Urban Climate Research Research Articles

A citizen centred urban network for weather and air quality in Australian schools

High-quality, standardized urban canopy layer observations are a worldwide necessity for urban climate and air quality research and monitoring. The Schools Weather and Air Quality (SWAQ) network was developed and distributed across the Greater Sydney region with a view to establish a citizen-centred network for investigation of the intra-urban heterogeneity and inter-parameter dependency of all major urban climate and air quality metrics. The network comprises a matrix of eleven automatic weather stations, nested with a web of six automatic air quality stations, stretched across 2779 km2, with average spacing of 10.2 km. Six meteorological parameters and six air pollutants are recorded. The network has a focus on Sydney’s western suburbs of rapid urbanization, but also extends to many eastern coastal sites where there are gaps in existing regulatory networks. Observations and metadata are available from September 2019 and undergo routine quality control, quality assurance and publication. Metadata, original datasets and quality-controlled datasets are open-source and available for extended academic and non-academic use.

A global seamless 1 km resolution daily land surface temperature dataset (2003–2020)

Abstract. Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution with global coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination, indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporal gap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020 based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the data pre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In the spatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in each pixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for each day. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values in the original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by large areas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation with different missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error (RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime and mid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling, and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhang et al., 2021).

Coupling Dual Graph Convolution Network and Residual Network for Local Climate Zone Mapping

Local climate zone (LCZ) has become a new standard classification scheme in urban landscapes and showed great potential in urban climate research. Traditional classifiers and ordinary neural networks only consider the spectral or local spatial features of the pixel, ignoring the effect of nonlocal information on the LCZ classification. The graph convolutional network (GCN) has been used to exploit the relationship between adjacent and global land covers owing to the ability to conduct flexible convolution over graphs. In this work, we integrated a convolutional neural network and two GCNs into an end-to-end hybrid framework and generated LCZs directly from the original images. Local-, regional-, and global-level features were extracted and grouped complementarily to foster better performance. Experiments were conducted in six cities around the world to verify the effectiveness of our method. Results showed that the average classification accuracy of the six cities reached 0.956 and performed better than any other comparable model. Ablation experiments also demonstrated the mutual promotion of the different modules. Finally, the small sample experiment provided a practical reference for the LCZ classification in the absence of samples in future.

Crowdsourcing Urban Air Temperature Data for Estimating Urban Heat Island and Building Heating/Cooling Load in London

Urban heat island (UHI) effects significantly impact building energy. Traditional UHI investigation methods are often incapable of providing the high spatial density of observations required to distinguish small-scale temperature differences in the UHI. Crowdsourcing offers a solution. Building cooling/heating load in 2018 has been estimated in London, UK, using crowdsourced data from over 1300 Netatmo personal weather stations. The local climate zone (LCZ) scheme was used to classify the different urban environments of London (UK). Inter-LCZ temperature differences are found to be generally consistent with LCZ temperature definitions. Analysis of cooling degree hours in July shows LCZ 2 (the densest urban LCZ in London) had the highest cooling demand, with a total of 1550 cooling degree hours. The suburban related LCZs 5 and 6 and rural LCZs B and D all had about 80% of the demand of LCZ 2. In December, the rural LCZs A, B and D had the greatest heating demand, with all recording around 5750 heating degree hours. Urban LCZs 2, 5 and 6 had 91%, 86% and 95% of the heating demand of LCZ D, respectively. This study has highlighted both advantages and issues with using crowdsourced data for urban climate and building energy research.

Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

In the last decades, urban climate researchers have highlighted the need for a reliable provision of meteorological data in the local urban context. Several efforts have been made in this direction using Artificial Neural Networks (ANN), demonstrating that they are an accurate alternative to numerical approaches when modelling large time series. However, existing approaches are varied, and it is unclear how much data are needed to train them. This study explores whether the need for training data can be reduced without overly compromising model accuracy, and if model reliability can be increased by selecting the UHI intensity as the main model output instead of air temperature. These two approaches were compared using a common ANN configuration and under different data availability scenarios. Results show that reducing the training dataset from 12 to 9 or even 6 months would still produce reliable results, particularly if the UHI intensity is used. The latter proved to be more effective than the temperature approach under most training scenarios, with an average RMSE improvement of 16.4% when using only 3 months of data. These findings have important implications for urban climate research as they can potentially reduce the duration and cost of field measurement campaigns.

Preliminary study on the temperature relationship at remotely-sensed tree canopy and below-canopy air and ground surface

Thermal infrared remote sensing is a popular tool to measure the impacts of land cover on land surface temperature (LST) in cities. Tree-covered areas usually exhibit a lower LST than other urban surfaces such as concrete, suggesting that urban trees can improve human thermal comfort. However, the remotely-sensed LST of trees is only representative of their canopy temperature. The linkage between the LST of trees and the below-canopy thermal conditions has not been investigated scientifically. This study aimed to measure the differences and determine the correlations between tree canopy temperature (Tc), below-canopy ground surface temperature (Ts) and below-canopy air temperature (Ta) from 42 trees of 16 species in a golf course in Hong Kong. Under the meteorological conditions of the study period (air temperature: ~26 °C; mean radiant temperature: ~30 °C), t-tests showed that mean ± SD Tc (24.1 ± 2.9 °C) did not significantly differ from Ts (24.5 ± 1.8 °C, p = 0.158), but it was significantly lower than Ta (25.4 ± 2.3 °C, p < 0.001). Tc was highly and significantly correlated with Ts (r = 0.81, p < 0.001) and Ta (r = 0.92, p < 0.001). Further analyses showed that the differences between Tc and Ts (ΔTcs) and between Tc and Ta (ΔTca) were significantly dependent on background air temperature (ΔTcs only), relative humidity, mean radiant temperature and incoming shortwave radiation. This study serves as a preliminary investigation on an important knowledge gap in urban climate research. The findings are particularly relevant to researchers who use remotely-sensed LST data to study human thermal comfort.

Dynamic synchronization of extreme heat in complex climate networks in the contiguous United States

Urban climate research has surged in the past decades due to its unique position in addressing the concomitant issues of the rapid global urbanization and emergent climate changes. Nevertheless, there is still a lack of holistic and system-based toolkit for investigating complex dynamics of urban climate systems, especially those of hydroclimate extremes. Here we propose a novel framework based on complex networks for analyzing the topological structure of the urban climate in the contiguous United States (CONUS). In addition, we simulate CONUS urban networks using Kuramoto model and the master stability function formalism, which sheds new light on the occurrence and evolution of heat waves as dynamic synchronization processes. It is found that the CONUS urban climate networks are highly modular with spatial hierarchical (hub-periphery) organization. The small-world effect is also manifest in these networks with the characteristic path length between any pair of nodes no more than 5. For globally connected urban networks, as the sparsity of connections increases (with threshold of correlation coefficient), the path length decreases roughly linearly, whereas the synchronizability reduces exponentially.

Improvement of community monitoring network data for urban heat island investigation in Hong Kong

The Community Weather Information Network (Co-WIN) was established by the Hong Kong Observatory (HKO) and Hong Kong Polytechnic University (PolyU) in 2007 for promoting weather and climate education. Under Co-WIN, participating members (mainly primary and secondary schools) operate automatic weather stations (AWS) onsite to collect weather data and transmit them to a central database for assimilation and archiving. These Co-WIN AWS are typically located on the rooftops of schools in densely populated areas surrounded by buildings, representing the living environment of the nearby inhabitants. With more than 100 AWSs geographically covering every district of Hong Kong (50 stations operated for 5 years or longer), Co-WIN offers a large set of weather data which are potentially useful in urban climate research. In this study, a Quality Control (QC) procedure tailored made for community network was developed to eliminate the potential errors associated with the power outage, loss in connection and instrumental malfunctions. In the process, the hourly air temperature data extracted from the Co-WIN database was screened with reference to the historical monthly extreme values and the hourly temperature data from nearby HKO stations. Regression analyses were then performed to assess the effects of different elements of urban development (e.g. building density, street configuration, amount of greening and water bodies) on the local temperature. At last, the data was applied to an urban climate study to investigate the relationship between the urban heat island (UHI) effect and the characteristics of the urban environment in Hong Kong. The results demonstrate the community network data can be a useful resource for supplementing the official HKO data for urban climatological study.

Improved methods for estimating mean radiant temperature in hot and sunny outdoor settings.

Thermal comfort research has utilized various sensors and models to estimate the mean radiant temperature (MRT) experienced by a human, including the standard black globe thermometer (SGT), acrylic globe thermometers (AGT), and cylindrical radiation thermometers (CRT). Rather than directly measuring radiation, a temperature is measured in the center of these low-cost sensors that can be related to MRT after theoretically accounting for convection. However, these sensors have not been systematically tested under long-term hot and clear conditions. Further,under variable weather conditions, many issues can arise due to slow response times, shape, inaccuracies in material properties and assumptions, and color (albedo, emissivity) inconsistencies. Here, we assess the performance of MRT produced by various heat transfer models, with and without new averagesurface temperature ([Formula: see text]) correction factors, using five instruments-the SGT (15 cm, black), tan and black CRTs, gray and black 38 mm AGTs-compared to 3D integral radiation measurements. Measurements were taken on an unobscured roof throughout summer-to-early-fall months in Tempe, Arizona, examining 58 full-sun days. Deviations without correcting for asymmetrical surface heating-found to be the main cause of errors-reached ± 15-20 °C MRT. By accounting for asymmetric heating through [Formula: see text] calculations, new corrective algorithms were derived for the low-cost sensor models. Results show significant improvements in the estimated MRT error for each sensor (i.e., ∆MRTmodel - IRM) when applying the [Formula: see text] corrections. The tan MRTCRT improved from 1.9 ± 6.2 to -0.1 ± 4.4 °C, while the gray AGT and SGT showed improvements from -1.6 ± 7.2 to -0.4 ± 6.3 °C and - 6.6 ± 6.4 to - 0.03 ± 5.7 °C, respectively. The new corrections also eliminated dependence on other meteorological factors (zenith, wind speed). From these results, we provide three simple equations for CRT, AGT, and SGT correction for future research use under warm-hot and clear conditions. This study is the most comprehensive empirical assessment of various low-cost instruments with broad applicability in urban climate and biometeorological research.

Mapping Local Climate Zones and Their Applications in European Urban Environments: A Systematic Literature Review and Future Development Trends

In the light of climate change and burgeoning urbanization, heat loads in urban areas have emerged as serious issues, affecting the well-being of the population and the environment. In response to a pressing need for more standardised and communicable research into urban climate, the concept of local climate zones (LCZs) has been created. This concept aims to define the morphological types of (urban) surface with respect to the formation of local climatic conditions, largely thermal. This systematic review paper analyses studies that have applied the concept of LCZs to European urban areas. The methodology utilized pre-determined keywords and five steps of literature selection. A total of 91 studies were found eligible for analysis. The results show that the concept of LCZs has been increasingly employed and become well established in European urban climate research. Dozens of measurements, satellite observations, and modelling outcomes have demonstrated the characteristic thermal responses of LCZs in European cities. However, a substantial number of the studies have concentrated on the methodological development of the classification process, generating a degree of inconsistency in the delineation of LCZs. Recent trends indicate an increasing prevalence of the accessible remote-sensing based approach over accurate GIS-based methods in the delineation of LCZs. In this context, applications of the concept in fine-scale modelling appear limited. Nevertheless, the concept of the LCZ has proven appropriate and valuable to the provision of metadata for urban stations, (surface) urban heat island analysis, and the assessment of outdoor thermal comfort and heat risk. Any further development of LCZ mapping appears to require a standardised objective approach that may be globally applicable.

Neighbourhood flOURISHing (NOURISH): A new short and inclusive interpersonal measure of subjective wellbeing

BackgroundShort individual Subjective Wellbeing (SWB) survey instruments are increasingly used to measure the impact of policy and community health initiatives. Neighbourhood-level interventions should affect social SWB, where intervention impact relates to a geographically bounded area. Existing social SWB measures lack spatial focus and have not been tested with low literacy users. The objective of the study was to develop and validate a short and inclusive neighbourhood flourishing tool. MethodsFive key stages, started with conceptualisation built on recent reviews and a community survey. Second, a set of survey items were operationalised. Third, cognitive interviews with six low education readers refined and reduced the pool. Fourth, these items plus new and established measures of SWB and predictors of both, were tested with 292 participants and subject to psychometric analyses. Fifth, the nine strongest items were validated in two deprived UK neighbourhoods with a further 221 participants and subjected to readability formulas. ResultsInitial work revealed 12 relevant concepts and 101 survey items assessing these were developed. Approximately half the new short items were acceptable in ‘think aloud’ interviews (n = 6). Two rounds of completion of items revealed a 2-factor solution consisting of neighbourhood feelings and functionings. The final solution consisted of three high loading items assessing feelings (safety, trust and respect) and three items assessing functionings (celebration, autonomy and participation), that did not cross-load, and demonstrated predictive and discriminant validity. Based on eight reading score formulas, the final measure was found to suit a reading age of 12–14 years old. ConclusionThe new valid six-item measure was tested with 519 participants, is acceptable and usable with people from deprived neighbourhoods, and with evidence of validity. This is a robust practical and inclusive tool for the measurement of Neighbourhood Flourishing, relevant to urban regeneration, inequalities and climate change policy and research.

Perceptions of urban heat island mitigation and implementation strategies: survey and gap analysis

Sustainable development of cities and communities under climate change calls for effective and pragmatic strategies to mitigate urban heat island (UHI) during hot seasons. Meanwhile, the effectiveness of UHI mitigation measures can be affected by public perceptions during planning and implementation processes. However, the general perceptions of UHI mitigation and implementation strategies have not received adequate attention in urban climate research. This study leverages the results of a carefully designed survey to fill this research gap. The perceptions of professional respondents are largely affected by the geographic areas they work in and partially affected by how familiar respondents are with the UHI-related building codes and regulations. In addition, academic literature and government reports are the two major sources for most respondents to obtain UHI mitigation information. We also identify four knowledge and implementation gaps: the lack of public education on UHI mitigation and implementation measures, the lack of effective communications between researchers and code writers, the lack of implementing UHI mitigation strategies in some countries, and the lack of trustworthy information shared on social media. Bridging these gaps are of key importance to fostering public engagement and improving the effectiveness of UHI mitigation measures.

Citizen weather stations data for monitoring applications and urban climate research: an example of Moscow megacity

This study considers experience in use of crowdsourced meteorological observations from the world’s biggest network of citizen weather stations (CWSs), Netatmo, for urban climate research and applied monitoring services on the example of Moscow megacity. Crowdsourcing paradigm is an emerging alternative to the development of expensive urban meteorological networks. We have experimentally evaluated the uncertainties of the Netatmo temperature observations and regard them as being acceptable when the stations are shadowed from the sun. In order to filter out the misrepresentative observations, a quality-control algorithm has been developed. Within more than 1500 CWSs in the Moscow region, only about 25% meet this quality control, which is still one order of magnitude higher than the number of official Roshydromet weather stations in the study area. Such amount of data opens new opportunities for spatially-resolving urban climate studies and for applied services. As an example of the latter, we present a prototype of a web-mapping application for a near-real-time temperature monitoring system in Moscow. The application’s backend includes automatic services for downloading of observations from Netatmo and official Roshydromet networks, as well as for database maintaining. The processed data are visualized interactively in a web browser. The application is available on the Internet at http://carto.geogr.msu.ru/mosclim/. It will be further developed to include a real-time thermal comfort assessment based on the contemporary PET and UTCI biometeorological indices, a visualization of the interpolated fields, and other improvements.

Applications of Local Climate Zone Classification Scheme to Improve Urban Sustainability: A Bibliometric Review

Many of the sustainable urban development issues, such as human heath, energy consumption, carbon emission, are related to the climate of cities. As a result, research insights gained in urban climate study can be applied to improve urban sustainability. Although the Local Climate Zones (LCZ) scheme was originally proposed to provide a standardized classification of landscapes to study urban air temperature, its use was not limited to the study of urban heat islands. This study explores the applications of LCZ scheme in various research domains by conducting a bibliometric analysis in CiteSpace on over 800 articles that cites the original article of LCZ. These articles cover a wide range of research categories including meteorology, atmospheric science, environmental science, remote sensing, building technology, civil engineering, ecology, urban studies, etc. The LCZ scheme facilitates urban climate data collection by refining monitoring network, providing reasonable modelling input, and improving database documentation. In addition to the study of urban heat islands, the LCZ scheme was applied in studies of urban thermal comfort, human health, building energy consumption, and carbon emission. The diffusion of the LCZ scheme to other research domains offers an example that the development of urban climate research advances sustainable urban development. This review provides insights of multidisciplinary studies related to urban climate for policy-makers, urban specialists, architects, ecologists, and others.

Urban climate research and planning applications in China: a scientometric and long-term review (1963-2018) based on CiteSpace

Urban climate research is essential for sustainable development. Few review papers have focused on long-term research and planning applications in China. We conducted a bibliometric review by using CiteSpace to elaborate changes in hot spots, frontiers, development processes, and applications of urban climate research from 1963 to 2018. The identified research could be categorized into 4 major research hot spots: (1) the urban climate effect brought on by urbanization; (2) analysis of meteorological factors and conditions; (3) numerical simulation of urban climate; and (4) urban planning and design in the context of climate change. Moreover, 15 of the most frequently cited key words indicating research frontiers with specific time patterns were detected, and 4 key aspects were identified in terms of research topic: urban climate effects and mechanisms, progress and reform of meteorological service systems, multi-scale numerical modeling, and mitigation and adaptation strategies for addressing climate change. Climate applications in China have undergone changes from the early wind rose map, to the use of multiple meteorological elements as a guide to city planning, and recently, to the adoption of emerging applications. Challenges and prospects, including the probable effects of the government and policies, specific long-standing research gaps, the paradigm shift in urban planning, and the requirement for readily available urban data were also highlighted. This study provides a holistic perspective on the development process of long-term urban climate research in China. The findings may be pivotal in understanding the inner logic behind policies and their implementations and are expected to pave the way for exploring new paths in urban sustainable development and planning transformation.

Computation of City-descriptive Parameters for High-resolution Numerical Weather Prediction in Moscow Megacity in the Framework of the COSMO Model

A new methodology is proposed for computing the city-descriptive parameters for the TERRA_URB scheme in the COSMO model. It is based on the GIS analysis of OpenStreetMap data, Sentinel-2 satellite images, and global land cover data. A set of parameters is obtained for Moscow megacity on a grid with the spacing of 500 m. The dataset is promising for high-resolution numerical weather prediction within the COSMO-Ru system and for urban climate research.

Thermal data analysis for urban climate research: A case study of Olomouc, Czechia

Due to the recent advancements in thermal camera technology, it is easier than ever to acquire information about surface temperature in very-high or extreme spatial resolution from manned or unmanned airborne vehicles. The main objective of the paper is to investigate the possibilities of extreme resolution thermal data for urban climate research. The main dataset used for the work is thermal mosaic acquired by an aeroplane above the city of Olomouc in the morning and afternoon on 10th July 2016. The spatial resolution of the dataset fluctuates between 90 and 100 cm per pixel edge. Auxiliary data used for the analysis included open data from European programmes Urban Atlas and CORINE Land Cover. For further analysis, building height information, material type and Local Climate Zones classification were used. The results show a possible comparison in various neighbourhoods and material types while maintaining reference units – Urban Atlas/CORINE Land Cover classes or Local Climate Zones. Verticality analysis shows a significant difference between ground and rooftop levels that need to be taken into account when investigating heat for quality of life. All the analyses confirmed knowledge about the difference between natural and artificial surfaces that most artificial surfaces tend to heat up much more during daytime and fail to cool down completely during the night-time. Moreover, this research lays the foundation to future urban climate research based on extreme resolution thermal images over multiple areas or during long-lasting campaigns.

Progressing urban climate research using a high-density monitoring network system.

An increasing number of urban meteorological networks (UMNs) and automated data acquisition are irreplaceable tools in modern urban climate monitoring, evaluation, and analysis. The most serious issue in such systems is data loss, caused primarily by communication problems between stations and servers. The Novi Sad Urban Network (NSUNET) consists of 28 remote stations and 2 servers built solely on open-source technologies. It is used for monitoring climate peculiarities and acquiring long-term meteorological data from the urban area of Novi Sad, as well as for the early warning notification to the city emergency services of the current urban weather conditions. Since its deployment, the system has managed to overcome most of the problems related to today's UMNs, to operate at a low Internet service fee, and ensure high reliability and performance on low-budget hardware. This study includes details on how to develop such a system and it presents a statistical analysis of the NSUNET system's performances and the measurement data. Furthermore, this kind of monitoring system provides good results in the analysis of air/surface temperature and outdoor human thermal comfort in the local climate zones (LCZs) of urban and surrounding areas and can help identify hot spots/districts in the urban area.

Airborne thermal remote sensing: the case of the city of Olomouc, Czech Republic

ABSTRACT This paper describes the process of very high-resolution thermal mosaic acquisition using low-altitude airborne remote sensing and the basic analysis of data regarding urban climate research. The process of data acquisition from flight planning to final mosaicking is described. A broadband thermal camera was mounted to a Cessna aeroplane for two flights over the city; one performed in the morning and one in the afternoon. The ground resolution of the final mosaic fluctuates between 90 and 105 cm. Gathered data had to be processed to acquire kinetic temperature values. The processing consisted of radiometric, geometric, atmospheric and emissivity corrections. As a result, two mosaics covering the city were created. The difference between the building canopy layer and ground level was investigated, and a 5°C increase was found during the day on the rooftop level. It was confirmed that natural materials do not heat as much as artificial ones. Local Climate Zones were used in the analysis as the spatial unit for comparison of the thermal regime at the neighbourhood level. To summarise, the possibilities of extreme resolution thermal remote sensing data acquisition and analysis are demonstrated.

Historia i współczesność badań nad klimatem Wrocławia – pomiary i badania modelowe

In this paper, we present the history and development of urban climate research in Wroclaw, undertaken at the University of Wroclaw (UWr), mainly at the Department of Climatology and Atmosphere Protection (DCAP). The main scientific topics, such as mesoclimate, bioclimate, urban heat island, and air quality in Wroclaw are described. There were two milestones in urban climate research at UWr. First, the turn of the 21st century was associated with the introduction of automatic weather stations, mobile measurements and acoustic soundings, supported with GIS analysis, for the spatio-temporal studies of Wroclaw (bio)climate. In the second decade of the 21st century, our research is mostly related to urban air quality. New measuring techniques have been introduced, supported with numerical models (WRF, WRF-Chem), and much greater attention has been devoted to scientific research applications (the LIFE-APIS/PL project).