Modelling the impact of high rise buildings in urban areas on precipitation initiation

The impact of urban areas upon precipitation distribution has been studied for many years. However, the relative importance of the distribution and type of surface morphology and urban heating remains unclear. A simple model of the surface sensible heat flux is used to explore the impact of urban heterogeneity. Sensitivity experiments are carried out to test the validity of the model, and experiments with a schematic urban morphology are used to investigate the impact of different types of building arrays. It is found that high-rise buildings over relatively small areas may have just as much impact as somewhat lower buildings covering a much larger area. The urban area produces considerable spatial variation in surface sensible heat flux. Data from a C-band radar located to the north of Greater Manchester provides evidence that convective cells may be initiated by the sensible heat flux input generated by the high-rise buildings in the city centre when the atmospheric boundary layer is unstable. Copyright © 2007 Royal Meteorological Societ

Effects of urbanization on precipitation in Beijing

Since the 1980s, the industrialization and urbanization of the Beijing area has entered a period of high-speed growth. This paper asks the question: How have such great changes in urban land-use over the past decades impacted urban precipitation? In this study, we investigate and analyze the effects of urbanization on the summer precipitation in Beijing using numerical modeling approaches. Applying the numerical mesoscale atmospheric model METRAS, we determine the impact of surface cover on 13 heavy precipitation events. We implement five idealized land-use scenarios: Reference scenario, No-urban scenario, High-building scenario, Urban-expand scenario, and No-vegetation scenario. There is nearly no difference in the mean precipitation sum across all 13 simulated rain events and between the urban-scenarios and the rural-scenario. We find effects of urbanization on precipitation only in some single cases. We conclude urbanization does effect the local precipitation of Beijing; it reduces rainfall in the urban area and increases rainfall downwind of the city. In some cases, larger percentage of sealed area could give rise to the heavier precipitation or extreme rain events. And we conclude the urban pattern significantly impacts rainfall area and intensity. Increased urban size or density may speed up rain clouds while increased urban height may disrupt or bifurcate the clouds. Our results offer a new viewpoint and further the study of urban impacts on precipitation (UIP). The results are important for sustainable and harmonious development of the economy, society, and environment in Beijing as well as other cities with rapid urbanization

A Comparison between Analog Ensemble and Convolutional Neural Network Empirical-Statistical Downscaling Techniques for Reconstructing High-Resolution Near-Surface Wind

Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamical downscaling in representing a high-resolution regional climate. Two distinct strategies of ESD are employed here to reconstruct near-surface winds in a region of rugged terrain. ESD is used to reconstruct the innermost grid of a multiply nested mesoscale model framework for regional climate downscaling. An analog ensemble (AnEn) and a convolutional neural network (CNN) are compared in their ability to represent near-surface winds in the innermost grid in lieu of dynamical downscaling. Downscaling for a 30 year climatology of 10 m April winds is performed for southern MO, USA. Five years of training suffices for producing low mean absolute error and bias for both ESD techniques. However, root-mean-squared error is not significantly reduced by either scheme. In the case of the AnEn, this is due to a minority of cases not producing a satisfactory representation of high-resolution wind, accentuating the root-mean-squared error in spite of a small mean absolute error. Homogeneous comparison shows that the AnEn produces smaller errors than the CNN. Though further tuning may improve results, the ESD techniques considered here show that they can produce a reliable, computationally inexpensive method for reconstructing high-resolution 10 m winds over complex terrain

A Comparison between Analog Ensemble and Convolutional Neural Network Empirical-Statistical Downscaling Techniques for Reconstructing High-Resolution Near-Surface Wind

Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamical downscaling in representing a high-resolution regional climate. Two distinct strategies of ESD are employed here to reconstruct near-surface winds in a region of rugged terrain. ESD is used to reconstruct the innermost grid of a multiply nested mesoscale model framework for regional climate downscaling. An analog ensemble (AnEn) and a convolutional neural network (CNN) are compared in their ability to represent near-surface winds in the innermost grid in lieu of dynamical downscaling. Downscaling for a 30 year climatology of 10 m April winds is performed for southern MO, USA. Five years of training suffices for producing low mean absolute error and bias for both ESD techniques. However, root-mean-squared error is not significantly reduced by either scheme. In the case of the AnEn, this is due to a minority of cases not producing a satisfactory representation of high-resolution wind, accentuating the root-mean-squared error in spite of a small mean absolute error. Homogeneous comparison shows that the AnEn produces smaller errors than the CNN. Though further tuning may improve results, the ESD techniques considered here show that they can produce a reliable, computationally inexpensive method for reconstructing high-resolution 10 m winds over complex terrain

Comparison of two turbulence parameterisations for the simulation of the concentration variance dispersion

In this work, we compare two different parameterisations for the wind velocity–component standard deviations. The first one is the (Hanna 1982) parameterisation, while the second is the (Scire et al. 2000) parameterisation, which provide the proper values and vertical structure for the wind standard deviations in the convective, neutral and stable layers, needed as input the Lagrangian stochastic model SPRAYWEB. The results of the model simulations carried out using the two parameterisations are compared, in terms of both mean concentration and concentration standard deviation, by evaluating some statistical indexes and trough scatter- and qq-plots

Rapid Filamentation Zones in Intense Tropical Cyclones

Abstract Intense tropical cyclones often possess relatively little convection around their cores. In radar composites, this surrounding region is usually echo-free or contains light stratiform precipitation. While subsidence is typically quite pronounced in this region, it is not the only mechanism suppressing convection. Another possible mechanism leading to weak-echo moats is presented in this paper. The basic idea is that the strain-dominated flow surrounding an intense vortex core creates an unfavorable environment for sustained deep, moist convection. Strain-dominated regions of a tropical cyclone can be distinguished from rotation-dominated regions by the sign of S21 + S22 − ζ2, where S1 = ux − υy and S2 = υx + uy are the rates of strain and ζ = υx − uy is the relative vorticity. Within the radius of maximum tangential wind, the flow tends to be rotation-dominated (ζ2 > S21 + S22), so that coherent structures, such as mesovortices, can survive for long periods of time. Outside the radius of maximum tangential wind, the flow tends to be strain-dominated (S21 + S22 > ζ2), resulting in filaments of anomalous vorticity. In the regions of strain-dominated flow the filamentation time is defined as τfil = 2(S21 + S22 − ζ2)−1/2. In a tropical cyclone, an approximately 30-km-wide annular region can exist just outside the radius of maximum tangential wind, where τfil is less than 30 min and even as small as 5 min. This region is defined as the rapid filamentation zone. Since the time scale for deep moist convective overturning is approximately 30 min, deep convection can be significantly distorted and even suppressed in the rapid filamentation zone. A nondivergent barotropic model illustrates the effects of rapid filamentation zones in category 1–5 hurricanes and demonstrates the evolution of such zones during binary vortex interaction and mesovortex formation from a thin annular ring of enhanced vorticity