Single-layer Urban Canopy Model Research Articles

On the energy impact of cool roofs in Dubai

Urban warming can significantly increase the energy demand for building cooling. As temperatures rise, buildings require additional energy for air conditioning, which can lead to higher electricity demand and increased greenhouse gas emissions. This can create a positive feedback loop, as increased energy demand leads to increased waste heat and further exacerbates the urban warming effect. To solve this issue, this study integrates high-resolution meteorological data from the Weather Research and Forecasting coupled with Single Layer Urban Canopy Model (WRF/SLUCM) into CitySim for the first time to evaluate the building energy demands at various building types in Dubai metropolis. Examining how cool materials reduced cooling loads was achievable utilizing modified albedo in Dubai's downtown buildings. An increase of urban albedo from 0.2 to 0.8 significantly lowers the urban heating effect, as well as the building cooling needs. The study shows that for albedo scenario 0.8, Low-rise residential buildings experienced cooling load reductions of about 13.1 kWh/m2 and 6 kWh/m2 for buildings without insulation and those with insulation, respectively, compared to the base case. Additionally, high-rise residential buildings saw cooling load reductions ranging from 7.7 kWh/m2 to 11.7 kWh/m2 for buildings without insulation and a cooling load reduction of 3.6 kWh/m2 to 5.4 kWh/m2 for insulated buildings. This study provides a tool for regulating the energy impact of cool roofs in different urban settings using energy simulation, which could help policymakers control the energy needs in subtropical desert urban environments.

Evaluation of a single-layer urban energy balance model using measured energy fluxes by scaled outdoor experiments in humid subtropical climate

Urban energy balance models can simulate the localized climate and fluxes in urban areas. They are often evaluated using real urban site observations. However, uncertainties in anthropogenic emission, geometry and materials can hinder interpretation. These uncertainties can be eliminated by scaled outdoor models. We assess the performance of a single-layer urban canopy model (SLUCM) offline in predicting surface energy balance (SEB) fluxes within a street canyon-like urban configuration using field measurements from a scaled outdoor setup in Guangzhou, China. We utilize observations of SEB fluxes and surface temperature from a homogenous scaled urban site characterized by street canyons. The scaled observations in June–December 2020 in a dense urban environment with a height-to-width ratio (H/W) of 2 are employed to assess seasonal variations in model performance. Additionally, the December data from two distinct urban scenarios (H/W = 2(2020) and 0.5(2019)) are utilized to compare model performance in deep and shallow street canyon settings. Results show that QS(R2 = ∼0.47, RMSE = ∼46.0) is the most challenging to predict, followed by QH (R2 = ∼0.92, RMSE = ∼65.5), Q*(R2 = ∼0.99, RMSE = ∼39.5), L↑(R2 = ∼0.95, RMSE = ∼33.7), and K↑(R2 = ∼0.99, RMSE = ∼6.2). SLUCM faces difficulties simulating L↑ particularly during nighttime and cloudy conditions. It also tends to underestimate daytime net radiation (Q*), especially during hot, humid months. Moreover, H/W ratio slightly affects SLUCM's performance in simulating SEB fluxes under dry cool conditions denser urban settings (H/W = 2) show better model performance in K↑, QH, and QS but worse performance in L↑ and Q*.

Urban irrigation reduces moist heat stress in Beijing, China

AbstractAlthough urban irrigation can modulate local hydrothermal conditions and mitigate urban heat island effects, its impact on moist heat stress (MHS) is poorly understood. Employing the Weather Research and Forecasting Single-Layer Urban Canopy Model (WRF-SLUCM), we evaluated the effect of urban irrigation on the MHS in Beijing, China, and found that the updated initial soil moisture (SM) field improved the simulation of temperature, relative humidity, and wind speed. Besides, urban irrigation reduced urban and rural MHS, and particularly reduced afternoon and evening MHS by up to 1.2 °C but increased morning MHS by up to 0.4 °C. In addition, the effect of different irrigation times on MHS showed that irrigation at 02 and 20 h increased urban and rural MHS, with the best cooling effect at 00 and 13 h, which reduced the MHS by up to 2.65 °C in urban areas and 0.71 °C in rural areas. The findings highlighted mechanistically the effect of urban irrigation on MHS and shed light on how to mitigate urban heat island effects on urban sustainable development.

Analyzing urban influence on extreme winter precipitation through observations and numerical simulation of two South China case studies

This study investigates the impact of urbanization on extreme winter rainfall in the South China Greater Bay Area (GBA) through the analysis of hourly station observations and simulations using the Weather Research and Forecasting Model with the Single Layer Urban Canopy Model (WRF-SLUCM). Data from 2008 to 2017 reveal that urban areas in the GBA experience lower 99th percentile hourly winter rainfall intensity compared to surrounding rural regions. However, urban locations exhibit higher annual maximum hourly rainfall (Rmax) and very extreme rainfall events (99.99th percentile) in winter, suggesting a positive influence of urbanization on extreme winter precipitation. A case study further underscores the role of the Urban Heat Island (UHI) effect in enhancing extreme rainfall intensity and probability in the GBA urban areas. Additionally, two extreme cases were dynamically downscaled using WRF-SLUCM, involving four parallel experiments: replacing urban land use with cropland (Nourban), using historical urban land use data from 1999 (99LS), projecting near-future urban land use for 2030 (30LS), and considering 2030 urban land use without anthropogenic heat (AH) (30LS-AH0). Synoptic analysis demonstrates that cold air intrusion suppresses the GBA UHI in Case 2013 but not in Case 2015. Reduced evaporation and humidity induced by urban surfaces significantly decrease urban precipitation in Case 2013. In contrast, the persistent UHI in Case 2015 enhances local convection and land–ocean circulation, leading to increased moisture flux convergence and amplified urban precipitation intensity and probability in 30LS compared to Nourban. This amplification is primarily attributed to AH, while the change in 99LS remains insignificant. These findings suggest that urban influences on extreme precipitation in the GBA persist during winter, particularly when the UHI effect is maintained.

Influence of urbanization on winter surface temperatures in a topographically asymmetric Tropical City, Bhubaneswar, India

Urban areas experience significant alterations in their local surface energy balance due to changes in the thermal properties of impervious surfaces, albedo, land use, and land cover. In addition, the embedded influence of urbanization and heat-trapping in the urban canopy cause city temperature warmer compared to its surroundings peri-urban regions. However, the influence of urbanization on winter surface temperatures remains unclear. In this study, the urbanization influence on winter surface temperature in Bhubaneswar, a tropical two-tier city in India, is assessed using a high-resolution (4 km × 4 km) urban canopy model coupled with the Weather Research and Forecasting model. Numerical experiments are conducted with no urban coupling (CTL) and with coupling of a single-layer urban canopy model (UCM) for the winters of 2004 and 2015. The study suggests that both model simulations exhibit a similar warm bias in mean surface temperature (~ 2.2 °C), but UCM’s surface temperature better agrees with the observations compared to CTL. The warm bias in both experiments is primarily contributed by a higher nighttime warm bias (~ 3.2 °C). The study reveals that urbanization contributes to ~ 0.4 °C increase in surface temperature in 2015, especially in the eastern lowland regions of the city, while the impact is minimal in 2004. In the western region, the influence is nullified, possibly due to lower surface specific humidity affecting longwave radiation in a higher terrain setting. This study underscores the significance of terrain and local microclimate conditions in shaping winter urban surface temperatures, shedding light on the complex interplay between urbanization and climate.

Performance evaluation of the WRF model under different physical schemes for air quality purposes in Buenos Aires, Argentina

This work presents the performance evaluation of the Weather Research and Forecasting (WRF) model to estimate surface wind speed and direction, air temperature, and water vapor mixing ratio considering 22 configurations at high spatial resolution (1 km) during one week in winter and one week in spring, in order to determine the best-performing schemes for air quality purposes in the Metropolitan Area of Buenos Aires, Argentina. Results show that the use of urban schemes mostly affects wind speed and temperature. The single-layer urban canopy model (UCM) coupled with the Boulac planetary boundary layer (PBL) scheme exhibits the best results for wind speed. Wind direction and water vapor mixing ratio are more sensitive to the land surface model scheme, with results slightly improving with the Noah-MP land surface model. Wind speed and direction errors are larger when the former is lower. When removing from the analysis wind speed values below 2.6 m s–1 for the winter week and 3.1 m s–1 for the spring week, the root mean square errors for wind direction decreased between 50 and 72% of the original value, depending on the configuration and week. Overall, under the studied conditions, configurations including Noah-Mp land surface model or the combination of a simple UCM with BouLac PBL are suitable for air quality applications, as they reproduce both temperature and water vapor mixing ratio relatively well, with errors below 10% and Correlation values above 0.7, and are the best performing configurations for wind direction and speed, respectively.

Impact of Anthropogenic Heat on Urban Environment: A Case Study of Singapore with High-Resolution Gridded Data

Anthropogenic heat (AH) emissions have great impacts on urban climate. AH is usually spatially heterogeneous and depends on the urban land use type. Studies using high-resolution gridded data that can resolve spatially heterogeneous AH are still scarce. The present study uses AH data of a high spatial resolution of 200 m by 200 m and a temporal resolution of 1 h to investigate the impact of AH in Singapore in April 2016, particularly regarding the relative contribution of individual AH components. The WRF model coupled with a single-layer urban canopy model is employed. The WRF model can predict the 2-m air temperature and 2-m relative humidity with good agreement with the observation data, while the simulated 10-m wind speed has relatively large deviation from the observation data. The largest spatially averaged temperature increases caused by total AH (QF), AH from buildings (QB) and AH from traffic (QV) are 1.44 °C, 1.44 °C and 1.35 °C, respectively. The effects of AH on sensible heat flux and boundary layer height are largely consistent, with both QF and QB exhibiting significant effects at night, while the effects of QV are small. The effect of AH on the local circulations (sea and land breezes) in Singapore is small, while its effect on the urban heat island (UHI) circulations is more pronounced. Due to the UHI circulations, the sum of the effects on local temperatures caused by QB and QV may exceed that by QF in some areas. This finding can guide comprehensive mitigation measures of AH by not only focusing on land use type but also on the contribution of individual AH components, in order to ameliorate the impacts of urban overheating.

Cooling Energy Benefits of Increased Green Infrastructure in Subtropical Urban Building Environments

Due to urban warming, the energy demand for cooling buildings is rising. The current study used CitySim (building energy model) to estimate the cooling energy requirements for 40 buildings in downtown Dubai using high-resolution climate data from weather research and forecasting (WRF) coupled with the single layer urban canopy model (SLUCM). Simulating the four mitigation scenarios allowed for the examination of the reduction in cooling load caused by the addition of greenery at a rate ranging from 25% to 100%. The insulated building’s cooling demand reduced by a maximum of 13.89% under 100% GI (M4). Scenario M4 resulted in a reduction of 4.6 kWh/m2 and 3.1 kWh/m2 for the non-insulated and insulated low-rise residential buildings, respectively, while the high-rise buildings saw a reduction of 3.09–4.91 kWh/m2 for the non-insulated and 2.07–3.09 kWh/m2 for the insulated buildings. This study offers a potential remedy to deal with the problem of urban heating in subtropical environments.

Quantifying the Impacts of High‐Resolution Urban Information on the Urban Thermal Environment

AbstractDetailed urban information, including land use/land cover (LULC), anthropogenic heat (AH) release, and urban canopy parameters (UCP), play critical roles in meteorological field simulations. It is particularly relevant for the Weather Research and Forecasting (WRF) model coupled with the single‐layer urban canopy model (SLUCM). Thus, we develop high‐resolution LULC, AH, and UCP data sets for Nanjing, a megacity in China, and conduct a series of numerical experiments with WRF/SLUCM to evaluate the impacts of urban parameters on the urban thermal environment. Model simulations with LULC scenarios have good agreement with the observed 2‐m temperature with a correlation coefficient of around 0.85, and present strong spatial homogeneity due to the more realistic representation of urban categories. The LULC change directly decreases the surface wind speed and increases (decreases) the sensible (latent) heat flux ( ()) in urban areas during the daytime; meanwhile increases and releases ground heat storage () during the nighttime, resulting in urban warming by 0.91°C in urban areas, compared with the control simulation (CTL) that does not take into account urban surfaces. In the LULC experiments combined with the UCP or AH, the UCP change enhances and releases more during nighttime, which increases by 0.13°C relative to LULC simulation. Also, the UCP effect reduces surface roughness and increases the width of the urban canopy, resulting in slightly enhanced wind speed, which is favorable for a warming environment in the urban area; the AH change contributes to increasing by 0.19°C through directly enhancing relative to LULC simulation. AH combined with the UCP further strengthens the UCP effect in the urban area. Overall, the influence of urban parameters on the is more pronounced during nighttime than daytime, which presents a decreasing trend with an increase in wind speed and spatial humidity in the urban area.

Impact of land use land cover on variation of urban heat island characteristics and surface energy fluxes using WRF and urban canopy model over metropolitan cities of India

The present study focuses on the assessment of urban heat island (UHI) and urban energy fluxes over Indian metropolitan cities. For this purpose, a high-resolution numerical Weather Research and Forecasting (WRF) model and a coupled single-layer urban canopy model (WRF-UCM) has been implemented over Delhi, Kolkata and Hyderabad to evaluate the performance of these models in capturing the impact of urban areas in simulating the UHI over different land use land cover (LULC) classes. The model initial conditions are obtained from NCEP-FNL reanalysis data. The model simulations reveal that the air temperature at 2 m height (T2) and UHI intensity are overestimated by WRF whereas the WRF-UCM simulations are matching well with ERA5-Land reanalysis data considered as observations. For the WRF-UCM simulation, the statistical errors in T2 and UHI intensity are within the desirable limits and the index of agreement (IoA) is more than 0.8 over the built-up areas (BAs). By comparing with the satellite observations, the relative surface UHI is noticed to be better captured over the BA by WRF-UCM simulation. The sensible heat flux simulated by WRF is highly overestimated whereas WRF-UCM is in good agreement with the satellite observation over the BA. Interestingly, the latent heat flux is reasonably well simulated over all the LULC classes. The results derived from the present study have shown the performance of WRF-UCM in simulating the spatial variation of UHI and fluxes of sensible and latent heat-related varied LULC classifications over metropolitan cities of India.

Industrial heat island mitigation in Angul-Talcher region of India: Evaluation using modified WRF-Single Urban Canopy Model

Linkages of urban and industrial cooling with sustainable development goals and climate change perspectives are well acknowledged, mainly for developing economies in tropical climates. Angul-Talcher region is one of the oldest industrial clusters of India, and the region experiences higher atmospheric heat island intensities with magnitudes of 7 to 9 °C attributed to the Industrial Heat Island (IHI) effect. In the present study, various measures for mitigating heat island effect in the region and assessed their impact using an Improved Weather Research and Forecasting model coupled with the Single-Layer Urban Canopy Model. The improved framework includes the release of industrial emissions at stack height and sector-wise diurnal profiles of anthropogenic heat (AH) released from vehicles, residential, and industry/power. The mitigation measures comprised strategies like alteration in building materials and conversion of landuse-landcover (LULC) of selected grid cells in the model domain to more vegetation or water bodies. It was noted that the cool roofs and walls together reduced IHIs by 0.5 °C, while green roofs and cool pavements achieved a reduction of 0.3 °C and 0.1 °C, respectively. The introduction of water bodies showed maximum reduction in IHIs by 3 to 5 °C during daytime and 1 to 2 °C over mining and industrial stations. During night-time, conversion to mixed forests was more effective (ΔT ≈ 1 °C) than conversion to water bodies. A combination of cool roofs with the introduction of water bodies in the mining areas and mixed forest patches in industry stations was found to be the most effective mitigation strategy for mitigating the industrial heat island effect over the Angul-Talcher region. These mitigation scenarios can/should serve as a theoretical reference for implementing actual mitigation measures, which would require consideration of economic, social, and policy aspects apart from scientific ones for practical application.

Water effects on urban heat islands in summer using WRF-UCM with gridded urban canopy parameters — A case study of Wuhan

Urbanization has rapidly occurred in China along with a significant reduction in ecological resources, greatly exacerbating urban heat island effect. In cities with abundant inland lake resources, it is valuable to study the use of climate regulation effects of water bodies to improve the urban climate environment. In this study, we built a single-layer urban canopy model with higher accuracy by importing gridded urban canopy parameters and studied the impact of water bodies distributed in different areas of Wuhan on the urban thermal environment using a weather research and forecasting model (WRF). Water bodies located in the upwind suburbs had the greatest cooling impact on the microclimate of downtown areas during the day. Furthermore, they promote the evacuation of heat inside the urban canopy to the upper air by enhancing the lake breeze circulation. Late morning temperatures drop above the ground when the breeze circulation fully develops, while the temperature rise in the upper air increases. However, the cooling effect of these water bodies on the downtown area was affected by construction intensity in the suburbs. When the building density increased from 20% to 40%, the cooling effect did not change significantly. However, a further increase from 40% to 60% increased the proportion of intense heat island areas downtown, and the cooling effect of water bodies was also severely weakened. Moreover, with an increase in building density in the suburbs, the intense heat island areas north of the city gradually expanded to the city center.

Numerical simulation of the impact of urban canopies and anthropogenic emissions on heat island effect in an industrial area: A case study of Angul-Talcher region in India

Changing urban land use dynamics and associated anthropogenic heat (AH) has increased warming over urban centres. This warming is associated with the Urban Heat Island (UHI) effect and is mainly studied in major cities worldwide. However, predominantly industrial regions can also experience heat island effect due to heat emissions from industrial infrastructure and associated activities. The current study examines the role of the urban morphology and anthropogenic heat over the Angul-Talcher region (industrial region) of Odisha state, India. The region is also referred to as India's highly polluted industrial cluster due to open cast mining and industries. In the present study, Weather Research and Forecast model (WRF) coupled with Single-Layer Urban Canopy Model (WRFUCM) has been modified to examine the impact of urban morphology and anthropogenic heat on the heat island effect over the region. The study refers modified version of WRFUCM as the WRFAH[Gridded] model, which incorporates anthropogenic heat from different industries with stack height information. It was found that the WRF model capabilities have improved in simulating surface meteorological variables such as temperature at 2 m, wind speed, and relative humidity with the inclusion of gridded AH. Urban canopies were estimated to bring in an increase of up to 0.77°C in local near-surface temperatures during daytime and up to 2.12°C during nighttime. AH emissions further increased temperatures by up to 0.32°C during daytime and 0.69°C during nighttime. Maximum heat island intensity with urban canopies is estimated to be 5.62°C which increases to 7.11°C with the inclusion of average AH released at canopy level. However, this has significantly improved to 5.88°C with inputs of industry-specific AH released at stack heights with the WRFAH[Gridded] model against observed maximum heat island intensity of 6.06°C. Overall, the influence of urban morphology was found to be higher than the influence of AH over the region at ground level, and the inclusion of gridded AH in the model showed improved performance for the heat island assessment.

Lisbon urban heat island in future urban and climate scenarios

Urban heat Islands (UHIs) may constitute a hazard for urban populations, particularly under extreme events such as heatwaves. Lisbon is a medium-size city regularly affected by heatwaves with maximum temperatures >35 °C and is expected to grow and densify during this century.This study evaluates the UHI in the Municipality of Lisbon (ML) for the heatwave with the highest maximum average temperature identified in long-term (2081–2100) RCP8.5 scenario climate simulations, relative to present conditions, represented by a typical heatwave identified in historical (1986–2005) period climate simulations. The expected future consolidation of the city (up to 2100) is considered, as well as the anthropogenic heat and irrigation effects. Simulations are performed with the Weather Research and Forecasting model (WRF) coupled with the Single-layer Urban Canopy Model (SLUCM). The results reveal that urban irrigation effects compensate anthropogenic heat due to increased latent heat release. The expected city consolidation up to 2100 produces the largest increase in the UHI intensity (UHII) during nighttime due to the introduction of urban land use land cover (LULC) with reduced green fraction. No evidence of synergistic interactions between UHI and heatwave intensities were found, with the UHII mostly depending on wind direction and intensity.

On the mitigation potential and urban climate impact of increased green infrastructures in a coastal mediterranean city

An increase of the green infrastructure (GI) in cities contributes to sharing the common goals of reducing energy usage, mitigating pollution emissions, and improving the urban climate. The synergy between urban heat island (UHI) and extreme urban heat in Athens (Greece), during the summer, is inevitable, while its green cover is considerably low. However, very few studies have reported the biophysical effects of the GI on the surface standard meteorological field and urban boundary layer (UBL) that are triggered by changes in the GI at a seasonal scale. The present study sheds light on the complex synergies between UHI and extreme urban heat focusing on warm coastal cities like Athens.To this end, we hypothetically designed and simulated three scenarios of increased GI, such as GI30, GI50, and GI70. The mosaic approach is used, and the portion of each urban grid cell is replaced with different types of vegetation patches, by 30%, 50%, and 70% using the weather research and forecasting (WRF) model, coupled with a single-layer urban canopy model (SLUCM) at city-level for assessing mitigation potential and urban climate impact. The results showed that the daily peak decrease of the ambient temperature at 17:00LT is close to 0.0157 °C per unit of increase of the GI, while the maximum expected daytime summer temperature decrease is 0.7 °C, 1 °C, and 1.1 °C for GI30, GI50, and GI70 scenarios respectively compared to the control case. The corresponding decrease of the nighttime ambient temperature is found to be 0.0375 °C per unit of increase of the GI, whereas the maximum temperature reduction for the G70 scenario is close to 1.9 °C. Increased GI is found to significantly lower the height of the planetary boundary layer (PBL), thus increasing the risk of higher pollution concentration, while a significant impact on the strength of the sea breeze and the level of humidity is observed increasing the risk of thermal discomfort. Results are also compared with the findings of thirty similar projects from other cities to provide a global assessment of the mitigation potential of additional GI in cities. Our results contribute significantly to understanding the mitigation potential and urban climate impact of increased GIs at a seasonal scale.

Impact of land cover transformation on urban heat islands in Harbin, China

At the local and regional climate scale, one of the most studied environmental issues is urban heat island (UHI). UHI is a thermal anomaly caused by temperature differences between urban and rural settings, which adds heat to the atmosphere and makes people feel uncomfortable. This study explores the influence of new land-cover data on UHI simulations using the high-resolution Weather Research and Forecasting (WRF) model coupled with the single-layer urban canopy model (SLUCM) in the city of Harbin. A comparison was performed between the new Tsinghua University (TU) land cover dataset with the default United States Geological Survey (USGS) and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover datasets. The results of this study revealed that the new TU land cover data had better representation and more realistic land cover changes than the default datasets. The diurnal, seasonal, and long-term nighttime UHIs of air and surface temperatures were higher than the daytime UHIs for both downtown Harbin and the satellite towns. We discovered that coal-burning during winter had a significant influence on UHI in Harbin. Moreover, the results from our buffer revealed a rapid increase in the UHIs of satellite towns, thus revealing the need to focus on the effects of UHI in satellite towns in the future. Therefore, the timely updating of land cover datasets in the WRF model and implementing mitigation strategies will help improve the urban climatic comfort.

Parameter Sensitivity Analysis and Optimization of the Single-Layer Urban Canopy Model in the Megacity of Shanghai

In order to meet the demand of more refined urban weather forecast, it is of great practical significance to improve and optimize the single-layer urban canopy model (SLUCM) suitable for the megacity of Shanghai. In this paper, based on the offline SLUCM model driven by a whole-year surface flux observation data in the Shanghai central business district, a series of parameter sensitivity tests are carried out by using the one at a time (OAT) method, the relative importance and a set of optimized parameters of the SLUCM suitable for high-density urban area are established, and the improvement of simulation is evaluated. The results show that SLUCM well reproduces the seasonal mean diurnal patterns of the net all-wave radiation flux ( Q ∗ ) and sensible heat flux (QH) but underestimates their magnitudes. Both Q ∗ and QH are linearly sensitive to the albedo, and most sensitive to the roof albedo, the second to the wall albedo, but relatively insensitive to the road albedo. The sensitivity of Q ∗ and QH to emissivity is not as strong as that of albedo, and the variation trend is also linear. Similar to albedo, Q ∗ and QH are most sensitive to roof emissivity. The effect of thermal parameters (heat capacity and conductivity) on fluxes is logarithmic. The sensitivity of surface fluxes to geometric parameters has no specific variation pattern. After parameter optimization, RMSE of Q ∗ decreases by about 3.4–18.7 Wm−2 in four seasons. RMSE of the longwave radiation (L↑) decreases by about 1.2–7.87 Wm−2. RMSE of QH decreases by about 2–5 Wm−2. This study provides guidance for future development of the urban canopy model parameterizations and urban climate risk response.

Improving the WRF/urban modeling system in China by developing a national urban dataset

Accurate modeling of urban climate is essential to predict potential environmental risks in cities. Urban datasets, such as urban land use and urban canopy parameters (UCPs), are key input data for urban climate models and largely affect their performance. However, access to reliable urban datasets is a challenge, especially in fast urbanizing countries. In this study, we developed a high-resolution national urban dataset in China (NUDC) for the WRF/urban modeling system and evaluated its effect on urban climate modeling. Specifically, an optimization method based on building morphology was proposed to classify urban land use types. The key UCPs, including building height and width, street width, surface imperviousness, and anthropogenic heat flux, were calculated for both single-layer Urban Canopy Model (UCM) and multiple-layer Building Energy Parameterization(BEP). The results show that the derived morphological-based urban land use classification could better reflect the urban characteristics, compared to the socioeconomic-function-based classification. The UCPs varied largely in spatial within and across the cities. The integration of the developed urban land use and UCPs datasets significantly improved the representation of urban canopy characteristics, contributing to a more accurate modeling of near-surface air temperature, humidity, and wind in urban areas. The UCM performed better in the modeling of air temperature and humidity, while the BEP performed better in the modeling of wind speed. The newly developed NUDC can advance the study of urban climate and improve the prediction of potential urban environmental risks in China.

Multi-objective optimization of urban environmental system design using machine learning

The efficacy of urban mitigation strategies for heat and carbon emissions relies heavily on local urban characteristics. The continuous development and improvement of urban land surface models enable rather accurate assessment of the environmental impact on urban development strategies, whereas physically-based simulations remain computationally costly and time consuming, as a consequence of the increasing complexity of urban system dynamics. Hence it is imperative to develop fast, efficient, and economic operational toolkits for urban planners to foster the design, implementation, and evaluation of urban mitigation strategies, while retaining the accuracy and robustness of physical models. In this study, we adopt a machine learning (ML) algorithm, viz. Gaussian Process Regression, to emulate the physics of heat and biogenic carbon exchange in the built environment. The ML surrogate is trained and validated on the simulation results generated by a state-of-the-art single-layer urban canopy model over a wide range of urban characteristics, showing high accuracy in capturing heat and carbon dynamics. Using the validated surrogate model, we then conduct multi-objective optimization using the genetic algorithm to optimize urban design scenarios for desirable urban mitigation effects. While the use of urban greenery is found effective in mitigating both urban heat and carbon emissions, there is manifest trade-offs among ameliorating diverse urban environmental indicators.

Observed and Modeled Urban Heat Island and Sea-Breeze Circulation Interactions: A Shanghai Case Study

Abstract Urban heat island (UHI) and sea–land-breeze systems are well-known and important characteristics of the climate of coastal cities. To model these, the accurate estimation of the surface energy balance (SEB) is a key factor needed to improve local-scale simulations of thermodynamic and dynamic boundary circulations. The Weather Research and Forecasting Model with a single-layer urban canopy model (WRF/SLUCM), with parameters derived from MODIS and local GIS information, is used to investigate the UHI and sea-breeze circulations (SBC) in the megacity of Shanghai. The WRF/SLUCM can reproduce observed urban radiation and SEB fluxes, near-surface meteorological variables, and the evolution of the UHI and SBC. Simulations for an August period show the maximum UHI tends to drift northwest in the afternoon, driven by the prevailing southeast wind. The sea breeze lasts for about 4 h and is strongest between 1200 and 1400 local time (UTC + 8 h). The interaction between UHI and SBC is evident with low-level convergence, upward motion, and moisture transport from the sea and urban breezes simulated. An urban circulation (horizontal/vertical/time scales: ∼20 km/∼1.5 km/∼3 h) with thermal vertical motions (∼1.5 m s−1) above the urban area and an SBC (horizontal/vertical/time scales: 6–7 km/∼1 km/2–3-h) above the northern coastal suburb occur. Combined the sea breeze and southerly winds form a low-level wind shear (convergence zone) ∼5 km from the coast that penetrates ∼20 km inland to the urban center. Using the WRF/SLUCM simulations we improve understanding of the complex spatial dynamics of summertime urban heating in coastal megacities, such as Shanghai.