Run" (IMERG-F), over Zhejiang Province (ZJP), China. The evaluation was conducted at daily and hourly timescales for a full year and for each season, based on dense rain gauge observations and continuous and categorical validation statistics. For the full year and for each season, IMERG-F outperformed IMERG-L in representing the spatial pattern of multiyear mean precipitation. For regional mean of ZJP, IMERG-F and IMERG-L overestimated the daily/hourly precipitation for the full year by 6.51 and 4.98%, respectively. Among seasons, the regional mean relative biases for IMERG-F were between 5.65 and 8.63%; however, for IMERG-L, they exhibited notable variations with a maximum of 11.09% in fall and a minimum of 0 in spring. Bias composition suggested that the regional mean overestimations were largely due to false bias for the full year and for each season, except in winter, wherein it was due to hit bias. Spatially, the biases for the full year and for each season commonly arose from false and hit biases at daily timescale, and from false and miss biases at hourly timescale. Based on the remaining continuous metrics (i.e., root-mean-square-error [RMSE], correlation coefficient [CC], and Kling-Gupta Efficiency [KGE]) and all categorical metrics, the IMERG daily/hourly performance was acceptable on regional and grid scales throughout the year and in all seasons. From a region-average perspective, IMERG-F outperformed IMERG-L according to CC, RMSE, and KGE, but both products showed the same performance overall based on all categorical metrics; most grids also share these characteristics. This study provides a valuable reference for IMERG developers to improve product accuracy from the perspective of the final postprocessing step and for potential users in ZJP.

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“…The role of the gauge outer geometry on the aerodynamic behaviour of the gauge was investigated by Colli et al (2018), by simulating four different geometries: cylindrical, chimney, and two different inverted conical shapes. The CFD velocity fields, obtained within a Reynolds Average Navier Stokes (RANS) modelling approach, revealed that gauges with inverted conical shape have better aerodynamic behaviour than the chimney shaped gauge, while the cylindrical gauge had and intermediate behaviour (see also Cauteruccio et al, 2021b). The cylindrical shape is typical of most tipping-bucket rain gauges that are employed operationally.…”

Section: Introductionmentioning

confidence: 95%

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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Experimental evidence of the wind-induced bias of precipitation gauges using particle image velocimetry and particle tracking in the wind tunnel

Cited by 21 publications

References 15 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

Systematic assessment of GPM IMERG V06 precipitation products with dense rain gauge observations over Zhejiang Province, China

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

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