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 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.

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 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

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 Spatial and temporal CCN variations in convection-permitting aerosol microphysics simulations in an idealised marine tropical domain

2017 ◽  
Vol 17 (5) ◽  
pp. 3371-3384 ◽  
Author(s):  
Céline Planche ◽  
Graham W. Mann ◽  
Kenneth S. Carslaw ◽  
Mohit Dalvi ◽  
John H. Marsham ◽  
...  
Keyword(s):  
Particle Size ◽  
High Resolution ◽  
Wind Speed ◽  
Large Scale ◽  
Dimethyl Sulfide ◽  
Climate Model ◽  
Sea Salt ◽  
Limited Area ◽  
Strong Wind ◽  
Aerosol Composition

Abstract. A convection-permitting limited area model with periodic lateral boundary conditions and prognostic aerosol microphysics is applied to investigate how concentrations of cloud condensation nuclei (CCN) in the marine boundary layer are affected by high-resolution dynamical and thermodynamic fields. The high-resolution aerosol microphysics–dynamics model, which resolves differential particle growth and aerosol composition across the particle size range, is applied to a domain designed to match approximately a single grid square of a climate model. We find that, during strongly convective conditions with high wind-speed conditions, CCN concentrations vary by more than a factor of 8 across the domain (5–95th percentile range), and a factor of  ∼  3 at more moderate wind speed. One reason for these large sub-climate-grid-scale variations in CCN is that emissions of sea salt and dimethyl sulfide (DMS) are much higher when spatial and temporal wind-speed fluctuations become resolved at this convection-permitting resolution (making peak wind speeds higher). By analysing how the model evolves during spin-up, we gain new insight into the way primary sea salt and secondary sulfate particles contribute to the overall CCN variance in these realistic conditions, and find a marked difference in the variability of super-micron and sub-micron CCN. Whereas the super-micron CCN are highly variable, dominated by strongly fluctuating sea spray emitted, the sub-micron CCN tend to be steadier, mainly produced on longer timescales following growth after new particle formation in the free troposphere, with fluctuations inherently buffered by the fact that coagulation is faster at higher particle concentrations. We also find that sub-micron CCN are less variable in particle size, the accumulation-mode mean size varying by  ∼  20 % (0.101 to 0.123 µm diameter) compared to  ∼  35 % (0.75 to 1.10 µm diameter) for coarse-mode particles at this resolution. We explore how the CCN variability changes in the vertical and at different points in the spin-up, showing how CCN concentrations are introduced both by the emissions close to the surface and at higher altitudes during strong wind-speed conditions associated to the intense convective period. We also explore how the non-linear variation of sea-salt emissions with wind speed propagates into variations in sea-salt mass mixing ratio and CCN concentrations, finding less variation in the latter two quantities due to the longer transport timescales inherent with finer CCN, which sediment more slowly. The complex mix of sources and diverse community of processes involved makes sub-grid parameterisation of CCN variations difficult. However, the results presented here illustrate the limitations of predictions with large-scale models and the high-resolution aerosol microphysics–dynamics modelling system shows promise for future studies where the aerosol variations will propagate through to modified cloud microphysical evolution.

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 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.

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

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

<p>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 – 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).</p><p>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.</p><p><strong>References:</strong></p><p>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.</p><p>Colli, M., Lanza, L.G., Rasmussen, R. and J.M., Thériault, 2016. The Collection Efficiency of Shielded and Unshielded Precipitation Gauges. Part II: Modeling Particle Trajectories. Journal of Hydrometeorology, 17(1), 245-255.</p><p>Colli, M., Stagnaro, M., Lanza, L.G., Rasmussen, R. and J.M., Thériault, 2020. Adjustments for Wind-Induced Undercatch in Snowfall Measurements based on Precipitation Intensity. Journal of Hydrometeorology, 21, 1039-1050.</p>

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