Improving Observations of Precipitation Type at the Surface: A 5-year Verification of a Radar-derived Product from the United Kingdom Met Office.

This study aims to verify the skill of a radar-based surface precipitation type (SPT) product with observations on the ground. Social and economic impacts can occur from SPT because it is not well forecast or observed. Observations from the United Kingdom Meteorological Office’s weather radar network are combined with post-processed numerical weather prediction (NWP) freezing level heights in a Boolean logic algorithm to create a 1 km resolution cartesian-gridded map of SPT. Here 5 years of discrete non-probabilistic outputs of rain, mixed phase, and snow are compared against surface observations made by trained observers, automatic weather stations, and laser disdrometers. The novel skill verification method developed as part of this study employs several tolerances of space and time from the SPT product, indicating the precision of the product for a desired accuracy. In general the results indicate that the tolerance verification method works well and produces reasonable statistical score ranges grounded in physical constraints. Using this method, we find that the mixed precipitation class is the least well diagnosed which is due to a negative bias in the input temperature height field, resulting in rain events frequently being classified as mixed. Snowis capturedwell by the product which is entirely reliant upon a post-processed NWP temperature field, although a single period of anomalously cold temperatures positively skewed snow scores with low-skill events. Furthermore, we conclude that more verification consistency is needed amongst studies to help identify successful approaches and thus improve SPT forecasts.

A Simplified Approach to Tolerance Verifications for Cylindrical Surfaces Using Point-Set Data

Cylindrical surface and its tolerance verification play important role in machining process. Although there exists many approaches that can fit the maximum, minimum and minimum zone cylinders, the cylinder fitting problems can even be further simplified. The methodology developed in this paper seeks to reduce the number of parameters used in cylinder fitting model using the projection model. The model evaluates 3-D tolerance specifications by introducing the optimal direction of projection such that the 2-D point projected onto this direction has optimal tolerance specifications (maximum, minimum and minimum zone circles). Besides, a global optimum solver, Particle Swarm Optimization (PSO) is applied to avoid the problem of finding local optimum. The proposed simplified method shows consistent results when compared with the results from literature.

Geometric Tolerance Verification - Innovative Evaluation of Facade Surface Flatness Using TLS (Terrestrial Laser Scanning)

This article assesses the application of TLS when evaluating the façade surface flatness on finished facade. Current methods of geometrical tolerance verification are reviewed and the results are compared data obtained from point clouds (using TLS).The process of currently used methods consist of manual verification of flatness by spirit level in accordance with national standards, which specifies exact places to be assessed and the number of measurements. With TLS technology we assess the surface quality and flatness more precisely, faster and moreover, investigate 100% of area. This may lead to better evaluation of the relative position of investigated areas, points and constructions. However, a further investigation, which would define the methodology for evaluation the geometric tolerance from point clouds is needed.

A Library of Feature Fitting Algorithms for GD&T Verification of Planar and Cylindrical Features

Conformation of a manufactured feature to the applied geometric tolerances is done by analyzing the point cloud that is measured on the feature. To that end, a geometric feature is fitted to the point cloud and the results are assessed to see whether the fitted features lies within the specified tolerance limits or not. Coordinate Measuring Machines (CMMs) use feature fitting algorithms that incorporate least square estimates as a basis for obtaining minimum, maximum, and zone fits. However, a comprehensive set of algorithms addressing the fitting procedure (all datums, targets) for every tolerance class is not available. Therefore, a Library of algorithms is developed to aid the process of feature fitting, and tolerance verification. This paper addresses linear, planar, circular, and cylindrical features only. This set of algorithms described conforms to the international Standards for GD&T. In order to reduce the number of points to be analyzed, and to identify the possible candidate points for linear, circular and planar features, 2D and 3D convex hulls are used. For minimum, maximum, and Chebyshev cylinders, geometric search algorithms are used. Algorithms are divided into three major categories: least square, unconstrained, and constrained fits. Primary datums require one sided unconstrained fits for their verification. Secondary datums require one sided constrained fits for their verification. For size and other tolerance verifications we require both unconstrained and constrained fits.

Measurements and Characterization of Photovoltaic Modules for Tolerance Verification

One of the most important aspects of photovoltaic modules is reliability for future uses, that is, a certain module will last certain number of years in use (generally 30 or 35 years). Reliability yields from excellent qualification tests on photovoltaic (PV) modules. Testing for reliability identifies unknown failure mechanisms and whether modules are susceptible to known failure mechanisms. This paper illustrates techniques of outdoor measurements and qualification characterization to know PV module conditions for commercial uses. Matrix methods are used for energy prediction. Failure material tests, using digital imaging and thermography, have also been conducted.