Improvement of Snowgauge Collection Efficiency through a knowledge of solid precipitation fallspeed

AbstractThe collection efficiency (CE) of a typical gauge-shield configuration decreases with increasing wind speed, with a high scatter for a given wind speed. The scatter in the CE for a given wind speed arises in part from the variability in the characteristics of falling snow and atmospheric turbulence. This study uses weighing gauge data collected at the Marshall Field Site near Boulder, Colorado during the WMO Solid Precipitation InterComparison Experiment (SPICE) to show that the scatter in the collection efficiency can be reduced by considering the fallspeed of solid precipitation particle types. Particle diameter and fallspeed data from a laser disdrometer were used to arrive at this conclusion. In particular, the scatter in the CE of an unshielded snow gauge and a single Alter shield snow gauge is shown to be largely produced by the variation in measured particle fallspeed. The CE was divided into two classes depending on the measured mean-event particle fallspeed. Slower-falling particles were associated with a lower CE. A new transfer function (i.e. the relationship between CE and other meteorological variables, such as wind speed or air temperature) that includes the fallspeed of the hydrometeors was developed. The RMSE of the adjusted precipitation with respect to a weighing gauge placed in a Double Fence Intercomparison Reference was lower than using previously developed transfer functions. This shows that the measured fallspeed of solid precipitation with a laser disdrometer accounts for a large amount of the observed scatter in weighing gauge collection efficiency.

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Using Snowfall Intensity to Improve the Correction of Wind-Induced Undercatch in Solid Precipitation Measurements

10.5194/hess-2018-447 ◽

2018 ◽

Cited By ~ 1

Author(s):

Matteo Colli ◽

Mattia Stagnaro ◽

Luca Lanza ◽

Roy Rasmussen ◽

Julie M. Thériault

Keyword(s):

Wind Speed ◽

Field Experiments ◽

Cfd Simulation ◽

Transfer Functions ◽

Collection Efficiency ◽

Surface Air Temperature ◽

Test Site ◽

Precipitation Intensity ◽

Solid Precipitation ◽

Tracking Model

Abstract. Transfer functions are generally used to adjust for the wind-induced undercatch of solid precipitation measurements. These functions are derived based on the variation of the collection efficiency with wind speed for a particular type of gauge, either using field experiments or based on numerical simulation. Most studies use the wind speed alone, while others also include surface air temperature and/or precipitation type to try to reduce the scatter of the residuals at a given wind speed. In this study, we propose the use of the measured precipitation intensity to improve the effectiveness of the transfer function. This is achieved by applying optimized curve fitting to field measurements from the Marshall field-test site (CO, USA). The use of a non-gradient optimization algorithm ensures optimal binning of experimental data according to the parameter under test. The results reveal that using precipitation intensity as an explanatory variable significantly reduce the scatter of the residuals. The scatter reduction as indicated by the Root Mean Square Error (RMSE) is confirmed by the analysis of the recent quality controlled data from the WMO/SPICE campaign, showing that this approach can be applied to a variety of locations and catching-type gauges. We demonstrate the physical basis of the relationship between the collection efficiency and the measured precipitation intensity, due to the correlation of large particles with high intensities, by conducting a Computational Fluid-Dynamics (CFD) simulation. We use a Reynolds Averaged Navier-Stokes SST k-ω model coupled with a Lagrangian particle-tracking model. Results validate the hypothesis of using the measured precipitation intensity as a key parameter to improve the correction of wind-induced undercatch. Findings have the potential to improve operational measurements since no additional instrument other than a wind sensor is required to apply the correction. This improves the accuracy of precipitation measurements without the additional cost of ancillary instruments such as particle counters.

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Improvement of solid precipitation measurements using a hotplate precipitation gauge

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0168.1 ◽

2021 ◽

Author(s):

Julie M. Thériault ◽

Nicolas R. Leroux ◽

Roy Rasmussen

Keyword(s):

Wind Speed ◽

Transfer Functions ◽

Collection Efficiency ◽

Meteorological Conditions ◽

Speed Limit ◽

Mean Square ◽

Wind Speeds ◽

Solid Precipitation ◽

Precipitation Measurement ◽

Precipitation Gauge

AbstractAccurate snowfall measurement is challenging because it depends on the precipitation gauge used, meteorological conditions, and the precipitation microphysics. Upstream of weighing gauges, the flow field is disturbed by the gauge and any shielding used usually creates an updraft, which deflects solid precipitation from falling in the gauge resulting in significant undercatch. Wind shields are often used with weighing gauges to reduce this updraft and transfer functions are required to adjust the snowfall measurements to consider gauge undercatch. Using these functions reduce the bias in precipitation measurement but not the Root Mean Square Error (RMSE) (Kochendorfer et al. 2017a, b). The analysis performed in this study shows that the hotplate precipitation gauge bias after wind correction is near zero and similar to wind corrected weighing gauges but improves on the RMSE or scatter of the collection efficiency of weighing gauges for a given wind speed. To do this, the accuracy of the hotplate was compared to standard unshielded and shielded weighing gauges collected during the WMO SPICE program. The RMSE of the hotplate measurements is lower than weighing gauges (with or without an Alter shield) for wind speeds up to 5 m s-1; the wind speed limit at which sufficient data were available. This study shows that the hotplate precipitation measurement has a low bias and RMSE due to its aerodynamic shape, making its performance mostly independent of the type of solid precipitation.

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Adjustments for Wind-Induced Undercatch in Snowfall Measurements Based on Precipitation Intensity

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0222.1 ◽

2020 ◽

Vol 21 (5) ◽

pp. 1039-1050 ◽

Cited By ~ 1

Author(s):

Matteo Colli ◽

Mattia Stagnaro ◽

Luca G. Lanza ◽

Roy Rasmussen ◽

Julie M. Thériault

Keyword(s):

Wind Speed ◽

Field Experiments ◽

Transfer Functions ◽

Collection Efficiency ◽

Surface Air Temperature ◽

Field Measurements ◽

Precipitation Intensity ◽

Operational Conditions ◽

Explanatory Variables ◽

Precipitation Gauge

AbstractAdjustments for the wind-induced undercatch of snowfall measurements use transfer functions to account for the expected reduction of the collection efficiency with increasing the wind speed for a particular catching-type gauge. Based on field experiments or numerical simulation, collection efficiency curves as a function of wind speed also involve further explanatory variables such as surface air temperature and/or precipitation type. However, while the wind speed or wind speed and temperature approach is generally effective at reducing the measurement bias, it does not significantly reduce the root-mean-square error (RMSE) of the residuals, implying that part of the variance is still unexplained. In this study, we show that using precipitation intensity as the explanatory variable significantly reduces the scatter of the residuals. This is achieved by optimized curve fitting of field measurements from the Marshall Field Site (Colorado, United States), using a nongradient optimization algorithm to ensure optimal binning of experimental data. The analysis of a recent quality-controlled dataset from the Solid Precipitation Intercomparison Experiment (SPICE) campaign of the World Meteorological Organization confirms the scatter reduction, showing that this approach is suitable to a variety of locations and catching-type gauges. Using computational fluid dynamics simulations, we demonstrate that the physical basis of the reduction in RMSE is the correlation of precipitation intensity with the particle size distribution. Overall, these findings could be relevant in operational conditions since the proposed adjustment of precipitation measurements only requires wind sensor and precipitation gauge data.

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Errors and adjustments for single-Alter shielded and unshielded weighing gauge precipitation measurements from WMO-SPICE

10.5194/hess-2016-684 ◽

2017 ◽

Cited By ~ 2

Author(s):

John Kochendorfer ◽

Rodica Nitu ◽

Mareile Wolff ◽

Eva Mekis ◽

Roy Rasmussen ◽

...

Keyword(s):

Wind Speed ◽

Transfer Functions ◽

Climatic Conditions ◽

Crystal Habit ◽

Solid Precipitation ◽

Precipitation Measurement ◽

The World ◽

Winter Time ◽

Precipitation Gauge ◽

Type Crystal

Abstract. Although precipitation has been measured for many centuries, precipitation measurements are still beset with significant inaccuracies. Solid precipitation is particularly difficult to measure accurately, and differences between winter-time precipitation measurements from different measurement networks or different regions can exceed 100 %. Using precipitation gauge results from the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), errors in precipitation measurement caused by gauge uncertainty, spatial variability in precipitation, hydrometeor type, crystal habit, and wind were quantified. The methods used to calculate gauge catch efficiency and correct known biases are described. Adjustments, in the form of transfer functions that describe catch efficiency as a function of air temperature and wind speed, were derived using measurements from eight separate WMO-SPICE sites for both unshielded and single-Alter shielded weighing precipitation gauges. The use of multiple sites to derive such adjustments makes these results unique and more broadly applicable to other sites with various climatic conditions. In addition, errors associated with the use of a single transfer function to correct gauge undercatch at multiple sites were estimated.

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Unshielded precipitation gauge collection efficiency with wind speed and hydrometeor fall velocity

Hydrology and Earth System Sciences ◽

10.5194/hess-25-5473-2021 ◽

2021 ◽

Vol 25 (10) ◽

pp. 5473-5491

Author(s):

Jeffery Hoover ◽

Michael E. Earle ◽

Paul I. Joe ◽

Pierre E. Sullivan

Keyword(s):

Wind Speed ◽

Transfer Functions ◽

Collection Efficiency ◽

Threshold Function ◽

Precipitation Event ◽

Bias Error ◽

Velocity Dependence ◽

Fall Velocity ◽

Dynamics Modelling ◽

Precipitation Gauge

Abstract. Collection efficiency transfer functions that compensate for wind-induced collection loss are presented and evaluated for unshielded precipitation gauges. Three novel transfer functions with wind speed and precipitation fall velocity dependence are developed, including a function from computational fluid dynamics modelling (CFD), an experimental fall velocity threshold function (HE1), and an experimental linear fall velocity dependence function (HE2). These functions are evaluated alongside universal (KUniversal) and climate-specific (KCARE) transfer functions with wind speed and temperature dependence. Transfer function performance is assessed using 30 min precipitation event accumulations reported by unshielded and shielded Geonor T-200B3 precipitation gauges over two winter seasons. The latter gauge was installed in a Double Fence Automated Reference (DFAR) configuration. Estimates of fall velocity were provided by the Precipitation Occurrence Sensor System (POSS). The CFD function reduced the RMSE (0.08 mm) relative to KUniversal (0.20 mm), KCARE (0.13 mm), and the unadjusted measurements (0.24 mm), with a bias error of 0.011 mm. The HE1 function provided a RMSE of 0.09 mm and bias error of 0.006 mm, capturing the collection efficiency trends for rain and snow well. The HE2 function better captured the overall collection efficiency, including mixed precipitation, resulting in a RMSE of 0.07 mm and bias error of 0.006 mm. These functions are assessed across solid and liquid hydrometeor types and for temperatures between −22 and 19 ∘C. The results demonstrate that transfer functions incorporating hydrometeor fall velocity can dramatically reduce the uncertainty of adjusted precipitation measurements relative to functions based on temperature.

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Measuring precipitation with a geolysimeter

10.5194/hess-2017-174 ◽

2017 ◽

Cited By ~ 1

Author(s):

Craig D. Smith ◽

Garth van der Kamp ◽

Lauren Arnold ◽

Randy Schmidt

Keyword(s):

Transfer Functions ◽

Water Pressure ◽

Pressure Change ◽

Precipitation Event ◽

Surface Loading ◽

Rainfall Events ◽

Solid Precipitation ◽

Precipitation Gauge ◽

Observation Wells ◽

The Relationship

Abstract. Using the relationship between measured groundwater pressures in deep observation wells with total surface loading, a geological weighing lysimeter (geolysimeter) has the capability of measuring precipitation event totals independent of conventional precipitation gauge observations. Correlations between ground water pressure change and event precipitation were observed at a co-located site near Duck Lake, SK over a multi-year and multi-season period. Correlations varied from 0.99 for rainfall to 0.94 for snowfall. The geolysimeter was shown to underestimate rainfall by 7 % while overestimating snowfall by 9 % as compared to the unadjusted gauge precipitation. It is speculated that the underestimation of rainfall is due to unmeasured runoff and evapotranspiration within the sensing area of the geolysimeter during larger rainfall events while the overestimation of snow is at least partially due to the systematic undercatch common to most precipitation gauges due to wind. Using recently developed transfer functions from the World Meteorological Organization's (WMO) Solid Precipitation Intercomparison Experiment (SPICE), bias adjustments were applied to the Alter shielded, Geonor T-200B precipitation gauge measurements of snowfall to mitigate wind induced errors. The bias between the gauge and geolysimeter measurements was reduced to 3 %. This suggests that the geolysimeter is capable of accurately measuring solid precipitation, and can be used as an independent and representative reference of true precipitation.

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Measuring precipitation with a geolysimeter

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5263-2017 ◽

2017 ◽

Vol 21 (10) ◽

pp. 5263-5272 ◽

Cited By ~ 3

Author(s):

Craig D. Smith ◽

Garth van der Kamp ◽

Lauren Arnold ◽

Randy Schmidt

Keyword(s):

Transfer Functions ◽

Correlation Coefficients ◽

Pressure Change ◽

Precipitation Event ◽

Surface Loading ◽

Solid Precipitation ◽

Run Off ◽

Precipitation Gauge ◽

Observation Wells ◽

The Relationship

Abstract. Using the relationship between measured groundwater pressures in deep observation wells and total surface loading, a geological weighing lysimeter (geolysimeter) has the capability of measuring precipitation event totals independently of conventional precipitation gauge observations. Correlations between groundwater pressure change and event precipitation were observed at a co-located site near Duck Lake, SK, over a multi-year and multi-season period. Correlation coefficients (r2) varied from 0.99 for rainfall to 0.94 for snowfall. The geolysimeter was shown to underestimate rainfall by 7 % while overestimating snowfall by 9 % as compared to the unadjusted gauge precipitation. It is speculated that the underestimation of rainfall is due to unmeasured run-off and evapotranspiration within the response area of the geolysimeter during larger rainfall events, while the overestimation of snow is at least partially due to the systematic undercatch common to most precipitation gauges due to wind. Using recently developed transfer functions from the World Meteorological Organization's (WMO) Solid Precipitation Intercomparison Experiment (SPICE), bias adjustments were applied to the Alter-shielded, Geonor T-200B precipitation gauge measurements of snowfall to mitigate wind-induced errors. The bias between the gauge and geolysimeter measurements was reduced to 3 %. This suggests that the geolysimeter is capable of accurately measuring solid precipitation and can be used as an independent and representative reference of true precipitation.

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The Quantification and Correction of Wind-Induced Precipitation Measurement Errors

10.5194/hess-2016-415 ◽

2016 ◽

Author(s):

John Kochendorfer ◽

Roy Rasmussen ◽

Mareile Wolff ◽

Bruce Baker ◽

Mark E. Hall ◽

...

Keyword(s):

Wind Speed ◽

Air Temperature ◽

Measurement Errors ◽

Transfer Functions ◽

Universal Function ◽

Climate Change Research ◽

Wind Speeds ◽

Solid Precipitation ◽

The Us ◽

Precipitation Gauge

Abstract. Hydrologic measurements are becoming increasingly important for both the short and long term management of water resources. Of all the terms in the hydrologic budget, precipitation is the typically most important input. However, measurements of precipitation are still subject to large errors and biases. For example, a high-quality but unshielded weighing precipitation gauge can collect less than 50 % of the actual amount of solid precipitation when wind speeds exceed 5 ms−1. Using results from two different precipitation testbeds, such errors have been assessed for unshielded weighing gauges and for four of the most common windshields currently in use. Functions used to correct wind-induced undercatch were developed and tested. In addition, corrections for the single Altar weighing gauge were developed using the combined results of two separate sites, one of which was in Norway and other in the US. In general the results indicate that corrections described as a function of air temperature and wind speed effectively remove the undercatch bias that affects such precipitation measurements. In addition, a single ‘universal’ function developed for the single Altar gauges effectively removed the bias at both sites, with the bias at the US site improved from −12 % to 0 %, and the bias at the Norwegian site improved from −27 % to −3 %. These correction functions require only wind speed and air temperature, and were developed for use in national and local precipitation networks, hydrological monitoring, roadway and airport safety work, and climate change research. The techniques used to develop and test these transfer functions at more than one site can also be used for other more comprehensive studies, such as the WMO Solid Precipitation Intercomparison Experiment.

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Unshielded Precipitation Gauge Collection Efficiency with Wind Speed and Hydrometeor Fall Velocity. Part II: Experimental Results

10.5194/hess-2020-554 ◽

2020 ◽

Author(s):

Jeffery Hoover ◽

Michael E. Earle ◽

Paul I. Joe

Keyword(s):

Wind Speed ◽

Transfer Functions ◽

Collection Efficiency ◽

Measurement Data ◽

Precipitation Event ◽

Bias Error ◽

Fall Velocity ◽

Precipitation Occurrence ◽

Dynamics Modelling ◽

Precipitation Gauge

Abstract. Five collection efficiency transfer functions for unshielded precipitation gauges are presented that compensate for wind-induced collection loss. Three of the transfer functions presented are dependent on wind speed and precipitation fall velocity, and were derived through computational fluid dynamics modelling in Part 1 (CFD function) and from measurement data (HE1 function with fall velocity threshold and HE2 function with linear fall velocity dependence). These functions are evaluated alongside universal (KUniversal) and site-specific (KCARE) transfer functions with wind speed and temperature dependence. Their performance was assessed using 30-minute precipitation event accumulations reported by unshielded and shielded Geonor T-200B3 precipitation gauges over two winter seasons. The latter gauge was installed in a Double Fence Automated Reference (DFAR) configuration comprising a single-Alter shield within an octagonal, wooden double fence. Estimates of fall velocity were provided by a Precipitation Occurrence Sensor System (POSS). The CFD function reduced the RMSE (0.08 mm) relative to KUniversal, KCARE, and the unadjusted measurements, with a bias error of 0.011 mm. The HE1 function provided a RMSE of 0.09 mm and bias error of 0.006 mm, capturing well the collection efficiency trends for rain and snow. The HE2 function better captured the overall collection efficiency, including mixed precipitation, resulting in a RMSE of 0.07 mm and bias error of 0.006 mm. The improved agreement demonstrates the importance of fall velocity for collection efficiency.

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Using the measured Particle Size Distribution to assess the wind-induced bias of catching-type raingauges.

10.5194/egusphere-egu21-12651 ◽

2021 ◽

Author(s):

Mattia Stagnaro ◽

Arianna Cauteruccio ◽

Luca Giovanni Lanza ◽

Pak-Wai Chan

Keyword(s):

Particle Size ◽

Wind Speed ◽

Particle Size Distribution ◽

Size Distribution ◽

Drop Size ◽

Rainfall Intensity ◽

Collection Efficiency ◽

Particle Trajectories ◽

Large Variability ◽

Solid Precipitation

Wind-induced biases that affect catching-type precipitation gauges have been largely studied in the literature and dedicated experimental campaigns in the field were carried out to quantify this bias for both liquid and solid precipitation (including the recent WMO intercomparison on solid precipitation &#8211; SPICE). Experimental results show a large variability of the Collection Efficiency (CE) curves that depend on the precipitation type, intensity and the Particle Size Distribution (PSD) (see e.g. Colli et al. 2020). This was confirmed by recent studies using Computational Fluid Dynamic simulations to assess the airflow pattern around the gauge body and particle tracking models to simulate the particle trajectories when approaching the collector and calculating the Catch Ratio (CR) associated with various drop size - wind speed combinations (see e.g. Colli et al 2016, Cauteruccio and Lanza 2020).In the present study, the CR values derived from the work of Cauteruccio and Lanza (2020) for a catching-type cylindrical gauge as a function of the drop size were fitted with an inverse second-order polynomial. The parameters of such curves were themselves expressed as a function of the wind speed. This formulation was adopted to calculate the CE of a catching-type cylindrical gauge based on contemporary wind and PSD measurements. These were obtained at the field test site of the Hong Kong International Airport using six co-located anemometers and a two-dimensional video disdrometer (2DVD), at one-minute resolution. The obtained CE was used to correct the rainfall intensity measured by three catching-type cylindrical gauges, located at the same site, and was compared with the ratio between the raw data measured by the three cylindrical gauges and the 2DVD rainfall intensity measurements. Results show the improvement due to the correction and suggest that the 2DVD is subject to some wind-induced bias as well.References:Cauteruccio, A. and L. G. Lanza, 2020. Parameterization of the Collection Efficiency of a Cylindrical Catching-Type Rain Gauge Based on Rainfall Intensity. Water, 12(12), 3431.Colli, M., Lanza, L.G., Rasmussen, R. and J.M., Th&#233;riault, 2016. The Collection Efficiency of Shielded and Unshielded Precipitation Gauges. Part II: Modeling Particle Trajectories. Journal of Hydrometeorology, 17(1), 245-255.Colli, M., Stagnaro, M., Lanza, L.G., Rasmussen, R. and J.M., Th&#233;riault, 2020. Adjustments for Wind-Induced Undercatch in Snowfall Measurements based on Precipitation Intensity. Journal of Hydrometeorology, 21, 1039-1050.

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