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The complexities of urban climate and environmental challenges have garnered significant attention in the 21st century. Numerical simulations, offering high spatiotemporal resolution meteorological data, are essential tools in meteorological research and atmospheric science. Accurate representation of urban morphology parameters is crucial for enhancing the precision of these simulations in urban areas. Despite the availability of urban canopy parameter (UCP) data for 44 major cities in the United States and 60 in China for the weather research and forecasting (WRF) model, a comprehensive global dataset representing urban morphology remains absent. This study addresses this gap by leveraging existing global three-dimensional vector data of buildings, including footprints and heights, to compile a global 1 km spatially continuous UCP (GloUCP) dataset for the WRF model. Our findings indicate that GloUCP not only surpasses existing datasets in accuracy but also provides superior spatial coverage. In key urban agglomerations such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Guangdong-Hong Kong-Macao Greater Bay Area in China, GloUCP offers detailed and reliable urban morphological information that closely aligns with reference datasets, outperforming other available sources. Similarly, in U.S. cities like Seattle, San Francisco, and Philadelphia, GloUCP consistently achieves lower RMSE values and higher correlation coefficients, demonstrating its robustness in modeling diverse urban environments. Furthermore, GloUCP's capability to effectively capture the vertical distribution of buildings, particularly in high-rise areas, highlights its utility in urban climate modeling and related applications. As UCPs are pivotal in regulating atmospheric responses to urbanization, the availability of this globally consistent urban description is a crucial prerequisite for advancing model development and informing climate-sensitive urban planning policies. The GloUCP dataset, converted to WRF binary file format, is available for download at https://doi.org/10.6084/m9.figshare.27011491 (Liao et al., 2024). [ABSTRACT FROM AUTHOR]

High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.11247599 (Cheng et al., 2024). [ABSTRACT FROM AUTHOR]

This research examines the interplay of outdoor thermal comfort, walkability, and three-dimensional geospatial landscape within cities. Employing advanced data collection methods, including smart wearables and street view imagery (SVI), we conduct a comprehensive exploration of integrating heterogeneous sensor data and computer vision in an urban digital twin (UDT) we develop. We focus on the integrated thermal walk concept, where participants, equipped with wearables, contribute health metrics and real-time thermal sensation feedback while walking along selected routes for which we collect detailed 3D geoinformation, which in turn provides a foundation for understanding and improving comfort and walkability in cities. Our study not only addresses the integration, but also underscores the transformative role of advanced analytics and UDT in deciphering how urban morphology influences thermal experiences. While the growing accessibility of wearable devices facilitates work such as ours, we highlight challenges in collecting diverse participant data and the imperative need for specialized expertise in UDT and computer vision. This research contributes to (1) digital twins, providing a novel combination of data integration and a new use case, and to (2) urban climatology, advancing our understanding of the relationship among microclimate, urban environment, and outdoor thermal comfort. Beyond theoretical contributions, the study's practical significance is revealed in the development of accurate predictive models for understanding walkability, promising improvements in the quality of urban life, and pronouncing the important role of 3D geoinformation in such a domain. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes amixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote sensing images. This framework consists of a two-stream deep network, including a pure scene classification network (PS-Net) and a mixed scene unmixing network (MSU-Net). We conducted random sampling tests in Wuhan, China in the experiment A. The results show that this model achieved a satisfactory accuracy with the Overall Accuracies (OAs) is 96.78% and a mixed scene unmixing Mean Absolute Error (MAE) of 0.0495. Furthermore, we applied the proposed model to generate LCZ map for five districts in Wuhan in the experiment B. The test accuracy between two experiments differs very slightly. These results demonstrate the applicability and potential of our model for LCZ mapping and urban climate analysis. [ABSTRACT FROM AUTHOR]

As the frequency and intensity of heatwaves will continue to increase in the future, accurate and high-resolution mapping and forecasting of human outdoor thermal comfort in urban environments are of great importance. This study presents a machine-learning-based outdoor thermal comfort model with a good trade-off between computational cost, complexity, and accuracy compared to common numerical urban climate models. The machine learning approach is basically an emulation of different numerical urban climate models. The final model consists of four submodels that predict air temperature, relative humidity, wind speed, and mean radiant temperature based on meteorological forcing and geospatial data on building forms, land cover, and vegetation. These variables are then combined into a thermal index (universal thermal climate index – UTCI). All four submodel predictions and the final model output are evaluated using street-level measurements from a dense urban sensor network in Freiburg, Germany. The final model has a mean absolute error of 2.3 K. Based on a city-wide simulation for Freiburg, we demonstrate that the model is fast and versatile enough to simulate multiple years at hourly time steps to predict street-level UTCI at 1 m spatial resolution for an entire city. Simulations indicate that neighbourhood-averaged thermal comfort conditions vary widely between neighbourhoods, even if they are attributed to the same local climate zones, for example, due to differences in age and degree of urban vegetation. Simulations also show contrasting differences in the location of hotspots during the day and at night. [ABSTRACT FROM AUTHOR]

Measurements of radiative and turbulent heat fluxes for 16 months in suburban Miyun with a mix of buildings and agriculture allows the changing role of these fluxes to be assessed. Daytime turbulent latent heat fluxes (QE) are largest in summer and smaller in winter, consistent with the net all-wave radiation (Q*), whereas the daytime sensible heat flux (QH) is greatest in spring but smallest in summer rather than in winter, as commonly observed in suburban areas. The results have larger seasonal variability in energy partitioning compared to previous suburban studies. Daytime energy partitioning is between 0.15–0.57 for QH/Q* (mean summer = 0.16; winter = 0.46), 0.06–0.56 for QE/Q* (mean summer = 0.52; winter = 0.10), and 0.26–7.40 for QH/QE (mean summer = 0.32; winter = 4.60). Compared to the literature for suburban areas, these are amongst the lowest and highest values. Results indicate that precipitation, irrigation, vegetation growth activity, and land use and land cover all play critical roles in the energy partitioning. These results will help to enhance our understanding of surface–atmosphere energy exchanges over cities and are critical to improving and evaluating urban canopy models needed to support integrated urban services that include urban planning to mitigate the adverse effects of urban climate change. [ABSTRACT FROM AUTHOR]

As the frequency and intensity of heat waves will continue to increase in the future, accurate and high-resolution mapping and forecasting of human outdoor thermal comfort in urban environments is of great importance. This study presents a machine learning based outdoor thermal comfort model with a good trade-off between computational cost, complexity, and accuracy compared to common numerical urban climate models. The machine learning approach is basically an emulation of different numerical urban climate models. The final model consists of four sub-models that predict air temperature, relative humidity, wind speed, and mean radiant temperature based on meteorological forcing and geospatial data on building form, land cover, and vegetation. These variables are then combined into a thermal index (Universal Thermal Climate Index - UTCI). All four sub-model predictions and the final model output are evaluated using street-level measurements from a dense urban sensor network in Freiburg, Germany. The final model has a mean absolute error of 2.3 K. Based on a city-wide simulation for the city of Freiburg we demonstrate that the model is fast and versatile enough to simulate multiple years at hourly timesteps to predict street-level UTCI at 1 m spatial resolution for an entire city. Simulations indicate that neighborhood-averaged thermal comfort conditions vary widely between neighborhoods, even if they are attributed to the same local climate zones, e.g. due to differences in age and degree of urban vegetation. Simulations also show contrasting differences in the location of hot spots during the day and at night. [ABSTRACT FROM AUTHOR]

Green roofs can mitigate heat, increase biodiversity, and attenuate storm water, giving some of the benefits of natural vegetation in an urban context where ground space is scarce. To guide the design of more sustainable and climate-resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is needed, but this information is currently lacking. Segmentation algorithms have been used widely to identify buildings and land cover in aerial imagery. Using a machine learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset (10.5281/zenodo.7603123,). We estimate that there was 0.23 km 2 of green roof in the Central Activities Zone (CAZ) of London, 1.07 km 2 in Inner London, and 1.89 km 2 in Greater London in the year 2021. This corresponds to 2.0 % of the total building footprint area in the CAZ and 1.3 % in Inner London. There is a relatively higher concentration of green roofs in the City of London, covering 3.9 % of the total building footprint area. Test set accuracy was 0.99, with an F score of 0.58. When tested against imagery and labels from a different year (2019), the model performed just as well as a model trained on the imagery and labels from that year, showing that the model generalised well between different imagery. We improve on previous studies by including more negative examples in the training data and by requiring coincidence between vector building footprints and green roof patches. We experimented with different data augmentation methods and found a small improvement in performance when applying random elastic deformations, colour shifts, gamma adjustments, and rotations to the imagery. The survey covers 1558 km 2 of Greater London, making this the largest open automatic survey of green roofs in any city. The geospatial dataset is at the single-building level, providing a higher level of detail over the larger area compared to what was already available. This dataset will enable future work exploring the potential of green roofs in London and on urban climate modelling. [ABSTRACT FROM AUTHOR]

Impacts of climate change and air pollutants are a growing concern. Reliable and accessible monitoring systems to assess air quality and climate extremes are essential to inform decision-makers and increase awareness of citizens. Approaches from Volunteered Geography play a pivotal role both in research and empowerment by new low-cost technologies. Recently, the spread of GeoICT and micro-sensors are offering opportunities for mobile environmental mapping. In general, official stations acquire data with high accuracy and reliability; in contrast low-cost mobile devices increase the spatio-temporal resolution of air sampling but with lower accuracy. Aims of study are i) assessing accuracy of temperature values from Sodaq Air and MeteoTracker devices; ii) assessing accuracy on PM 2.5 acquisition for Sodaq Air; iii) geovisualizing three months of environmental monitoring in the city of Padua. Accuracy assessment for air temperature was performed by using a calibrated thermometer; PM 2.5 from Sodaq Air were compared with an official air quality station. Preliminary results on dynamic mobile mapping indicate that temperature values from MeteoTracker present good accuracy, while those from Sodaq Air showed bias of approximately +2.5 °C. Air quality data from the latter seems to present, in this phase of development, some limitations, since comparative analysis with official air quality station indicates 93% of overestimation, on average. On the other hand, the environmental campaign with mobile mapping devices at urban scale highlights the capability of geovisualizing hotspots and densifying georeferenced data acquisition over space and time. Further software/hardware implementation and applied research are required with various devices in different environmental conditions to improve data quality and reliability. [ABSTRACT FROM AUTHOR]

Cities concentrate people, wealth, emissions, and infrastructure, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This urgently demands for accurate urban climate projections to help organizations and individuals to make climate-smart decisions. However, most of the large ensembles of global and regional climate model simulations do not include sophisticated urban parameterizations (e.g., EURO-CORDEX; CMIP5/6). Here, we explore this shortcoming in ERA5 (the latest generation reanalysis from the European Centre for Medium-Range Weather Forecasts) and in a simulation with the SURFEX (Surface Externalisée) land surface model employing the widely used bulk bare rock approach. The city of Paris is considered as a case study. Subsequently, we apply a more complex urban scheme – SURFEX coupled to the Town Energy Balance (TEB) urban canopy model to assess its benefits on characterizing the Paris urban climate. Both simulations and ERA5 were compared to the LSA SAF (Satellite Application Facility on Land Surface Analysis) land surface temperature product to evaluate the simulation of Parisian surface urban heat island (SUHI). Our results show a significant added value of SURFEX-TEB in reproducing the SUHI during the daytime and the UHI during both the daytime and nighttime (with overall reductions in the bias and root mean square error and improvements in the representation of the statistics of the SUHI/UHI displayed by the Perkins skill score or S score). The improvement in the simulated SUHI is lower during the nighttime due to the lack of land–atmosphere feedbacks in the proposed offline framework. Nonetheless, the offline SURFEX-TEB framework applied here clearly demonstrates the added value of using more comprehensive parameterization schemes to simulate the urban climate and, therefore, allowing the improvement of urban climate projections. [ABSTRACT FROM AUTHOR]

A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022). [ABSTRACT FROM AUTHOR]

In recent years, the concept of Digital Twin (DT) for cities is increasingly at the core of most smart city initiatives, as it has been identified as a critical tool for tackling the challenges of this century. A robust city modelling framework is essential if local, state and national governments are to move towards sustainable built environments and work together across complex multi-sectoral problems to drive impacts that improve urban liveability and climate adaptability. Furthermore, the level of collaboration and interoperability required to address these cannot be achieved without proper standardisation of DT components. The aim of this project is to develop a demonstration DT that integrates existing data using a standardised 3D format based on CityGML and that embeds analytics, such as sun exposure and tree coverage, to assess liveability within a 3D city modelling framework. Common urban features such as buildings, roads, railways, vegetation and water bodies are also processed and incorporated. Additionally, IoT sensors are integrated into the model and all processes are performed using open-source tools to improve accessibility and repeatability. Details of the workflow, including the storage of the city features in a 3D City Database (3DCityDB), the 3D upgrading of urban features commonly available as 2D data as well as a few use cases are illustrated and discussed in this paper. [ABSTRACT FROM AUTHOR]

Fast population growth is a significant contributor to worldwide land scarcity and has a long-term impact on the environment and climate. Rapid urbanisation along with industrialisation contributed to land transformation across the globe. Understanding, monitoring, and mitigating the environmental impacts of urbanisation are critical for sustainable development. Also, Rapid urbanisation has contributed significantly to regional and global temperature. Land surface temperature (LST) has long been regarded as a critical physical metric for analysing regional and global climate change and its effects on many ecosystems. Remote sensing is an effective method for monitoring and quantifying urbanisation and assessing its impact on regional heating. This paper examines the combined impact of land use and land cover (LULC) change and climatic variability in Bhubaneswar city using remote sensing, geographic information system and google earth engine. Images from the Landsat were utilised to prepare the LULC, LST and NDVI layer for 2001, 2010 and 2020. The random forest method of supervised classification was employed to estimate the LULC. According to this study, the impact of urbanisation shows that the built-up areas in Bhubaneswar City have increased by over 70% at the cost of agricultural land and vegetation cover. The finding also indicates the urban area has been expanded around 12 percent where LST has increased by 4°C in the last two decades. As a result, continuous monitoring of LULC dynamics is required to guide sustainable land-use decisions that promote environmental protection and economic development in this region. A multi-temporal environmental analysis of the region is critical for future environmental planning, such as the restoration of damaged areas, to be more effective and efficient. This study, thus, would be helpful for the administrators, urban planners, and policymakers for proper planning and sustainable development of Bhubaneswar City. [ABSTRACT FROM AUTHOR]

Green roofs are roofs incorporating a deliberate layer of growing substrate and vegetation. They can reduce both indoor and outdoor temperatures, so are often presented as a strategy to reduce urban overheating, which is expected to increase due to climate change and urban growth. In addition, they could help decrease the cooling energy demand of buildings thereby contributing to energy and emissions reductions, and provide benefits to biodiversity and human well-being. To guide the design of more sustainable and climate resilient buildings and neighbourhoods, there is a need to assess the existing status of green roof coverage and explore the potential for future implementation. Therefore, accurate information on the prevalence and characteristics of existing green roofs is required to estimate any effect of green roofs on temperatures (or other phenomena), but this information is currently lacking. Using a machine-learning algorithm based on U-Net to segment aerial imagery, we surveyed the area and coverage of green roofs in London, producing a geospatial dataset (Simpson et al., 2022). We estimate that there was 0.19 km2 of green roof in the Central Activities Zone (CAZ) of London, (0.81 km2) in Inner London, and (1.25 km2) in Greater London in the year 2019. This corresponds to 1.6% of the total building footprint area in the CAZ, and 1.0% in Inner London. There is a relatively higher concentration of green roofs in the City of London (the historic financial district), covering 3.1% of the total building footprint area. The survey covers 1463 km2 of Greater London, making this the largest open automatic survey of green roofs in any city. We improve on previous studies by including more negative examples in the training data, by experimenting with different data augmentation methods, and by requiring coincidence between vector building footprints and green roof patches. This dataset will enable future work examining the distribution and potential of green roofs in London and on urban climate modelling. [ABSTRACT FROM AUTHOR]

China is experiencing the largest and fastest urbanization process in the world (Kneebone E., 2013). At the same time, its current rapid urbanization process is almost simultaneously accompanied by urban sprawl. Since the 1990s, the sprawl process of Chinese cities has begun to reach a climax (Shan Baoguo, 2018). As the largest developing country in the world, China is also one of the countries most vulnerable to the coercion of climate change. This research takes the four municipalities that are China's urban development orientation as typical representatives, and uses multiple indicators to measure urban sprawl and climate change in them. Finally, models are established to explore the impact of urban sprawl on climate warming. The results showed that all four cities experienced sprawl, but to varying degrees. Shanghai is very compact. Also, the climate warming is definitely, yet urban sprawl doesn't always contribute to its deterioration. The proportion of arable land had the least impact on global warming, but it was the only factor that could improve temperatures, albeit conditionally. Fragmented built-up land heating the climate is most critical. [ABSTRACT FROM AUTHOR]

Cities concentrate people, wealth, emissions, and infrastructures, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This places an urgent demand for accurate urban climate projections to help organizations and individuals making climate smart-decisions. However, most of the state-of-the-art global and regional climate models have an oversimplified representation of (or completely neglect) urban climate processes. Here, we use the city of Paris as a case study to show that this is the case for the fifth (and latest) generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and for simulations employing the widely used bulk bare rock approach to urban climate parameterization. Subsequently, we leveraged on the hourly resolution of ERA5 and the Satellite Application Facility Land Surface Analysis (LSA-SAF) land surface temperature product to demonstrate the significant added value of employing the SURFEX land-surface model coupled to Town Energy Balance (TEB) urban canopy model in simulating the Parisian Surface Urban Heat Island (SUHI) during daytime and the urban heat island during both daytime and nighttime. Our results showed the significant added value of SURFEX-TEB in reproducing the observed daytime and nighttime Parisian urban heat island effect. An annual average bias magnitude reduction of 0.5 °C was observed for daytime and around 1.5 °C for nighttime when compared to ERA5 and bare rock approach. Also, SURFEX-TEB revealed an overall better performance in reproducing the observed daytime SUHI, whilst the added value of SURFEX-TEB was lower during nighttime (but still slightly better than ERA5 and the bare rock approach), due to the lack of land-atmosphere feedbacks in the proposed offline framework. Finally, the offline SURFEX-TEB framework applied here demonstrates the ability to simulate the urban climate, which is an asset to build urban climate projections that allow the development of mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]

Including radiative transfer processes within the urban canopy layer into microscale urban climate models (UCMs) is essential to obtain realistic model results. These processes include the interaction of buildings and vegetation with shortwave and longwave radiation, thermal emission, and radiation reflections. They contribute differently to the radiation budget of urban surfaces. Each process requires different computational resources and physical data for the urban elements. This study investigates how much detail modellers should include to parameterize radiative transfer in microscale building-resolving UCMs. To that end, we introduce a stepwise parameterization method to the Parallelized Large-eddy Simulation Model (PALM) system 6.0 to quantify individually the effects of the main radiative transfer processes on the radiation budget and on the flow field. We quantify numerical simulations of both simple and realistic urban configurations to identify the major and the minor effects of radiative transfer processes on the radiation budget. The study shows that processes such as surface and vegetation interaction with shortwave and longwave radiation will have major effects, while a process such as multiple reflections will have minor effects. The study also shows that radiative transfer processes within the canopy layer implicitly affect the incoming radiation since the radiative transfer model is coupled to the radiation model. The flow field changes considerably in response to the radiative transfer processes included in the model. The study identified those processes which are essentially needed to assure acceptable quality of the flow field. These processes are receiving radiation from atmosphere based on the sky-view factors, interaction of urban vegetation with radiation, radiative transfer among urban surfaces, and considering at least single reflection of radiation. Omitting any of these processes may lead to high uncertainties in the model results. [ABSTRACT FROM AUTHOR]

The combination between dense point clouds and 3D vector objects permits new cartographic representation of urban information. This paper proposes an extension for the CityJSON encoding to support point clouds. Following the 3.0 CityGML specifications, attributes and features are added to the core module of v1.0.1 CityJSON. Two solutions are proposed: inline complex geometries and external link to a remote file. The extended schema can be illustrated in four scenarios: detailed features visualization, fall-back solution in features reconstruction processes, simulating urban climate represented as vector fields, and true-to-life representation solution for complex elements such as solitary vegetation objects. It permits 3D city modelers to handle points clouds in a native way reducing files size and avoiding redundancy. All developments and documentation are available open-source. [ABSTRACT FROM AUTHOR]

More and more uses and applications are being given to local climate zone (LCZ) maps, which describe the structure of the urban and semi-urban areas. Among others, it is worth highlighting its use in studies of urban heat islands (UHI), sustainability and urban energy balance. Even if the classes are well described in the literature, it is difficult to estimate the general precision of these classification maps because the classification is highly dependent of the urban typology of the city under study. However, LCZ maps represent a reference in the field of urban climatology. This research work aims to make use of these maps to explain the strong influence of LCZ classes on land surface temperature (LST) and, consequently, on air temperature (AT). This kind of investigations will help us to explain the outliers observed in previous work between LST and AT at specific locations in the city of Strasbourg for the period 2012–2019. The LST data were obtained from the thermal infrared data of both ASTER (with 90-m spatial resolution and 16-days temporal resolution) and MODIS satellite (with 1-km spatial resolution and daily revisit period). The reference ATs were obtained from different field measurement provided by a huge network of meteorological stations distributed in the city of Strasbourg. The comparison of measured ATs and remote LSTs provide the opportunity to thoroughly evaluate the relationship between these two parameters both during the day and night, for different land covers and for different times of the year. Finally, UHI maps of Strasbourg for every season are presented. [ABSTRACT FROM AUTHOR]

The urban climate literature has highlighted the remarkable prominence of nighttime UHI phenomenon. During nighttime the UHI effects become more evident due to the greater thermal inertia of the materials used in urban fabric. It is during the night when the heat accumulated in urban materials, especially in contexts of heat waves, can generate significant health risks. The low cooling capacity of urban construction materials negatively affects the comfort and the health of urban dwellers. However, and despite the great importance of night stress due to heat, the study of night UHIs is still underdeveloped. In this context, this paper aims to determine nighttime LST contrasting Landsat's very limited nighttime images with daytime ones. The example developed refers to heat wave situations during the summer 2015. The case study is the Metropolitan Area of Barcelona (35 municipalities, 636 km2, 3.3 million inhabitants). [ABSTRACT FROM AUTHOR]

The growth of computational power unleashed the potential of high-resolution urban climate simulations using limited-area models in recent years. This trend empowered us to deepen our understanding of urban-scale climatology with much finer spatial–temporal details. However, these high-resolution models would also be particularly sensitive to model uncertainties, especially in urbanizing cities where natural surface texture is changed artificially into impervious surfaces with extreme rapidity. These artificial changes always lead to dramatic changes in the land surface process. While models capturing detailed meteorological processes are being refined continuously, the input data quality has been the primary source of biases in modeling results but has received inadequate attention. To address this issue, we first examine the quality of the incoming static data in two cities in China, i.e., Shenzhen and Hong Kong SAR, provided by the WRF ARW model, a widely applied state-of-the-art mesoscale numerical weather simulation model. Shenzhen has gone through an unprecedented urbanization process in the past 30 years, and Hong Kong SAR is another well-urbanized city. A significant proportion of the incoming data is outdated, highlighting the necessity of conducting incoming data quality control in the region of Shenzhen and Hong Kong SAR. Therefore, we proposed a sophisticated methodology to develop a high-resolution land surface dataset in this region. We conducted urban climate simulations in this region using both the developed land surface dataset and the original dataset utilizing the WRF ARW model coupled with Noah LSM/SLUCM and evaluated the performance of modeling results. The performance of modeling results using the developed high-resolution urban land surface datasets is significantly improved compared to modeling results using the original land surface dataset in this region. This result demonstrates the necessity and effectiveness of the proposed methodology. Our results provide evidence of the effects of incoming land surface data quality on the accuracy of high-resolution urban climate simulations and emphasize the importance of the incoming data quality control. [ABSTRACT FROM AUTHOR]

Urban canopy models (UCMs) represent the exchange of momentum, heat, and moisture between cities and the atmosphere. Single-layer UCMs interact with the lowest atmospheric model level and are suited for low- to mid-rise cities, whereas multi-layer UCMs interact with multiple levels and can also be employed for high-rise cities. The present study describes the multi-layer coupling between the Town Energy Balance (TEB) UCM included in the Surface Externalisée (SURFEX) land surface model and the Meso-NH mesoscale atmospheric model. This is a step towards better high-resolution weather prediction for urban areas in the future and studies quantifying the impact of climate change adaptation measures in high-rise cities. The effect of the buildings on the wind is considered using a drag force and a production term in the prognostic equation for turbulent kinetic energy. The heat and moisture fluxes from the walls and the roofs to the atmosphere are released at the model levels intersecting these urban facets. No variety of building height at grid-point scale is considered to remain the consistency between the modification of the Meso-NH equations and the geometric assumptions of TEB. The multi-layer coupling is evaluated for the heterogeneous high-rise, high-density city of Hong Kong. It leads to a strong improvement of model results for near-surface air temperature and relative humidity, which is due to better consideration of the process of horizontal advection in the urban canopy layer. For wind speed, model results are improved on average by the multi-layer coupling but not for all stations. Future developments of the multi-layer SURFEX-TEB will focus on improving the calculation of radiative exchanges, which will allow a variety of building heights at grid-point scale to be taken into account. [ABSTRACT FROM AUTHOR]

Thermal emissions – or anthropogenic heat fluxes (QF) – from human activities impact urban climates at a local and larger scale. DASH considers both urban form and function in simulating QF through the use of an agent-based structure that includes behavioural characteristics of urban residents. This allows human activities to drive the calculation of QF , incorporating dynamic responses to environmental conditions. The spatial resolution of simulations depends on data availability. DASH has simple transport and building energy models to allow simulation of dynamic vehicle use, occupancy and heating–cooling demand, and release of energy to the outdoor environment through the building fabric. Building stock variations are captured using archetypes. Evaluation of DASH in Greater London for periods in 2015 uses a top-down inventory model (GQF) and national energy consumption statistics. DASH reproduces the expected spatial and temporal patterns of QF , but the annual average is smaller than published energy data. Overall, the model generally performs well, including for domestic appliance energy use. DASH could be coupled to an urban land surface model and/or used offline for developing coefficients for simpler/faster models. [ABSTRACT FROM AUTHOR]

There is a strong interaction between the urban and the building energy balance. The urban climate affects the heat transfer through exterior walls, the longwave heat transfer between the building surfaces and the surroundings, the shortwave solar heat gains and the heat transport by ventilation. Considering also the internal heat gains and the heat capacity of the building structure, the energy demand for heating and cooling and the indoor thermal environment can be calculated based on the urban climate. According to the building energy concept, the energy demand results in an (anthropogenic) waste heat, this is directly transferred to the urban environment. Furthermore, the indoor temperature is re-coupled via the building envelope to the urban environment and affects indirectly the urban climate with a time shifted and damped temperature fluctuation. We developed and implemented a holistic building model for the combined calculation of indoor climate and energy demand based on an analytic solution of Fourier's equation. The building model is integrated via an urban surface model into the urban climate model. [ABSTRACT FROM AUTHOR]

In order to understand and explain urban climate, the visual analysis of urban climate data and their relationships with the urban morphology is at stake. This involves partly to co-visualize 3D field climate data, obtained from simulation, with urban 3D models. We propose two ways to visualize and navigate into simulated climate data in urban 3D models, using series of horizontal 2D planes and 3D point clouds. We then explore different parameters regarding transparency, 3D semiologic rules, filtering and animation functions in order to improve the visual analysis of climate data 3D distribution. To achieve this, we apply our propositions to the co-visualization of air temperature data with a 3D urban city model. [ABSTRACT FROM AUTHOR]

Over the past decades, climate change has become among the top issues challenging cities worldwide, endangering the urban infrastructures and threatening the health of millions of people. Hence, climate action, both in terms of mitigation and adaptation to climate change, has become a priority for urban planning. This work introduces an example of the promising role that spatial analysis and statistical modelling, employing Geographical Information Systems (GIS) and freely available satellite and land-based data, can provide in supporting urban climate design and policymaking. In particular, this study puts special attention on the Urban Heat Island (UHI) phenomenon. Here, we first introduce a simple, but effective morphological-based approach for mapping potential ventilation corridors across cities of uniform built-up structure, as a common UHI mitigation measure. Then, we propose a methodology for assessing the relative role of these corridors in maximizing the impacts of green solutions upon lowering high temperature. Results show that even under very calm wind conditions, there is still an opportunity for maximizing the benefits of greening measures on the urban climate. Also, it has been demonstrated that green ventilation corridors are more effective during night-time when the UHI effect is peaked. The research findings are very promising, especially for cities where wind is a marginal potentiality. [ABSTRACT FROM AUTHOR]

Including radiative transfer processes within the urban canopy layer into microscale urban climate models (UCMs) is essential to obtain realistic model results. These processes include the interaction of buildings and vegetation with shortwave and longwave radiation, thermal emission, and radiation reflections. They contribute differently to the radiation budget of urban surfaces. Each process requires different computational resources and physical data for the urban elements. This study investigates how much detail modellers should include to parameterise radiative transfer in microscale building resolving UCMs. To that end, we introduce a stepwise parameterization method to the the PALM model system 6.0 to quantify individually the effects of the main radiative transfer processes on the radiation budget and on the flow field. We quantify numerical simulations of both simple and realistic urban configurations to identify the radiative transfer processes which have major effects on the radiation budget, such as surface and vegetation interaction with short wave and long wave radiation, and those which have minor effects, such as multiple reflections. The study also shows that radiative transfer processes within the canopy layer implicitly affect the incoming radiation since the radiative transfer model is coupled to the radiation model. The flow field changes considerably in response to the radiative transfer processes included in the model. The study highlights those processes which are essentially needed to assure acceptable quality of the flow field. Omitting any of these processes may lead to high uncertainties in the model results. [ABSTRACT FROM AUTHOR]

In the frame of the project "MOSAIK – Model-based city planning and application in climate change", a German-wide research project within the call "Urban Climate Under Change" (UC2) funded by the German Federal Ministry of Education and Research (BMBF), a biometeorology module was implemented into the Parallelized Large-Eddy Simulation Model (PALM) system. The new biometeorology module is comprised of methods for the calculation of UV-exposure quantities, a human–biometeorologically weighted mean radiant temperature (Tmrt), as well as for the estimation of human thermal comfort or stress. The latter is achieved through the implementation of the three widely used thermal indices: perceived temperature (PT), Universal Thermal Climate Index (UTCI), as well as physiologically equivalent temperature (PET). Comparison calculations were performed for the PT, UTCI and PET indices based on the SkyHelios model and showing PALM calculates higher values in general. This is mostly due to a higher radiational gain leading to higher values of mean radiant temperature. For a more direct comparison, the PT, PET and UTCI indices were calculated by the biometeorology module, as well as the programs provided by the attachment to Verein Deutscher Ingenieure (VDI) guideline 3787, as well as by the RayMan model based on the very same input dataset. Results show deviations below the relevant precision of 0.1 K for PET and UTCI and some deviations of up to 2.683 K for PT caused by repeated unfavorable rounding in very rare cases (0.027 %). [ABSTRACT FROM AUTHOR]

The urban heat island intensity (UHII) is the temperature difference between urban areas and their rural surroundings. It is commonly attributed to changes in the underlying surface structure caused by urbanization. Air pollution caused by aerosol particles can affect the UHII through changing (1) the surface energy balance by the aerosol radiative effect (ARE) and (2) planetary-boundary-layer (PBL) stability and airflow intensity by modifying thermodynamic structure, which is referred to as the aerosol dynamic effect (ADE). By analyzing satellite data and ground-based observations collected from 2001 to 2010 at 35 cities in China and using the WRF-Chem model, we find that the impact of aerosols on UHII differs considerably: reducing the UHII in summer but increasing the UHII in winter. This seasonal contrast is proposed to be caused by the different strengths of the ARE and ADE between summer and winter. In summer, the ARE on UHII is dominant over the ADE, cooling down surface temperature more strongly in urban areas than in rural areas because of much higher aerosol loading, and offsets the urban heating, therefore weakening UHII. In winter, however, the ADE is more dominant, because aerosols stabilize the PBL more in the polluted condition, weakening the near-surface heat transport over urban areas in both vertical and horizontal directions. This means that the heat accumulated in urban areas is dispersed less effectively, and thus the UHII is enhanced. These findings shed new light on the impact of the interaction between urbanization-induced surface changes and air pollution on urban climate. [ABSTRACT FROM AUTHOR]

Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local and larger scales urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, socio-economic factors and in response to environmental conditions. The spatial resolution depends on data availability. DASH has simple transport and building energy models to allow simulation of dynamic vehicle use, occupancy and heating/cooling demand, with subsequent release of energy to the outdoor environment through the building fabric. Building stock variations are captured using archetypes. Evaluation of DASH in Greater London for various periods in 2015 uses a top-down inventory model (GQF) and national energy consumption statistics. DASH reproduces the expected spatial and temporal patterns of QF but the annual average is smaller than published energy data. Overall the model generally performs well, including for domestic appliance energy use against top down model results. DASH could be coupled to an urban land surface model and/or used offline for developing coefficients for simpler/faster models. [ABSTRACT FROM AUTHOR]

Growing computational power in recent years enabled high-resolution urban climate simulations using limited-area models to flourish. This trend empowered us to deepen our understanding of urban-scale climatology with much finer spatial-temporal details. However, these high-resolution models would also be particularly sensitive to model uncertainties, especially in urbanizing cities where natural surface texture is changed artificially into impervious surfaces with extreme rapidity, and these artificial changes always lead to dramatic changes in the land surface process. While models capturing detailed meteorological processes are being refined continuously, the input data quality has been the primary source of biases in modeling results but has received inadequate attention. To address this issue, we first examine the quality of the incoming static data in two cities in China, i.e., Shenzhen and Hong Kong SAR, provided by the WRF ARW model, a widely-applied state-of-the-art mesoscale numerical weather simulation model. Shenzhen was going through an unprecedented urbanization process in the past thirty years, and Hong Kong SAR is another well-urbanized city. A significant proportion of the incoming data are found out-dated, which highlights the necessity of conducting incoming data quality control in the region of Shenzhen and Hong Kong SAR. Then, we proposed a sophisticated methodology to develop a high-resolution land surface dataset in this region. We conducted urban climate simulations in this region using both the developed land surface dataset and the original dataset utilizing the WRF ARW model coupled with Noah LSM/SLUCM and evaluated the reliability of modeling results. The reliability of modeling results using the developed high-resolution urban land surface datasets is significantly improved compared to modeling results using the original land surface dataset in this region. This result demonstrates the necessity and effectiveness of the proposed methodology. Our results provide evidence on the effects of incoming land surface data quality on the accuracy of high-resolution urban climate simulations and emphasize the importance of the incoming data quality control. [ABSTRACT FROM AUTHOR]

Street trees are more and more regarded as an effective measure to reduce excessive heat in urban areas. However, the vast majority of mesoscale urban climate models do not represent street trees in an explicit manner and, for example, do not take the important effect of shading by trees into account. In addition, urban canopy models that take interactions of trees and urban fabrics directly into account are usually limited to the street or neighbourhood scale and hence cannot be used to analyse the citywide effect of urban greening. In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the Building Effect Parameterisation with Trees (BEP-Tree) vegetated urban canopy model and the Consortium for Small-scale Modeling (COSMO) mesoscale weather and climate model. The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June–July 2015. Overall, the model compared well with measurements of individual components of the surface energy balance and with air and surface temperatures obtained from a flux tower, surface stations and satellites. Deficiencies were identified for nighttime air temperature and humidity, which can mainly be traced back to limitations in the simulation of the nighttime stable boundary layer in COSMO. The representation of street trees in the coupled model generally improved the agreement with observations. Street trees produced large changes in simulated sensible and latent heat flux, and wind speed. Within the canopy layer, the presence of street trees resulted in a slight reduction in daytime air temperature and a very minor increase in nighttime air temperature. The model was found to realistically respond to changes in the parameters defining the street trees: leaf area density and stomatal conductance. Overall, COSMO-BEP-Tree demonstrated the potential of (a) enabling city-wide studies on the cooling potential of street trees and (b) further enhancing the modelling capabilities and performance in urban climate modelling studies. [ABSTRACT FROM AUTHOR]

The Town Energy Balance (TEB) urban climate model has recently been improved to more realistically address the radiative effects of trees within the urban canopy. These processes necessarily have an impact on the energy balance that needs to be taken into account. This is why a new method for calculating the turbulent fluxes for sensible and latent heat has been implemented. This method remains consistent with the "bigleaf" approach of the Interaction Soil–Biosphere–Atmosphere (ISBA) model, which deals with energy exchanges between vegetation and atmosphere within TEB. Nonetheless, the turbulent fluxes can now be dissociated between ground-based natural covers and the tree stratum above (knowing the vertical leaf density profile), which can modify the vertical profile in air temperature and humidity in the urban canopy. In addition, the aeraulic effect of trees is added, parameterized as a drag term and an energy dissipation term in the evolution equations of momentum and turbulent kinetic energy, respectively. This set of modifications relating to the explicit representation of the tree stratum in TEB is evaluated on an experimental case study. The model results are compared to micrometeorological and surface temperature measurements collected in a semi-open courtyard with trees and bordered by buildings. The new parameterizations improve the modeling of surface temperatures of walls and pavements, thanks to taking into account radiation absorption by trees, and of air temperature. The effect of wind speed being strongly slowed down by trees is also much more realistic. The universal thermal climate index diagnosed in TEB from inside-canyon environmental variables is highly dependent and sensitive to these variations in wind speed and radiation. This demonstrates the importance of properly modeling interactions between buildings and trees in urban environments, especially for climate-sensitive design issues. [ABSTRACT FROM AUTHOR]

Increasing urbanization is likely to intensify the urban heat island effect, decrease outdoor thermal comfort, and enhance runoff generation in cities. Urban green spaces are often proposed as a mitigation strategy to counteract these adverse effects, and many recent developments of urban climate models focus on the inclusion of green and blue infrastructure to inform urban planning. However, many models still lack the ability to account for different plant types and oversimplify the interactions between the built environment, vegetation, and hydrology. In this study, we present an urban ecohydrological model, Urban Tethys-Chloris (UT&C), that combines principles of ecosystem modelling with an urban canopy scheme accounting for the biophysical and ecophysiological characteristics of roof vegetation, ground vegetation, and urban trees. UT&C is a fully coupled energy and water balance model that calculates 2 m air temperature, 2 m humidity, and surface temperatures based on the infinite urban canyon approach. It further calculates the urban hydrological fluxes in the absence of snow, including transpiration as a function of plant photosynthesis. Hence, UT&C accounts for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements of eddy-covariance towers located in three cities in different climates (Singapore, Melbourne, and Phoenix). A sensitivity analysis, performed as a proof of concept for the city of Singapore, shows a mean decrease in 2 m air temperature of 1.1 ∘ C for fully grass-covered ground, 0.2 ∘ C for high values of leaf area index (LAI), and 0.3 ∘ C for high values of Vc,max (an expression of photosynthetic capacity). These reductions in temperature were combined with a simultaneous increase in relative humidity by 6.5 % , 2.1 % , and 1.6 % , for fully grass-covered ground, high values of LAI, and high values of Vc,max , respectively. Furthermore, the increase of pervious vegetated ground is able to significantly reduce surface runoff. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is one of the important factors in monitoring urban climate. Observing the variations of LST can provide a better understanding of the Urban Heat Islands (UHI) phenomenon. The aim of this research is to assess the relationship between the spatial and temporal distribution of LST and water consumption in Zamboanga City for years 2016 and 2017. Data from the city's water district were used to compute for the per capita water consumption (PCWC) of 49 barangays. Landsat 8 LST data with 30m spatial resolution were computed using inverse Plank function and other parameters such as vegetation proportion and surface emissivity to assess LST spatially while MODIS Terra data with 1km spatial resolution were used to assess LST temporally. Result showed that Landsat LST and PCWC have moderate correlations with p < 0.01: 0.59 and 0.55 for March and April 2016, respectively; 0.49 and 0.56 for March and April 2017, respectively. These indicated that warmer barangays consumed more water. The temporal correlation of the MODIS LST and the computed PCWC equated a −0.71, p < 0.01, correlation. This negative correlation indicated that when LST increases, PCWC decreases, which do not directly indicate that the city consumed less water but rather that the supply was less during warmer months. It was evident as water rationing was experienced during the first quarter of 2016 and intensified on April where the highest LST was recorded. Finally, LST was found of good use in assessing the relationship of temperature and water consumption. [ABSTRACT FROM AUTHOR]

The veracity of urban climate simulation models should be systematically evaluated to demonstrate the trustworthiness of these models against possible model uncertainties. However, existing studies paid insufficient attention to model evaluation; most studies only provided some simple comparison lines between modelled variables and their corresponding observed ones on the temporal dimension. Challenges remain since such simple comparisons cannot concretely prove that the simulation of urban climate behaviours is reliable. Studies without systematic model evaluations, being ambiguous or arbitrary to some extent, may lead to some seemingly new but scientifically misleading findings. To tackle these challenges, this article proposes a methodological framework for the model evaluation of high-resolution urban climate simulations and demonstrates its effectiveness with a case study in the area of Shenzhen and Hong Kong SAR, China. It is intended to (again) remind urban climate modellers of the necessity of conducting systematic model evaluations with urban-scale climatology modelling and reduce these ambiguous or arbitrary modelling practices. [ABSTRACT FROM AUTHOR]

Increasing urbanization is likely to intensify the urban heat island effect, decrease outdoor thermal comfort and enhance runoff generation in cities. Urban green spaces are often proposed as a mitigation strategy to counteract these adverse effects and many recent developments of urban climate models focus on the inclusion of green and blue infrastructure to inform urban planning. However, many models still lack the ability to account for different plant types and oversimplify the interactions between the built environment, vegetation, and hydrology. In this study, we present an urban ecohydrological model, Urban Tethys-Chloris (UT&C), that combines principles of ecosystem modelling with an urban canopy scheme accounting for the biophysical and ecophysiological characteristics of roof vegetation, ground vegetation and urban trees. UT&C is a fully coupled energy and water balance model that calculates 2 m air temperature, 2 m humidity, and surface temperatures based on the infinite urban canyon approach. It further calculates all urban hydrological fluxes, including transpiration as a function of plant photosynthesis. Hence, UT&C accounts for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements of eddy covariance towers located in three cities in different climates (Singapore, Melbourne, Phoenix). A sensitivity analysis, performed as a proof of concept for the city of Singapore, shows a mean decrease in 2 m air temperature of 1.1 °C for fully grass covered ground, 0.2 °C for high values of leaf area index (LAI), and 0.3 °C for high values of Vçmax (an expression of photosynthetic activity). These reductions in temperature were combined with a simultaneous increase in relative humidity by 6.5 %, 2.1 %, and 1.6 %, for fully grass covered ground, high values of LAI, and high values of Vçmax, respectively. Furthermore, the increase of pervious vegetated ground is able to significantly reduce surface runoff. These results show that urban greening can lead to a decrease in urban air temperature and surface runoff, but this effect is limited in cities characterized by a hot, humid climate. [ABSTRACT FROM AUTHOR]

Street trees are more and more regarded as an effective measure to reduce excessive heat in urban areas. However, the vast majority of mesoscale urban climate models do not represent street trees in an explicit manner and for example do not take the important effect of shading by trees into account. In addition, urban canopy models that take interactions of trees and urban fabrics directly into account are usually limited to the street or neighbourhood scale and, hence, cannot be used to analyse the citywide effect of urban greening. In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEP-Tree and the mesoscale weather and climate model COSMO. The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015. Overall, the model compared well with measurements of individual components of the surface energy balance and with air and surface temperatures obtained from a flux tower, surface stations and satellites. Deficiencies were identified for night-time air temperature and humidity, which can mainly be traced back to limitations in the simulation of the night-time stable boundary layer in COSMO. The representation of street trees in the coupled model generally improved the agreement with observations. Street trees produced large changes in simulated sensible and latent heat flux, and wind speed. Within the canopy layer, the presence of street trees resulted in a slight reduction in daytime air temperature and a very minor increase in night-time air temperature. The model was found to realistically respond to changes in the parameters defining the street trees: leaf area density and stomatal conductance. Overall, COSMO-BEP-Tree demonstrated the potential of (a) enabling city-wide studies on the cooling potential of street trees and (b) further enhancing the modelling capabilities and performance in urban climate modelling studies. [ABSTRACT FROM AUTHOR]

change", a German-wide research project within the call "Urban Climate Under Change" ([UC]²) funded by the German Federal Ministry of Education and Research (BMBF), a biometeorology module was implemented into the PALM model system. The new biometeorology module comprises of methods for the calculation of uv-exposure quantities, a human-biometeorologically weighted mean radiant temperature (Tmrt), as well as for the estimation of human thermal comfort or stress. The latter is achieved through the implementation of the three widely-used thermal indices Perceived Temperature (PT), Universal Thermal Climate Index (UTCI), as well as Physiologically Equivalent Temperature (PET) together with a newly developed instationary index instationary Perceived Temperature (iPT) based on PT for use with the multi-agent model. Comparison calculations were performed for the indices PT, UTCI and PET based on the SkyHelios model and showing PALM calculates higher values in general. This is mostly due to a higher radiational gain leading to higher values of mean radiant temperature. For a more direct comparison, the indices PT, PET and UTCI were calculated by the biometeorology module, as well as the programs provided by the attachment to the VDI guideline 3787, as well as by the RayMan model based on the very same input dataset. Results show deviations below rounding precision (less than 0.1 K) for PET and UTCI and some deviations of up to 2.683 K for PT caused by rounding leading to the selection of a different clothing insulation step in very rare cases (0.027 %). [ABSTRACT FROM AUTHOR]

Accurate and agile modelling of cities weather, climate, hydrology and air quality is essential for integrated urban services. The Surface Urban Energy and Water balance Scheme (SUEWS) is a state-of-the-art widely used urban land surface model (ULSM) which simulates urban–atmospheric interactions by quantifying the energy, water and mass fluxes. Using SUEWS as the computation kernel, SuPy (SUEWS in Python) uses a Python-based data stack to streamline the pre-processing, computation and post-processing that are involved in the common modelling-centred urban climate studies. This paper documents the development of SuPy, including the SUEWS interface modification, F2PY (Fortran to Python) configuration and Python front-end implementation. In addition, the deployment of SuPy via PyPI (Python Package Index) is introduced along with the automated workflow for cross-platform compilation. This makes SuPy available for all mainstream operating systems (Windows, Linux and macOS). Three online tutorials in Jupyter Notebook are provided to users of different levels to become familiar with SuPy urban climate modelling. The SuPy package represents a significant enhancement that supports existing and new model applications, reproducibility and enhanced functionality. [ABSTRACT FROM AUTHOR]

The TEB urban climate model has recently been improved to more realistically address the radiative effects of trees within the urban canopy. These processes necessarily have an impact on the energy balance that needs to be taken into account. This is why a new method for calculating the turbulent fluxes for sensible and latent heat has been implemented. This method remains consistent with the "bigleaf" approach of the ISBA model which deals with energy exchanges between vegetation and atmosphere within TEB. Nonetheless, the turbulent fluxes can now be dissociated between ground-based natural covers and tree stratum above (knowing the vertical leaf density profile), which can modify the vertical profile in air temperature and humidity in the urban canopy. In addition, the aeraulic effect of trees is added, parameterized as a drag term and an energy dissipation term in the evolution equations of momentum and of turbulent kinetic energy, respectively. This set of modifications relating to the explicit representation of tree stratum in TEB is evaluated on an experimental case study. The model results are compared to micrometeorological and surface temperature measurements collected in a semi-open courtyard with trees and bordered by buildings. The new parameterizations improve the modelling of surface temperatures of walls and pavements thanks to taking into account radiation absorption by trees, and of air temperature. The wind speed is strongly slowed down by trees that is also much more realistic. The universal thermal climate index diagnosed in TEB from inside-canyon environmental variables is highly dependent and sensitive to these variations in wind speed and radiation. This demonstrates the importance of properly modelling interactions between buildings and trees in urban environments, especially for climate-sensitive design issues. [ABSTRACT FROM AUTHOR]

Accurate and agile modelling of the climate of cities is essential for urban climate services. The Surface Urban Energy and Water balance Scheme (SUEWS) is a state-of-the-art, widely used, urban land surface model (ULSM) which simulates urban-atmospheric interactions by quantifying the energy, water and mass fluxes. Using SUEWS as the computation kernel, SuPy (SUEWS in Python), stands on the Python-based data stack to streamline the pre-processing, computation and post-processing that are involved in the common modelling-centred urban climate studies. This paper documents the development of SuPy, which includes the SUEWS interface modification, F2PY (Fortran to Python) configuration and Python frontend implementation. In addition, the deployment of SuPy via PyPI (Python Package Index) is introduced along with the automated workflow for cross-platform compilation. This makes SuPy available for all mainstream operating systems (Windows, Linux, and macOS). Furthermore, three online tutorials in Jupyter notebooks are provided to users of different levels to become familiar with SuPy urban climate modelling. The SuPy package represents a significant enhancement that supports existing and new model applications, reproducibility, and enhanced functionality. [ABSTRACT FROM AUTHOR]

The adverse impacts of urban heat and global climate change are leading policymakers to consider green and blue infrastructure (GBI) for heat mitigation benefits. Though many models exist to evaluate the cooling impacts of GBI, their complexity and computational demand leaves most of them largely inaccessible to those without specialist expertise and computing facilities. Here a new model called The Air-temperature Response to Green/blue-infrastructure Evaluation Tool (TARGET) is presented. TARGET is designed to be efficient and easy to use, with fewer user-defined parameters and less model input data required than other urban climate models. TARGET can be used to model average street-level air temperature at canyon-to-block scales (e.g. 100 m resolution), meaning it can be used to assess temperature impacts of suburb-to-city-scale GBI proposals. The model aims to balance realistic representation of physical processes and computation efficiency. An evaluation against two different datasets shows that TARGET can reproduce the magnitude and patterns of both air temperature and surface temperature within suburban environments. To demonstrate the utility of the model for planners and policymakers, the results from two precinct-scale heat mitigation scenarios are presented. TARGET is available to the public, and ongoing development, including a graphical user interface, is planned for future work. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to understand the relation between micro-climate and urban planning in the case of a cold-climate city, Erzurum. The effects of different urban patterns on micro-climate are analyzed in the context of this study. As a methodology, ENVI-met is used for processing micro-climate simulation of selected urban areas by using measured and obtained climate data such as air temperature, relative humidity, average reflected temperature, surface temperatures, sky view factor, wind velocity and direction. In order to check the accuracy of the simulation for the case study area, obtained data (from meteorology station) is simulated with ENVI-met and results were compared with measured data in the area. Also, land uses and field searches based on the observation of existing situation of urban environment were included into analysis. The findings show that irregular building plot sizes and building heights are mostly existing in historical areas and those urban forms increase thermal comfort under cold climate conditions. The results of simulations provided that same heights of the buildings, regular separation of buildings and regular plot sizes have led to severe urban micro-climates. In contrast, it is observed that variety of those urban physical environment features supported comfortable micro-climate conditions. Urban geometry and climate variables are two of the most important factors shaping outdoor spaces thermal comfort feeling. [ABSTRACT FROM AUTHOR]

Urban areas are an important part of the climate system and many aspects of urban climate have direct effects on human health and living conditions. This implies that reliable tools for local urban climate studies supporting sustainable urban planning are needed. However, a realistic implementation of urban canopy processes still poses a serious challenge for weather and climate modelling for the current generation of numerical models. To address this demand, a new urban surface model (USM), describing the surface energy processes for urban environments, was developed and integrated as a module into the PALM large-eddy simulation model. The development of the presented first version of the USM originated from modelling the urban heat island during summer heat wave episodes and thus implements primarily processes important in such conditions. The USM contains a multi-reflection radiation model for shortwave and longwave radiation with an integrated model of absorption of radiation by resolved plant canopy (i.e. trees, shrubs). Furthermore, it consists of an energy balance solver for horizontal and vertical impervious surfaces, and thermal diffusion in ground, wall, and roof materials, and it includes a simple model for the consideration of anthropogenic heat sources. The USM was parallelized using the standard Message Passing Interface and performance testing demonstrates that the computational costs of the USM are reasonable on typical clusters for the tested configurations. The module was fully integrated into PALM and is available via its online repository under the GNU General Public License (GPL). The USM was tested on a summer heat-wave episode for a selected Prague crossroads. The general representation of the urban boundary layer and patterns of surface temperatures of various surface types (walls, pavement) are in good agreement with in situ observations made in Prague. Additional simulations were performed in order to assess the sensitivity of the results to uncertainties in the material parameters, the domain size, and the general effect of the USM itself. The first version of the USM is limited to the processes most relevant to the study of summer heat waves and serves as a basis for ongoing development which will address additional processes of the urban environment and lead to improvements to extend the utilization of the USM to other environments and conditions. [ABSTRACT FROM AUTHOR]

Knowledge of surface albedo at individual roof scale is important for mitigating urban heat islands and understanding urban climate change. This study presents a method for quantifying surface albedo of individual roofs in a complex urban area using the integration of Landsat 8 and airborne LiDAR data. First, individual roofs were extracted from airborne LiDAR data and orthophotos using optimized segmentation and supervised object based image analysis (OBIA). Support vector machine (SVM) was used as a classifier in OBIA process for extracting individual roofs. The user-defined parameters required in SVM classifier were selected using v-fold cross validation method. After that, surface albedo was calculated for each individual roof from Landsat images. Finally, thematic maps of mean surface albedo of individual roofs were generated in GIS and the results were discussed. Results showed that the study area is covered by 35% of buildings varying in roofing material types and conditions. The calculated surface albedo of buildings ranged from 0.16 to 0.65 in the study area. More importantly, the results indicated that the types and conditions of roofing materials significantly effect on the mean value of surface albedo. Mean albedo of new concrete, old concrete, new steel, and old steel were found to be equal to 0.38, 0.26, 0.51, and 0.44 respectively. Replacing old roofing materials with new ones should highly prioritized. [ABSTRACT FROM AUTHOR]

3D geo-information is essential for urban climate studies. It is obvious that both natural environment and built-up environment play the fundamental role in defining the climatic conditions for urban areas, which affect the quality of human life and human comfort. The paper presents the main categories of 3D geo-information used in urban climate studies and roles in creating and operating the numerical models specially designed to simulate urban planning scenarios and improvement of the urban climate situation. [ABSTRACT FROM AUTHOR]

This paper presents the Semi-empirical URban canopY parametrization (SURY) v1.0, which bridges the gap between bulk urban land-surface schemes and explicitcanyon schemes. Based on detailed observational studies, modelling experiments and available parameter inventories, it offers a robust translation of urban canopy parameters - containing the three-dimensional information - into bulk parameters. As a result, it brings canopy-dependent urban physics to existing bulk urban land-surface schemes of atmospheric models. At the same time, SURY preserves a low computational cost of bulk schemes for efficient numerical weather prediction and climate modelling at the convectionpermitting scales. It offers versatility and consistency for employing both urban canopy parameters from bottom-up inventories and bulk parameters from top-down estimates. SURY is tested for Belgium at 2.8 km resolution with the COSMO-CLM model (v5.0_clm6) that is extended with the bulk urban land-surface scheme TERRA_URB (v2.0). The model reproduces very well the urban heat islands observed from in situ urban-climate observations, satellite imagery and tower observations, which is in contrast to the original COSMO-CLM model without an urban land-surface scheme. As an application of SURY, the sensitivity of atmospheric modelling with the COSMO-CLM model is addressed for the urban canopy parameter ranges from the local climate zones of http://WUDAPT.org. City-scale effects are found in modelling the land-surface temperatures, air temperatures and associated urban heat islands. Recommendations are formulated for more precise urban atmospheric modelling at the convection-permitting scales. It is concluded that urban canopy parametrizations including SURY, combined with the deployment of the WUDAPT urban database platform and advancements in atmospheric modelling systems, are essential. [ABSTRACT FROM AUTHOR]

In the 21st century, urban areas undergo specific climatic conditions like urban heat islands which frequency and intensity increase over the years. Towards the understanding and the monitoring of these conditions, vegetation effects on urban climate are studied. It appears that a natural phenomenon, the evapotranspiration of trees, generates a cooling effect in urban environment. In this work, a 3D microclimate model is used to quantify the evapotranspiration of trees in relation with their architecture, their physiology and the climate. These three characteristics are determined with field measurements and data processing. Based on point clouds acquired with terrestrial laser scanner (TLS), the 3D reconstruction of the tree wood architecture is performed. Then the 3D reconstruction of leaves is carried out from the 3D skeleton of vegetative shoots and allometric statistics. With the aim of extending the simulation on several trees simultaneously, it is necessary to apply the 3D reconstruction process on each tree individually. However, as well for the acquisition as for the processing, the 3D reconstruction approach is time consuming. Mobile laser scanners could provide point clouds in a faster way than static TLS, but this implies a lower point density. Also the processing time could be shortened, but under the assumption that a coarser 3D model is sufficient for the simulation. In this context, the criterion of level of details and accuracy of the tree 3D reconstructed model must be studied. In this paper first tests to assess their impact on the determination of the evapotranspiration are presented. [ABSTRACT FROM AUTHOR]