Search Results (5)

The impact of wind on precipitation measurements from the OTT Parsivel² optical transmission disdrometer is quantified using computational fluid dynamics simulations. The numerical velocity field around the instrument body and above the instrument sensing area (the laser beam) shows significant disturbance that depends heavily on the wind direction. By computing the trajectories of raindrops approaching the instrument, the wind-induced bias is quantified for a wide range of environmental conditions. Adjustments are derived in terms of site-independent catch ratios, which can be used to correct measurements in post-processing. The impact on two integral rainfall variables, the rainfall intensity and radar reflectivity, is calculated in terms of collection and radar retrieval efficiency assuming a sample drop size distribution. For rainfall intensity measurements, the OTT Parsivel² shows significant bias, even much higher than the wind-induced bias typical of catching-type rain gauges. Large underestimation is shown for wind parallel to the laser beam, while limited bias occurs for wind perpendicular to it. The intermediate case, with wind at 45°, presents non negligible overestimation. Proper alignment of the instrument with the laser beam perpendicular to the prevailing wind direction at the installation site and the use of windshields may significantly reduce the overall wind-induced bias. Full article

This paper delves into precisely measuring liquid levels using a specific methodology with diverse real-world applications such as process optimization, quality control, fault detection and diagnosis, etc. It demonstrates the process of liquid level measurement by employing a chaotic observer, which senses multiple variables within a system. A three-dimensional computational fluid dynamics (CFD) model is meticulously created using ANSYS to explore the laminar flow characteristics of liquids comprehensively. The methodology integrates the system identification technique to formulate a third-order state–space model that characterizes the system. Based on this mathematical model, we develop estimators inspired by Lorenz and Rossler’s principles to gauge the liquid level under specified liquid temperature, density, inlet velocity, and sensor placement conditions. The estimated results are compared with those of an artificial neural network (ANN) model. These ANN models learn and adapt to the patterns and features in data and catch non-linear relationships between input and output variables. The accuracy and error minimization of the developed model are confirmed through a thorough validation process. Experimental setups are employed to ensure the reliability and precision of the estimation results, thereby underscoring the robustness of our liquid-level measurement methodology. In summary, this study helps to estimate unmeasured states using the available measurements, which is essential for understanding and controlling the behavior of a system. It helps improve the performance and robustness of control systems, enhance fault detection capabilities, and contribute to dynamic systems’ overall efficiency and reliability. Full article

Accurate measurement of solid precipitation ($\mathrm{S}$) has a critical importance for proper understanding of the Earth’s hydrological cycle, validation of emerging technologies and weather prediction models, and developing parameterizations of severe weather elements such as visibility ($\mathrm{V}\mathrm{i}\mathrm{s}$). However, measuring $\mathrm{S}$ is still a challenging problem, due mainly to wind effects. The wind effects are normally mitigated by using a Double-Fence Automated Reference (DFAR) system to reduce the wind speed (${\mathrm{U}}_{\mathrm{g}}$). To contribute towards addressing some of these problems, we have analyzed datasets collected at two sites, Center for Atmospheric Research Experiments (CARE) and Bratt’s Lake, located in southern Ontario and southern Saskatchewan, Canada, respectively, using several instruments. The instruments at CARE include two Geonor gauges, one placed inside a DFAR (${\mathrm{S}}_{\mathrm{D}\mathrm{F}\mathrm{A}\mathrm{R}})$ and the other inside a double Alter shield ($\mathrm{D}\mathrm{A}{\mathrm{S}}_{\mathrm{G}}$), a Pluvio2 gauge inside a single Alter shield ($\mathrm{S}\mathrm{A}{\mathrm{S}}_{\mathrm{P}}$), a HotPlate, a PARSIVEL2 disdrometer that measures S and fall velocity ($\mathrm{V}$), and an FD12P senor that measures S and type and Vis. The instruments deployed in Bratt’s Lake includes a similar DFAR system and DAS Pluvio2 gauge. The results show that for the ${\mathrm{U}}_{\mathrm{g}}$ observed in this study (${\mathrm{U}}_{\mathrm{g}}$ < 6 ms⁻¹), both $\mathrm{D}\mathrm{A}{\mathrm{S}}_{\mathrm{G}}$ and $\mathrm{S}\mathrm{A}{\mathrm{S}}_{\mathrm{P}}$ have similar collection efficiency (CE) of near 70%. The transfer functions (TF) for $\mathrm{D}\mathrm{A}{\mathrm{S}}_{\mathrm{G}}$ and $\mathrm{S}\mathrm{A}{\mathrm{S}}_{\mathrm{P}}$ as a function of ${\mathrm{U}}_{\mathrm{g}}$ and also ${\mathrm{U}}_{\mathrm{g}}$, and $\mathrm{V}$ were derived. The TF developed for the $\mathrm{D}\mathrm{A}{\mathrm{S}}_{\mathrm{G}}$ that includes both ${\mathrm{U}}_{\mathrm{g}}$ and $\mathrm{V}$ showed better agreement with observation than just ${\mathrm{U}}_{\mathrm{g}}$ alone. The TF developed for $\mathrm{D}\mathrm{A}{\mathrm{S}}_{\mathrm{G}}$ at CARE site was tested using the data collected in Bratt’s Lake and correlated well (R = 0.86), but slightly overestimated the S accumulation by about 12%. The S measured at CARE site using all the other instruments were correlated well with${\mathrm{S}}_{\mathrm{D}\mathrm{F}\mathrm{A}\mathrm{R}}$ (R = 0.9), but the PARSIVEL2 and FD12P overestimated and underestimated the snow amount, respectively, as compared the ${\mathrm{S}}_{\mathrm{D}\mathrm{F}\mathrm{A}\mathrm{R}}$. However, the HotPlate captured similar amount of $\mathrm{S}$ as the ${\mathrm{S}}_{\mathrm{D}\mathrm{F}\mathrm{A}\mathrm{R}}$. According to this study, the ${\mathrm{S}}_{\mathrm{D}\mathrm{F}\mathrm{A}\mathrm{R}}$ showed good correlation with Vis. Full article

Precipitation is among the most important meteorological variables for, e.g., meteorological, hydrological, water management and climate studies. In recent years, non-catching precipitation gauges are increasingly adopted in meteorological networks. Despite such growing diffusion, calibration procedures and associated uncertainty budget are not yet standardized or prescribed in best practice documents and standards. This paper reports a metrological study aimed at proposing calibration procedures and completing the uncertainty budgets, to make non-catching precipitation gauge measurements traceable to primary standards. The study is based on the preliminary characterization of different rain drop generators, specifically developed for the investigation. Characterization of different models of non-catching rain gauges is also included. Full article

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. Full article