Observation of the Urban Wind Island Effect

Urban wind island effect (UWI) is defined as a phenomenon in which boundary layer mean wind speeds in an urban area are noticeably higher than its neighboring rural areas. Unlike urban heat island effect which has been extensively studied, the UWI was only recently observed in a modeling study. Here we study existence of the UWI over Indianapolis, Indiana using wind profile measurements from two Doppler wind lidars (DWL) that were deployed in climatologically upwind and downwind of the city. Under certain atmospheric conditions higher wind speeds and turbulence were observed at the downwind site over the entire urban boundary layer outside the urban canopy layer.

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The Impact of the Urban Heat Island during an Intense Heat Wave in Oklahoma City

Advances in Meteorology ◽

10.1155/2010/230365 ◽

2010 ◽

Vol 2010 ◽

pp. 1-10 ◽

Cited By ~ 78

Author(s):

Jeffrey B. Basara ◽

Heather G. Basara ◽

Bradley G. Illston ◽

Kenneth C. Crawford

Keyword(s):

Urban Heat Island ◽

Heat Wave ◽

Rural Areas ◽

Oklahoma City ◽

Apparent Temperature ◽

Heat Island ◽

Atmospheric Conditions ◽

Urban Heat ◽

The Impact ◽

Intense Heat

During late July and early August 2008, an intense heat wave occurred in Oklahoma City. To quantify the impact of the urban heat island (UHI) in Oklahoma City on observed and apparent temperature conditions during the heat wave event, this study used observations from 46 locations in and around Oklahoma City. The methodology utilized composite values of atmospheric conditions for three primary categories defined by population and general land use: rural, suburban, and urban. The results of the analyses demonstrated that a consistent UHI existed during the study period whereby the composite temperature values within the urban core were approximately C warmer during the day than the rural areas and over C warmer at night. Further, when the warmer temperatures were combined with ambient humidity conditions, the composite values consistently revealed even warmer heat-related variables within the urban environment as compared with the rural zone.

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Urban "Heat-Island Effect" and its Connection with Architectural and Climatic Features on the Example of Poltava

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.2.14598 ◽

2018 ◽

Vol 7 (3.2) ◽

pp. 597

Author(s):

Yuri Golik ◽

Oksana Illiash ◽

Nataliia Maksiuta

Keyword(s):

Urban Heat Island ◽

Traditional Method ◽

Urban Heat Island Effect ◽

Heat Island ◽

Island Effect ◽

Temperature Regimes ◽

Architectural Features ◽

Urban Heat ◽

The Given ◽

The City

The concept of "heat-island effect", its structure and features of formation over the city are given. The climatic and other features of the city that influence the formation of this phenomenon are mentioned. The data on functioning in the city of the municipal production enterprise of the heat economy is indicated. The traditional method for determining the formation of the urban "heat-island effect" is described. The data and comparative graphs on the temperature regimes of the city and region are presented. The possibility of influencing architectural features of the city on the formation of the "heat-island-effect" is determined. According to the obtained results, further integrated researches are proposed for obtaining reliable results of the given question.

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An Observational Case Study of Synergies between an Intense Heat Wave and the Urban Heat Island in Beijing

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0125.1 ◽

2020 ◽

Vol 59 (4) ◽

pp. 605-620 ◽

Cited By ~ 3

Author(s):

Ning An ◽

Jingjing Dou ◽

Jorge E. González-Cruz ◽

Robert D. Bornstein ◽

Shiguang Miao ◽

...

Keyword(s):

Boundary Layer ◽

Urban Heat Island ◽

Heat Wave ◽

Apparent Temperature ◽

Heat Index ◽

Heat Island ◽

Canopy Layer ◽

Urban Heat ◽

Peak Pattern ◽

Intense Heat

AbstractThe focus of this study is an intense heat episode that occurred on 9–13 July 2017 in Beijing, China, that resulted in severe impacts on natural and human variables, including record-setting daily electricity consumption levels. This event was observed and analyzed with a suite of local and mesoscale instruments, including a high-density automated weather station network, soil moisture sensors, and ground-based vertical instruments (e.g., a wind profiler, a ceilometer, and three radiometers) situated in and around the city, as well as electric power consumption data and analysis data from the U.S. National Centers for Environmental Prediction. The results show that the heat wave originated from dry adiabatic warming induced by the dynamic downslope and synoptic subsidence. The conditions were aggravated by the increased air humidity during subsequent days, which resulted in historically high records of the heat index (i.e., an index representing the apparent temperature that incorporates both air temperature and moisture). The increased thermal energy and decreased boundary layer height resulted in a highly energized urban boundary layer. The differences between urban and rural thermal conditions throughout almost the entire boundary layer were enhanced during the heat wave, and the canopy-layer urban heat island intensity (UHII) reached up to 8°C at a central urban station at 2300 local standard time 10 July. A double-peak pattern in the diurnal cycle of UHIIs occurred during the heat wave and differed from the single-peak pattern of the decadal average UHII cycles. Different spatial distributions of UHII values occurred during the day and night.

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Advantages of using a fast urban boundary layer model as compared to a full mesoscale model to simulate the urban heat island of Barcelona

Geoscientific Model Development ◽

10.5194/gmd-9-4439-2016 ◽

2016 ◽

Vol 9 (12) ◽

pp. 4439-4450 ◽

Cited By ~ 6

Author(s):

Markel García-Díez ◽

Dirk Lauwaet ◽

Hans Hooyberghs ◽

Joan Ballester ◽

Koen De Ridder ◽

...

Keyword(s):

Boundary Layer ◽

Simple Model ◽

Wind Speed ◽

Mesoscale Model ◽

Urban Climate ◽

Urban Canopy ◽

Forecast Model ◽

Heat Island ◽

Layer Model ◽

Urban Heat

Abstract. As most of the population lives in urban environments, the simulation of the urban climate has become a key problem in the framework of the climate change impact assessment. However, the high computational power required by high-resolution (sub-kilometre) fully coupled land–atmosphere simulations using urban canopy parameterisations is a severe limitation. Here we present a study on the performance of UrbClim, an urban boundary layer model designed to be several orders of magnitude faster than a full-fledged mesoscale model. The simulations are evaluated with station data and land surface temperature observations from satellites, focusing on the urban heat island (UHI). To explore the advantages of using a simple model like UrbClim, the results are compared with a simulation carried out with a state-of-the-art mesoscale model, the Weather Research and Forecasting Model, which includes an urban canopy model. This comparison is performed with driving data from ERA-Interim reanalysis (70 km). In addition, the effect of using driving data from a higher-resolution forecast model (15 km) is explored in the case of UrbClim. The results show that the performance of reproducing the average UHI in the simple model is generally comparable to the one in the mesoscale model when driven with reanalysis data (70 km). However, the simple model needs higher-resolution data from the forecast model (15 km) to correctly reproduce the variability of the UHI at a daily scale, which is related to the wind speed. This lack of accuracy in reproducing the wind speed, especially the sea-breeze daily cycle, which is strong in Barcelona, also causes a warm bias in the reanalysis driven UrbClim run. We conclude that medium-complexity models as UrbClim are a suitable tool to simulate the urban climate, but that they are sensitive to the ability of the input data to represent the local wind regime. UrbClim is a well suited model for impact and adaptation studies at city scale without high computing requirements, but does not replace the need for mesoscale atmospheric models when the focus is on the two-way interactions between the city and the atmosphere.

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Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing

International Journal of Remote Sensing ◽

10.1080/01431161.2012.716548 ◽

2012 ◽

Vol 34 (9-10) ◽

pp. 3177-3192 ◽

Cited By ~ 47

Author(s):

José A. Sobrino ◽

Rosa Oltra-Carrió ◽

Guillem Sòria ◽

Juan Carlos Jiménez-Muñoz ◽

Belén Franch ◽

...

Keyword(s):

Remote Sensing ◽

Urban Heat Island ◽

Urban Heat Island Effect ◽

Heat Island ◽

Thermal Remote Sensing ◽

Island Effect ◽

Urban Heat ◽

Surface Urban Heat Island ◽

The City

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The Influence of Vegetation on UHIs, MUHIs, and Microclimate of Selected Southern Cities

HortScience ◽

10.21273/hortsci.32.3.509a ◽

1997 ◽

Vol 32 (3) ◽

pp. 509A-509

Author(s):

Derald A. Harp ◽

Edward L. McWilliams

Keyword(s):

Rural Areas ◽

Urban Areas ◽

Building Materials ◽

Urban Heat Islands ◽

Shopping Malls ◽

Heat Islands ◽

Suburban Areas ◽

Winter Temperatures ◽

Urban Heat ◽

The City

Urban areas have average annual temperatures 2–3°C warmer than surrounding rural areas, with daily differences of 5–6°C common. A suggested reason for this temperature difference is the extensive use of concrete, asphalt, and other building materials in the urban environment. Vegetation can moderate these temperatures by intercepting incoming radiation. The influence of vegetation patterns on the magnitude of urban and micro-urban “heat islands” (UHI and MUHI, respectively) is compared for several cities including Houston, Austin, College Station, and Ft. Worth, Texas; Huntsville, Ala.; and Gainesville, Fla. Temperatures for all cities studied were greatest in the built-up areas and dropped off in suburban areas and adjacent rural areas. In Houston, surrounding rice fields were 3–5°C cooler than urban areas. Heavily built-up areas of Austin were 2–4°C warmer than parks and fields outside of the city. In all of the cities, large parks were typically 2–3°C cooler than adjacent built-up areas. Large shopping malls varied in nocturnal winter and summer temperature, with winter temperatures near door openings 2–3°C warmer, and summer daytime temperatures as much as 17°C cooler beneath trees. This effect seemed to persist at the microclimatic scale. Areas beneath evergreen trees and shrubs were warmer in the winter than surrounding grass covered areas. Video thermography indicated that the lower surfaces of limbs in deciduous trees were warmer than the upper surfaces. Overall, vegetation played a significant role, both at the local and microscale, in temperature moderation.

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Data and techniques for studying the urban heat island effect in Johannesburg

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-203-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 203-206 ◽

Cited By ~ 3

Author(s):

C. H. Hardy ◽

A. L. Nel

Keyword(s):

Remote Sensing ◽

Urban Heat Island ◽

Urban Areas ◽

Remote Sensing Data ◽

Urban Heat Island Effect ◽

Heat Island ◽

Sensing Data ◽

Island Effect ◽

Urban Heat ◽

The City

The city of Johannesburg contains over 10 million trees and is often referred to as an urban forest. The intra-urban spatial variability of the levels of vegetation across Johannesburg’s residential regions has an influence on the urban heat island effect within the city. Residential areas with high levels of vegetation benefit from cooling due to evapo-transpirative processes and thus exhibit weaker heat island effects; while their impoverished counterparts are not so fortunate. The urban heat island effect describes a phenomenon where some urban areas exhibit temperatures that are warmer than that of surrounding areas. The factors influencing the urban heat island effect include the high density of people and buildings and low levels of vegetative cover within populated urban areas. This paper describes the remote sensing data sets and the processing techniques employed to study the heat island effect within Johannesburg. In particular we consider the use of multi-sensorial multi-temporal remote sensing data towards a predictive model, based on the analysis of influencing factors.

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The urban heat island effect is closely related to cicada density in metropolitan Seoul

10.7287/peerj.preprints.3064v1 ◽

2017 ◽

Author(s):

Hoa Q. Nguyen ◽

Yuseob Kim ◽

Yikweon Jang

Keyword(s):

Urban Heat Island ◽

Rural Areas ◽

Urban Areas ◽

Underlying Mechanism ◽

Heat Island ◽

Population Densities ◽

Island Effect ◽

Significant Difference ◽

Order Of Magnitude ◽

Urban Heat

Background. Cryptotympana atrata and Hyalessa fuscata are the most abundant cicada species in the Korean Peninsula, where their population densities are higher in urban areas than in rural ones. The urban heat island (UHI) effect, wherein human activities cause urban areas to be significantly warmer than surrounding rural areas, may underlie this difference. We predicted a positive relationship between the degrees of UHI in urban areas and population densities of C. atrata and H. fuscata. Methods. To test this prediction, we examined cicada population densities in three groups: those of high and low UHI areas within metropolitan Seoul, and suburban areas. Enumeration surveys of cicada exuviae were conducted from July to August, 2015. Results. C. atrata and H. fuscata constituted almost 30% and 70% of the cicada populations, respectively, collected across all sampling localities. No significant difference in species composition was observed, regardless of groups, but the densities of the two species were significantly higher in urban areas with high UHI than in other groups. Specifically, densities of C. atrata in high UHI areas were approximately seven and four times higher compared to those in low UHI and in suburban groups, respectively. The order of magnitude was greater in H. fuscata, where densities in high UHI group were respectively 22 and six times higher than those in low UHI and in suburban groups. Discussion. These results suggest that the UHI effect may be closely linked to high cicada densities in metropolitan Seoul, although the underlying mechanism for this remains unclear.

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Birmingham’s air and surface urban heat islands associated with Lamb weather types and cloudless anticyclonic conditions

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133314538725 ◽

2014 ◽

Vol 38 (4) ◽

pp. 431-447 ◽

Cited By ~ 13

Author(s):

Fang Zhang ◽

Xiaoming Cai ◽

John E. Thornes

Keyword(s):

Spatial Patterns ◽

Land Surface ◽

Urban Canopy ◽

Urban Heat Islands ◽

City Centre ◽

Canopy Layer ◽

Weather Types ◽

Heat Islands ◽

Urban Heat ◽

The Difference

This study investigates the characteristics of the air and surface urban heat islands (aUHI and sUHI) of Birmingham in relation to Lamb weather types (LWTs) over the period 2002–2007, with a particular focus on cloudless anticyclonic conditions. Ground-based MIDAS air temperatures within the urban canopy layer at the urban Edgbaston and rural Shawbury weather stations were used to derive the aUHI intensity (aUHII). Satellite-derived MODIS/Aqua land surface temperatures (LST) under cloudless conditions were used to derive the spatial patterns of the sUHI as well as the sUHI intensity (sUHII). Using Jenkinson’s objective daily synoptic indices, a combined subset of 11 LWTs were examined for their association with the nocturnal aUHI. Over the study period, the most frequently occurring LWT, ‘anticyclonic’ (21.1%), gives a strongest mean/maximum nocturnal aUHII of 2.5°C/7°C (391 nights) and the largest proportion of nocturnal heat island events of 65.2%. The spatial patterns of nocturnal sUHI for each LWT were also assessed, and the results demonstrate Birmingham’s urban warming of up to 4.16°C (48 clear nights) in the city centre under cloudless anticyclonic conditions. The scatter plot of nocturnal aUHII and sUHII for the 48 nights demonstrates a linear relationship. We also developed a simple analytical model that links the slope of the aUHII–sUHII relationship to the difference of ‘built-up’ area fraction between the urban pixel and the rural pixel in satellite imagery of land cover. This partially explains the physical basis behind the relationship. These findings of the aUHII–sUHII relationship may lead to the future development of a generic methodology of deriving the spatial patterns of aUHI from satellite measurements.

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