Quantifying and Mitigating Wind‐Induced Undercatch in Rainfall Measurements

Pollock, Michael; O’Donnell, Greg; Quinn, Paul; Dutton, Mark; Black, Andrew; Wilkinson, Mark E.; Colli, Matteo; Stagnaro, Mattia; Lanza, Luca G.; Lewis, Elizabeth; Kilsby, C. G.; O’Connell, P. E.

doi:10.1029/2017wr022421

Cited by 123 publications

(101 citation statements)

References 38 publications

(49 reference statements)

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“…For example, analysing some experimental observations in the field, Yang et al [27] considered that the impact of wind speed on the measurements of rain gauges can be negligible. But some other researchers found that the wind could induce undercatch of rain gauges [28,29]. Our evaluation result cannot support this viewpoint probably due to the relatively small wind speed (less than 5 m/s) observed during the experiments.…”

Section: Impact Of Wind Speed and Rainfall Intensity On Rg3-m Measurecontrasting

confidence: 75%

Preliminary Evaluation of the HOBO Data Logging Rain Gauge at the Chuzhou Hydrological Experiment Station, China

Zhou

Yong

Liu

et al. 2019

Advances in Meteorology

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As a tipping bucket rain gauge, the HOBO Data Logging Rain Gauge RG3-M (RG3-M) has been widely used for the field precipitation observation owing to its superiority of independent power supply by a small portable battery. To quantify the measurement accuracy of the RG3-M gauge, a standard Manual Gauge (MG) and eight other models of tipping bucket rain gauges were installed at the Chuzhou hydrological experiment station of China. In this study, we first compared and investigated the accumulated mounts of 18 rainfall events of two RG3-M gauges benchmarked by the standard MG. Then, five typical rainfall events were chosen to further analyse the observed accuracy of the RG3-M gauge for different rainfall intensities at hourly temporal scale. Finally, the impacts of wind speed and rainfall intensity on the precipitation measurements of the RG3-M gauge were preliminarily explored. Results indicate that the RG3-M gauge measurement generally underestimates rainfall approximately −4% against the standard MG observation, but the maximum deviation even reaches −12.87%. In terms of the hourly rainfall process, the reliable measurement scope of the RG3-M gauge is ranging from 1.5 to 3 mm/h; however, it should be noted that the underestimation is rather significant at the higher rainfall rates (>6 mm/h). Last, it was found that rainfall intensity is a nonnegligible factor for influencing the measurement of the RG3-M gauge. But the windy effect seems to be insignificant in our experiments, which might be attributed to the similar exposure of the compared gauges.

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Section: Impact Of Wind Speed and Rainfall Intensity On Rg3-m Measurecontrasting

confidence: 75%

Preliminary Evaluation of the HOBO Data Logging Rain Gauge at the Chuzhou Hydrological Experiment Station, China

Zhou

Yong

Liu

et al. 2019

Advances in Meteorology

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“…The turbulent kinetic energy, k , provides an indication of the level of turbulence generated by the rain gauge shape. Using real‐world boundary conditions (Pollock et al, ) retains the wind speed characteristics that are enforced on the system in the field. These can be nonparameterized and scaled according to each input wind speed selected.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…p = 0 m 2 s −2 (where the pressure units are normalized with the air density). Turbulent kinetic energy . Calculated as

k = 3 / 2 {(I U_{w})}^{2}

with U w the undisturbed wind speed and I = 0.20 the turbulent intensity evaluated from field measurements (Pollock et al, ). Three‐dimensional wind measurements have been obtained using a Gill WindMaster ultrasonic anemometer at a resolution of 20 Hz.…”

Section: Methods Of Investigationmentioning

confidence: 99%

A Computational Fluid‐Dynamics Assessment of the Improved Performance of Aerodynamic Rain Gauges

Colli

Pollock

Stagnaro

et al. 2018

Water Resources Research

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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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“…CC BY 4.0 License. or more in conditions of extremely heavy rainfall rates over 50-100 mmh −1 (Nystuen, 1999;Sieck et al, 2007;Pollock et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

Schleiss¹,

Olsson²,

Berg³

et al. 2019

Preprint

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Abstract. Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by moving to higher resolution and making use of dual-polarization, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. But since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 hours. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, 6 different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events for each country were used to quantify the overall agreement between radar and gauges and the errors affecting the peaks. Results show that the overall agreement between radar and gauges in heavy rain is fair, with multiplicative biases in the order of 1.41–1.66 (i.e., radar underestimates by 29–39.8 %) and correlation coefficients of 0.71–0.83 across countries. However, the bias increases with intensity, reaching 45.9 %–66.2 % during the peaks. Only part of the bias (i.e., roughly 13 %–30 % depending on the radar product) can be explained by differences in measurement areas between gauges and radar. Radar products with higher spatial and temporal resolutions agreed better with the gauges, highlighting the importance of high-resolution radar for urban hydrology. However, for capturing peak intensity and reducing the bias during the most intense part of a storm, the ability to combine measurements from multiple overlapping radars to help mitigate attenuation seemed to play a more important role than resolution. The use of dual-polarization and phase information (e.g., Kdp) in the experimental Finnish OSAPOL product also seemed to provide a slight advantage in heavy rain. But improvements were hard to quantify and similarly good results were achieved in the Netherlands by applying a simple Z–R relation together with a mean field bias-correction.

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Quantifying and Mitigating Wind‐Induced Undercatch in Rainfall Measurements

Cited by 123 publications

References 38 publications

Preliminary Evaluation of the HOBO Data Logging Rain Gauge at the Chuzhou Hydrological Experiment Station, China

Preliminary Evaluation of the HOBO Data Logging Rain Gauge at the Chuzhou Hydrological Experiment Station, China

A Computational Fluid‐Dynamics Assessment of the Improved Performance of Aerodynamic Rain Gauges

The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden

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