Thunderstorms, winds, downdraft, downburst, vertical profile, flutuaction, extreme value. The model can be applied in any region of interest, once the relevant meteorological variables are known, to simulate the excitation due to TS winds in the design of transmission lines, long-span crossings, cable-stayed bridges, towers or similar structures The present model predicts horizontal velocity profiles that depend on the distance to the storm center, effect not considered by previous models, although the various proposals are globally compatible.

The resulting vertical velocity profiles were also studied and compared with existing models, such as the profiles proposed by Vicroy, et al.

When the translation of the TS is caused by an EPS, a squall line is produced, causing the highest wind velocities associated with TS events. A background wind is also considered, in order to account for the translational velocity of the thunderstorm, normally due to local wind conditions. All parameters employed in the model are related to meteorological variables, which are susceptible of statistical assessment. The model allows the generation of a spatially and temporally variable velocity field, which also includes a fluctuating component of the velocity. The model is based on a semi empirical representation of an axially-symmetrical flow line pattern that describes a stationary field, modulated by a function that accounts for the evolution of the wind velocity with time. In this paper a model that describes the three-dimensional wind velocity field originated from a downburst in a thunderstorm (TS) is proposed. Wind effects due to other phenomena, such as thunderstorms, and its combination with EPS winds in so-called squall lines, are simply neglected. Codes for structural design usually assume that the incident mean wind velocity is parallel to the ground, which constitutes a valid simplification for frequent winds caused by meteorological phenomena such as Extratropical Storms (EPS) or Tropical Storms. This index is a resultant of all the selected indices, and this indicator demonstrates that GBTP is capable of providing the most performance improvement in both training (IM train = 9.2%) and testing (IM train = 11.3%) phases, comparing with a well-known formula in this context.Wind action is a factor of fundamental importance in the structural design of light or slender constructions. To make a quantified comparison between the established and literature models, an index, called improvement index (IM), is computed. Based on the obtained performance indices and some graphical techniques, the research findings confirm that a significant enhancement in prediction performance is achieved through the proposed GBTP compared with the previously developed formulas in the literature. A number of performance indices are calculated in training and testing phases to compare the developed models with the conventional regression-based equations available in the literature. In this way, the generalized likelihood uncertainty estimation (GLUE) is used to develop two parametric equations, called GLUE-based four-parameter and GLUE-based two-parameter (GBTP) models to enhance the prediction accuracy of the velocity at the limit of deposition. This study provides application of a novel stochastic-based model to predict the densimetric Froude number in sewer pipes.

Sedimentation in storm sewers strongly depends on velocity at limit of deposition.

Velocity storm it