Solving the hydrodynamical equations in urban canopies often requires substantial computational resources. This is especially the case when tackling urban wind comfort issues. In this article, a novel and efficient technique for predicting wind velocity is discussed. Reynolds-averaged Navier–Stokes (RANS) simulations of the Michaelstadt wind tunnel experiment and the Tel Aviv center are used to supervise a machine learning function. Using the machine learning function it is possible to observe wind flow patterns in the form of eddies and spirals emerging from street canyons. The flow patterns observed in urban canopies tend to be predominantly localized, as the machine learning algorithms utilized for flow prediction are based on local morphological features.

Despite an initial success, Israel’s quarantine-isolation COVID-19 policy has abruptly collapsed. This study’s aim is to identify the causes that led to this exponential rise in the accumulation of confirmed cases. Epidemiological investigation reports were used to reconstruct chains of transmission as well as assess the net contribution of local infections relative to imported cases, infected travelers arriving from abroad. A mathematical model was implemented in order to describe the efficiency of the quarantine-isolation policy and the inflow of imported cases. The model’s simulations included two scenarios for the actual time series of the symptomatic cases, providing insights into the conditions that lead to the abrupt change. The abrupt change followed a Jewish holiday, Purim, in which many public gatherings were held. According to the first scenario, the accumulation of confirmed cases before Purim was driven by imported cases resulting in a controlled regime, with an effective reproduction number, Re, of 0.69. In the second scenario, which followed Purim, a continuous rise of the local to imported cases ratio began, which led to an exponential growth regime characterized by an Re of 4.34. It was found that the change of regime cannot be attributed to super-spreader events, as these consisted of approximately 5% of the primary cases, which resulted in 17% of the secondary cases. A general lesson for health policymakers should be that even a short lapse in public responsiveness can lead to dire consequences.

Lagrangian stochastic models are widely used to predict and analyze turbulent dispersion in complex environments, such as in various terrestrial and marine canopy flows. However, due to a lack of empirical data, it is still not understood how particular features of highly inhomogeneous canopy flows affect the Lagrangian statistics. In this work, we study Lagrangian short-time statistics by analyzing empirical Lagrangian trajectories in subvolumes of space that are small in comparison with the canopy height. For the analysis we used 3D Lagrangian trajectories measured in a dense canopy flow model in a wind-tunnel, using an extended version of real-time 3D particle tracking velocimetry. One of our key results is that the random turbulent fluctuations due to the intense dissipation were more dominant than the flow's inhomogeneity in affecting the short-time Lagrangian statistics. This amounts to a so-called quasihomogeneous regime of Lagrangian statistics at small scales. Using the Lagrangian dataset, we calculate the Lagrangian autocorrelation function and the second-order Lagrangian structure-function and extract associated parameters, namely, a Lagrangian velocity decorrelation timescale, $T_i$, and the Kolmogorov constant, $C_0$. We demonstrate that in the quasihomogeneous regime, both these functions are well represented using a second-order Lagrangian stochastic model that was designed for homogeneous flows. Furthermore, we show that the spatial variations of the Lagrangian separation of scales, $T_i/{\tau }_{\eta }$, and the Kolmogorov constant, $C_0$, cannot be explained by the variation of the Reynolds number, ${\text{Re}}_{\lambda }$, in space, and that $T_i/{\tau }_{\eta }$ was small as compared with homogeneous turbulence predictions at similar ${\text{Re}}_{\lambda }$. We thus hypothesize that these characteristics occurred due to the injection of kinetic energy at small scales due to the so-called “wake-production” process, and we show empirical results supporting our hypothesis. These findings shed light on key features of Lagrangian statistics in flows with intense dissipation, and have direct implications for modeling short term dispersion in such complex environments.

A reliable forecast of the wind vector at a given site is an important problem with a broad scope of application. Many uses are in the field of wind power in which prediction of strong winds is important; other uses are in the field of civil engineering, where wind gusts are important to structural integrity. For predicting air pollution events or the hazard zone in case of toxic gas accidents the low wind scenarios are of the highest importance, especially under stable conditions. These low wind scenarios are the situations which meso‐scale numerical models encounter difficulties or are often neglected when dealing with wind power tasks. Additional drawbacks of meso‐scale numerical models in this context are their lack of site‐specific detail and the fact that they are very demanding numerically.The ‘similar days’ method presented here is a method for the prediction of winds at a given location which works well also for low and variable wind conditions. The method is very efficient numerically: typically, runtime is less than a minute on a single PC. This method is based on using the last few hours' measurements at the given site and comparing it to an historical database. A set of criteria is defined to determine the time series similarity. Those similar days are used to construct the forecast.The similar days method was applied to a total of 5 years of measurements collected in two different locations. Very good agreement was achieved between prediction and measurements. Copyright © 2009 Royal Meteorological Society

The accumulated particulate matter concentration at a given vertical column due to traffic sources in urban area has many important consequences. This task, however, imposes a major challenge, since the problem of realistic pollutant dispersion in an urban environment is a very demanding task, both theoretically and computationally. This is mainly due to the highly inhomogeneous three dimensional turbulent flow regime in the urban canopy roughness sublayer, which is far from “local equilibrium” between shear production and dissipation. We present here a mass-consistent urban Lagrangian stochastic model for pollutants dispersion, where the flow field is modeled using a hybrid approach by which we model the surface layer based on the typical turbulent scales, both of the canopy and in the surface layer inertial sub-layer. In particular it relies on representing the canopy aerodynamically as a porous medium by spatial averaging the equations of motion, with the assumption that the canopy is laterally uniform on a scale much larger than the buildings but smaller than the urban block/neighbourhood, i.e., at the sub-urban-block scale. Choosing the spatial representative averaging volume allows the averaged variables to reflect the characteristic vertical heterogeneity of the canopy but to smooth out smaller scale spatial fluctuations caused as air flows in between the buildings. This modeling approach serves as the base for a realistic and efficient methodology for the calculation of the accumulated concentration from multiple traffic sources for any vertical column in the urban area. The existence of multiple traffic sources impose further difficulty since the computational effort required is very demanding for practical uses. Therefore, footprint analysis screening was introduced to identify the relevant part of the urban area which contributes to the chosen column. All the traffic sources in this footprint area where merged into several areal sources, further used for the evaluation of the concentration profile. This methodology was implemented for four cases in the Tel Aviv metropolitan area based on several selected summer climatological scenarios. We present different typical behaviors, demonstrating combination of source structure, urban morphology, flow characteristics, and the resultant dispersion pattern in each case.