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

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.

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

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.

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

2020 ◽  
Vol 21 (5) ◽  
pp. 1039-1050 ◽  
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.

scholarly journals Unshielded precipitation gauge collection efficiency with wind speed and hydrometeor fall velocity. Part I: modelling results

2020 ◽  
Author(s):  
Jeffery Hoover ◽  
Pierre E. Sullivan ◽  
Paul I. Joe ◽  
Michael E. Earle
Keyword(s):  
Wind Speed ◽  
Collection Efficiency ◽  
Navier Stokes ◽  
Lagrangian Analysis ◽  
Fall Velocity ◽  
Empirical Expression ◽  
Wind Speeds ◽  
Wet Snow ◽  
Precipitation Type ◽  
Precipitation Gauge

Abstract. A new method for assessing collection efficiency using wind speed and hydrometeor fall velocity is presented for the unshielded Geonor T-200B3 precipitation gauge based on computational fluid dynamics results. Time-averaged Navier–Stokes simulations with a k–e turbulence model were used to determine the airflow around the gauge for 0 to 10 m s−1 wind speeds. Hydrometeor trajectories and collection efficiencies were determined using Lagrangian analysis for spherical 10 hydrometeor fall velocities between 0.25 to 10 m s−1 for rain (0.01–3.9 mm diameter), wet snow (0.2–21 mm diameter), dry snow (0.2–7.1 mm diameter), and ice pellets (1.5–4.3 mm diameter). The model results demonstrate that gauge collection efficiency strongly depends on both wind speed and hydrometeor fall velocity. Collection efficiency differences for identical hydrometeor fall velocities are within 0.05 for wind speeds less than 4 m s−1, despite differences in hydrometeor type, diameter, density, and mass. An empirical expression for collection efficiency with dependence on wind speed and fall velocity is 15 presented based on the numerical results, giving a RMSE of 0.03 for dry snow, wet snow, and rain, for wind speeds between 0 and 10 m s−1. The use of fall velocity captures differences in collection efficiency due to different hydrometeor types and sizes, and can be broadly applied even where the precipitation type may be unknown or uncertain. Results are compared to previous models and good model agreement with experimental results is demonstrated in Part II.

scholarly journals Improvement of solid precipitation measurements using a hotplate precipitation gauge

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.

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

2021 ◽  
Author(s):  
Nicolas R. Leroux ◽  
Julie M. Thériault ◽  
Roy Rasmussen
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Transfer Functions ◽  
Collection Efficiency ◽  
Particle Diameter ◽  
Field Site ◽  
Solid Precipitation ◽  
Gauge Data ◽  
The Relationship ◽  
Measured Particle

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.

Dependence of Snow Gauge Collection Efficiency on Snowflake Characteristics

2012 ◽  
Vol 51 (4) ◽  
pp. 745-762 ◽  
Author(s):  
Julie M. Thériault ◽  
Roy Rasmussen ◽  
Kyoko Ikeda ◽  
Scott Landolt
Keyword(s):  
Wind Speed ◽  
Theoretical Study ◽  
Collection Efficiency ◽  
Weather Conditions ◽  
Systematic Errors ◽  
Lagrangian Model ◽  
Field Observations ◽  
Research Fields ◽  
Different Types ◽  
Precipitation Gauge

AbstractAccurate snowfall measurements are critical for a wide variety of research fields, including snowpack monitoring, climate variability, and hydrological applications. It has been recognized that systematic errors in snowfall measurements are often observed as a result of the gauge geometry and the weather conditions. The goal of this study is to understand better the scatter in the snowfall precipitation rate measured by a gauge. To address this issue, field observations and numerical simulations were carried out. First, a theoretical study using finite-element modeling was used to simulate the flow around the gauge. The snowflake trajectories were investigated using a Lagrangian model, and the derived flow field was used to compute a theoretical collection efficiency for different types of snowflakes. Second, field observations were undertaken to determine how different types, shapes, and sizes of snowflakes are collected inside a Geonor, Inc., precipitation gauge. The results show that the collection efficiency is influenced by the type of snowflakes as well as by their size distribution. Different types of snowflakes, which fall at different terminal velocities, interact differently with the airflow around the gauge. Fast-falling snowflakes are more efficiently collected by the gauge than slow-falling ones. The correction factor used to correct the data for the wind speed is improved by adding a parameter for each type of snowflake. The results show that accurate measure of snow depends on the wind speed as well as the type of snowflake observed during a snowstorm.

scholarly journals Snow Particle Collection Efficiency and Adjustment Curves for the Hotplate© Precipitation Gauge

2021 ◽  
Author(s):  
Arianna Cauteruccio ◽  
Enrico Chinchella ◽  
Mattia Stagnaro ◽  
Luca G. Lanza
Keyword(s):  
Wind Speed ◽  
Bluff Body ◽  
Stokes Equations ◽  
Collection Efficiency ◽  
Navier Stokes ◽  
Strong Wind ◽  
Navier Stokes Equations ◽  
Relevant Role ◽  
Tracking Model ◽  
Precipitation Gauge

AbstractThe hotplate precipitation gauge operates by means of a thermodynamic principle. It is composed by a small size disk with two thin aluminium 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 modelled 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.

Evaluation of wind-induced errors for the Hotplate precipitation gauge using computational fluid dynamic simulations.

2020 ◽  
Author(s):  
Enrico Chinchella ◽  
Arianna Cauteruccio ◽  
Mattia Stagnaro ◽  
Andrea Freda ◽  
Luca Giovanni Lanza
Keyword(s):  
Particle Size ◽  
Wind Speed ◽  
Velocity Field ◽  
Wind Velocity ◽  
Complex Geometry ◽  
Collection Efficiency ◽  
Fluid Dynamic ◽  
Strong Wind ◽  
Precipitation Gauge ◽  
Number Of Particles

<p>Wind is recognised as the major environmental source of error in precipitation measurements. For traditional catching type gauges, which are composed by a funnel to collect the precipitation and a container with a bluff body shape, the exposure effect produces the updraft and acceleration of the velocity field in front and above of the collector. These divert the trajectories of approaching hydrometeors producing  a relevant under-catch, which increases with increasing the wind velocity. This problem has been recently addressed in the literature using Computational Fluid Dynamics (CFD) simulations and a Lagrangian Particle Tracking (LPT) model to provide correction curves for various instruments, which closely match the under-catch observed in field measurements.</p><p>The present work concentrates on the Hotplate precipitation gauge developed at the Research Applications Laboratory, National Center for Atmospheric Research in Boulder, Colorado. The Hotplate differs from the traditional catching type gauges because it operates by means of an indirect thermodynamic principle. Therefore, it is not equipped with any funnel to collect the precipitation and is composed by a small disk with a diameter of 13 cm with two thin aluminium heated plates on the upper and lower faces. On the plates three concentric rings are installed to prevent the hydrometeors from sliding off during strong wind conditions.</p><p>In order to quantify the wind-induced error, the Unsteady Reynolds Averaged Navier Stokes (URANS) equations were numerically solved, with a k-ω SST turbulence closure model, to calculate the airflow velocity field around the instrument. Numerical results were validated by comparison with wind tunnel flow velocity measurements from pressure probes and a Particle Image Velocimetry (PIV) technique.</p><p>Then, with the objective to calculate the Collection Efficiency (CE) the hydrometeor trajectories were modelled using a literature LPT model (Colli et al. 2015) that solves the particle motion equation under the effects of gravity and wind. The path of each particle was analysed, considering the complex geometry of the gauge body, to establish whether it is captured by the instrument or not.</p><p>For various particle size/wind velocity combinations, the ratio between the number of particles captured by the instrument and the number of particles that would be captured if the instrument was transparent to the wind was calculated. Finally, the CE curve was derived assuming a suitable particle size distribution for solid precipitation.</p><p>The results show that the Hotplate gauge presents a very unique response to the wind if compared with more traditional instruments. The CE indeed decreases with increasing the wind speed up to 7.5 m/s, where the effect of geometry starts to overcome the aerodynamic effect, and slowly reverses the trend beyond that value. This effect is so prominent at high wind speed that slightly beyond 15 m/s the under-catch fully disappears and the instrument starts to exhibit a rapidly increasing over-catching bias.</p><p><strong>References:</strong></p><p>Colli, M., Lanza, L.G., Rasmussen, R., Thériault, J.M., Baker, B.C. & Kochendorfer, J. An improved trajectory model to evaluate the collection performance of snow gauges.  Journal of Applied Meteorology and Climatology, 2015, 54, 1826–1836.</p>

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

2017 ◽  
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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