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... the idealized city-scale energy balance model, we sim- plify the complex urban geometry as a lumped system, and the force-restore method enables us to consider the average temperature of effective thermal mass and surfaces, T s to be the same throughout, while keeping retaining the energy balance ( Figure 1). ...
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... cities with a small time constant í µí¼, UCIdh easily reach zero as the anthropogenic heat increases, which means that under such conditions, the UCI phenomenon no longer occurs, and the UHI phenomenon will dominate the entire day. Figure 10 illustrates the relationship between the UCIdh for different plan area indices í µí¼ p and the time constant í µí¼ under the maximum anthropogenic heat of 40 W m −2 condition. The tendency is similar to Figure 7 where there is no anthropogenic heat. ...
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... Figure 10, the UCIdh data for Hong Kong is included for reference. In order to eliminate the evapotranspiration effect, the vegetated surface is defined as an artificial surface in this case. ...
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... reality, most of the cities in the world are much less dense and have lower buildings than Hong Kong. The inset graph in Figure 10 illustrates the situations in which the thermal storage capacity (mc) is less than or equal to the city of Hong Kong (mc = 1.8 × 10 14 J/K). We can see that the UCIdh values are quite small for most of the cities, and often zero. ...
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... Because 3 stations (MI Bocconi, MI Centro, MI Sarpi) are near each other in the most central part of the city, they were considered together in defining the most symmetric and well-centered UHI form. In the summer morning, UHI is normally at a minimum in Milan with a clear urban cool island effect as in other compact cities (Yang et al 2017;Gonçalves et al. 2018), and, therefore, no episode was found with UHI Index > 3°C. Table 2 shows that the centered symmetric UHI is the most frequent configuration in Milan, in agreement with the climatological prevalence of low winds and calms in the area. ...
Urbanized environments are of greater relevance because of the high and still rapidly increasing percentage of the world population living in and around cities and as the preferred location of human activities of every type. For this reason, much attention is paid to the urban climate worldwide. Among the UN 2030 17 Sustainable Development Goals, at least one concerns resilient cities and climate action. The WMO supports these goals promoting safe, healthy, and resilient cities by developing specially tailored integrated urban weather, climate, and environmental services. An unavoidable basis for that is an improved observational capability of urban weather and climate, as well as high-resolution modeling. For both the former and the latter, and of primary importance for the latter, urban meteorological surface networks are undoubtedly a very useful basis. Nevertheless, they are often unfit for detailed urban climatological studies and they are generally unable to describe the air temperature field in the urban canopy layer (UCL) with a spatial resolution which is sufficient to satisfy the requirements set by several professional activities and especially for local adaptation measures to climate change. On the other hand, remote sensing data from space offer a much higher spatial resolution of the surface characteristics, although the frequency is still relatively lower. A useful climatological variable from space is, for instance, the land surface temperature (LST), one of the WMO Essential Climate Variables (ECV). So often used to describe the Surface Urban Heat Islands (S-UHI), LST has no simple correlation with UCL air temperature, which is the most crucial variable for planning and management purposes in cities. In this work, after a review of correlation and interpolation methods and some experimentation, the cokriging methodology to obtain surface air temperature is proposed. The implemented methodology uses high quality but under-sampled in situ measurements of air temperature at the top of UCL, obtained by using a dedicated urban network, and satellite-derived LST. The satellite data used are taken at medium (1 × 1 km2) resolution from Copernicus Sentinel 3 and at high resolution (30 × 30 m2) from NASA-USGS Landsat 8. This fully exportable cokriging-based methodology, which also provides a quantitative measure of the related uncertainties, was tested and used to obtain medium to high spatial resolution air temperature maps of Milan (Italy) and the larger, much populated, but also partly rural, surrounding area of about 6000 km2. Instantaneous as well as long period mean fields of fine spatially resolved air temperature obtained by this method for selected weather types and different Urban Heat Island configurations represent an important knowledge improvement for the climatology of the urban and peri-urban area of Milan. It finds application not only in more detailed urban climate studies but also in monitoring the effects of urban activities and for the assessment of adaptation and mitigation measures in the urban environment. Finally, the first set of interactive maps of medium–high resolution UCL air temperature was produced in the framework of the locally funded ClimaMi project and made freely available to urban authorities and professionals as an improved climatological basis for present and future plans and projects to be developed in the framework of the national and international adaptation and mitigation measures.
Urban morphological change impacts the land surface temperature (LST) through modifying the net radiation, convective heat transfer, evapotranspiration, and heat storage on the ground. It is essential to quantify the contributions of these physical changes on LST changes. In this work, we conduct simulations using a weather research and forecasting model for the Chengdu–Chongqing urban agglomeration to identify causes of LST changes due to urban morphological changes through different morphological parameters: the aspect ratio, building plan area fraction, and average building height. A new method is proposed and used to quantify the contribution of these physical changes on LST changes. The results show as the aspect ratio increases, an increase of the average LST is induced by variations in radiation, and daytime cooling and nighttime warming are induced by variations in heat storage. There is warming associated with an increase in the building plan area fraction, which is mostly caused by a decrease in the efficiency of the long‐wave radiant heat emitted from the surface to the atmosphere. We also find that an increase in the average building height enhance the efficiency of convective heat transfer, which results in cooling. These results are important for the management of urban thermal environments.
Land use and land cover and Urban Fabric (UF) mapping are very useful for urban modeling and simulation (growth, pollution, noise, micro-climate, mobility) in a context of global change. In recent years, due to the increase of Earth Observation data researchers built and shared datasets to the machine learning scientific community to apply and test their models for semantic segmentation. Few works have trained their deep learning model based on a geographic region and applied it to another geographical area of the same country. In this study, we explore the inference of a deep learning model pretrained on a multitemporal and multimodal dataset named 'MultiSenGE' dataset (based on a region in the East of France representing one fifth of the area of France) on five different cities over France (Toulouse, Dijon, Orléans, Lille, Rennes) away from the trained area. Results are encouraging and achieve F1-Score between 0.70 and 0.80 for the five urban fabric classes.
The current study highlights the importance of a detailed representation of urban processes in numerical weather prediction models and emphasizes the need for accurate urban morphology data for improving the near‐surface weather prediction over Delhi, a tropical Indian city. The Met Office Reading Urban Surface Exchange Scheme (MORUSES), a two‐tile urban energy‐budget parameterization scheme, is introduced in a high‐resolution (330‐m) model of Delhi. A new empirical relationship is established for the MORUSES scheme from the local urban morphology of Delhi. The performance is evaluated using both the newly developed empirical relationships (MORUSES‐IND) and the existing empirical relationships for the MORUSES scheme (MORUSES‐LON) against the default one‐tile configuration (BEST‐1t) for clear and foggy events and validations are performed against ground observations. MORUSES‐IND exhibits a significant improvement in the diurnal evolution of the wind speed in terms of amplitude and phase, compared with the other two configurations. Screen temperature (Tscreen$$ {T}_{\mathrm{screen}} $$) simulations using MORUSES‐IND reduce the warm bias, especially during the evening and night hours. The root‐mean‐square error of Tscreen$$ {T}_{\mathrm{screen}} $$ is reduced up to 29% using MORUSES‐IND for both synoptic conditions. The diurnal cycle of surface‐energy fluxes is reproduced well using MORUSES‐IND. The net longwave fluxes are underestimated in the model and biases are more significant during foggy events, partly due to the misrepresentation of fog. An urban cool island (UCI) effect is observed in the early morning hours during clear‐sky conditions, but it is not evident on foggy days. Compared with BEST‐1t, MORUSES‐IND represents the impact of urbanization more realistically, which is reflected in the reduction of the urban heat island and UCI in both synoptic conditions. Future works would improve the coupling between the urban surface energy budget and anthropogenic aerosols by introducing MORUSES‐IND in a chemistry aerosol framework model.
The present study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHI) in Singapore. Observations collected at 20 locations across the island city‐state and during 6 years make this one of the most extensive studies carried out in a tropical city. Local climate zones (LCZs), defined as urban built and rural land cover types which produce a unique air temperature response, are used to standardize intersite comparison. The results show that the choice of the rural reference can affect the night‐time UHI magnitude by up to 2°C under “ideal” (dry, clear, calm) conditions. The most frequently observed median UHI magnitude increases from 2.8 during all‐weather to 3.7°C during “ideal” conditions, respectively. A seasonality is present with lowest (highest) mean all‐weather values of ~2.0°C (~3.3°C), observed during the wet (dry) December–January (April–October) period. Mean night‐time UHI intensity across seven built‐type LCZs ranges between 1.8 and 3.5°C (2.5 and 4.3°C) for all‐weather (“ideal”) conditions. Corresponding daily values are 1.1–2.3°C (1.5–2.7°C). The lowest (highest) night‐time magnitudes are associated with open low‐rise LCZ 6 (compact high‐rise LCZ 1) built type. In the middle of the day LCZ 1 can experience a cool island effect. The average UHI values presented here give an indication of the extra warmth experienced in the built‐up spaces of Singapore relative to one possible rural reference land cover type (scattered trees LCZ B). Highest daytime heat exposure is observed in LCZs 3 and 8 which are characterized by low building heights, high impervious surface fraction and lack of vegetation. The present results can be used to support the development and evaluation of urban climate models, develop urban planning policies and improve local weather forecasts and the delivery of integrated urban services.
An urban heat island (UHI) is a well‐known urban climatic feature; however, an opposite effect, i.e., an urban cool island (UCI), was recently observed at locations where high‐rises were concentrated. Here, we analyzed the impact of two urbanization factors (anthropogenic heat flux (AH) and building height (ZR)), which could affect the formation of UHIs and UCIs, on urban precipitation over the Seoul Metropolitan area. AH caused an increasing precipitation in urban and downwind areas, especially during daytime. AH directly contributed to the sensible heat flux, resulting in surface warming associated with an UHI. Surface heat was transferred within the planetary boundary layer by turbulence, which increased the low‐level instability, resulting in increased precipitation. In the experiments with the ZR, both surface temperature and precipitation increased (decreased) during the nighttime (daytime). The diurnal variation of ground heat flux intensified with an increasing ZR, inducing a smaller variation of sensible heat flux for surface energy balance. Changes in the ground heat flux could also explain occurrence of UCIs in cities with high‐rises. Tall buildings reduced daytime surface temperature, thereby facilitating atmospheric stabilization, and reducing precipitation. Also, the surface friction increased with a higher ZR, resulting in enhanced convergence in the western part of urban areas where dominant westerlies first met land, which was favorable for increased precipitation. In summary, a study of variations in AH and ZR demonstrated how UHIs and UCIs significantly altered precipitation, respectively. In particular, an UCI alleviated an increment in thermo‐driven daytime precipitation caused by an UHI.
Whether the urban heat island (UHI) is affected by air pollution in urban areas has attracted much attention. By analyzing the observation data of automatic weather stations and environmental monitoring stations in Beijing from 2016 to 2018, we found a seasonally dependent interlink of the UHI intensity (UHII) and PM2.5 concentration in urban areas. PM2.5 pollution weakens the UHII in summer and winter night, but strengthens it during winter daytime. The correlation between the UHI and PM2.5 concentration has been regulated by the interaction of aerosol with radiation, evaporation and planetary boundary layer (PBL) height. The former two change the surface energy balance via sensible and latent heat fluxes, while the latter affects atmospheric stability and energy exchange. In summer daytime, aerosol‐radiation interaction plays an important role, and the energy balance in urban areas is more sensitive to PM2.5 concentration than in rural areas, thereby weakening UHII. In winter daytime, aerosol‐PBL interaction is dominant, because aerosols lower the PBL height and stabilize atmosphere, weaken the heat exchange with the surrounding, with more heat accumulated in the urban areas and the increased UHII. Changes in evaporation and radiation strengthen the relationship. At night, the change of UHII more depends on the energy stored in the urban canopy. Aerosols effectively reduce the incident energy during daytime, and the long‐wave radiation from the buildings of urban canopy at night becomes less, leading to a weakened UHII. Our analysis results can improve the understanding of climate‐aerosols interaction in megacities like Beijing.
Urban heat island (UHI) and cool island (UCI) effects are well‐known and prevalent in cities worldwide. An increasing trend of extreme heat events has been observed over the last few decades and is expected to continue in the foreseeable future. In this study, warm periods (May to September) of 2000–2018 were examined to acquire a comprehensive understanding of the UHI and UCI characteristics for the case study of Hong Kong, China. Twenty‐two weather stations in Hong Kong were classified into four categories, namely urban, urban oasis, suburban, and rural, with reference to the local climate zone (LCZ) scheme, to analyze UHI and UCI phenomena during extreme heat and non‐extreme heat situations. One representative type of extreme heat events was considered in this study: three consecutive hot nights with two very hot days in between (2D3N). Results show that both the UHI and UCI effects are exacerbated during extreme heat events. Using the concept of the UHI degree hours (UHIdh) and UCI degree hours (UCIdh), their spatial patterns in Hong Kong during extreme heat and non‐extreme heat situations were mapped based on multiple linear regression models. It is found that the predictor variable – windward/leeward index is a significant influential factor of both UHIdh and UCIdh during extreme heat events. The resulting UHIdh and UCIdh maps not only enhance our understanding on the spatial pattern and characteristics of the UHI and UCI during extreme heat events, but could also serve as a useful reference in climate change adaptation, heat‐health risk detection, cooling‐energy estimation and policy making.
More than half of the total population in China are living in cities. Especially, the people in highly developed and spatially integrated city clusters, i.e., urban agglomerations (UAs), are facing increasing human-perceived heat stress that describes the combined effects of hot temperature, high humidity, and lowered surface wind speed. By analyzing multiple indicators over 20 major UAs across China, we demonstrate that summer heat stress has been significantly intensifying in nearly all UAs during 1971‒2014. This intensification is more profound in northern than southern regions and is especially stronger in more urbanized and densely populated areas (e.g., Beijing-Tianjin-Hebei and the Yangtze River Delta). Based on a dynamic classification of weather stations using time-varying land use/land cover maps, we find that urban core areas exhibit distinctly stronger increasing heat stress trends than their surrounding rural areas. On average, urbanization contributes to approximately one-quarter of the total increase in mean heat stress over urban core areas of UAs and nearly half of the total increase in extreme heat events. The urbanization effect is also dependent on the geographical region within China. Urbanization tends to have stronger intensifying effects on heat stress in UAs with higher population density in low-altitude areas, while it has a relatively weaker intensifying and even weakening effect in some arid and high-altitude regions. Moreover, as various heat stress metrics may yield different estimations of long-term trend and urbanization contribution, the particular choice of heat stress indicator is of critical importance for investigations on this subject matter.
A barrier to urban heat island (UHI) mitigation is the lack of quantitative attribution of the various contributions to UHI intensity. This study demonstrates the daily and seasonal dynamics of UHIs in Hong Kong, a subtropical high‐density city. The nocturnal UHIs of the city are grouped according to various dynamic stability conditions (neutral, weak stable, and strong stable) of the boundary layer. Results indicate that the stronger the atmospheric stability, the more intense the UHI. The atmospheric anomalies linked to these stability classifications are hence revealed. In summer, nights of neutral (strong stable) stratification are controlled by low (high) pressure with rising (sinking) motion, less (more) precipitation, and lower (higher) air temperature at the surface. In winter, the influence of the large‐scale circulation system of the East Asian winter monsoon is significant. In the upper layer, the East Asian jet stream retreats westward (is displaced northward) on nights with neutral (strong stable) atmospheric stratification. At the surface, southeast China is hot and humid on neutral nights, while on strong stable nights, the coastal regions of southeast China are dry, and East Asia is dominated by positive surface air temperature anomalies. Atmospheric anomalies are generally nonsignificant on nights with weak stable stratification in both summer and winter. These findings provide potential predictors for UHI intensity. The daily and seasonal dynamics of urban heat islands (UHIs) in Hong Kong are revealed in the illustration. Steady nocturnal UHIs were found and grouped according to nighttime stability classifications. Strong stable nights with strong UHI intensity were found to bond with northward displacement of the East Asian jet stream and continental surface warm anomalies over East Asia.
