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Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS - "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC dataset Version 1.1 at 0.1° resolution for Europe with annual LULC maps from 1950–2100 (Hoffmann et al., 2022b, a), which is tailored towards the use in state-of-the-art RCMs. The plant functional type distribution (PFT) for the year 2015 (i.e., LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method, cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25° resolution as input for CMIP6 experiments, to derive LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.

Estimating future temporal patterns of Surface Urban Heat Islands (SUHIs) on multiple time scales is an ongoing research endeavor. Among these time scales, estimation of next-day SUHIs is of special significance to urban residents, yet we currently lack a simple but efficient approach for making such estimations. In the present study, we propose a statistical strategy for estimating next-day nighttime SUHIs, based on incorporating various SUHI controls into a support vector machine regression (SVR) model. The majority of both the surface controls (including factors related to land cover and solar radiation) and meteorological controls (including temperature fluctuations, relative humidity, accumulated precipitation, wind speed, aerosol optical depth, and soil moisture) that have previously been found to account for daily SUHI variations were used as estimators, and we provide estimations for both the overall SUHI intensity (SUHII) and pixel-by-pixel Gaussian-based LSTs over 59 Chinese megacities. For the overall SUHII, the mean absolute error (MAE) is 0.67 K on average, and the mean absolute percentage error (MAPE) is no more than 25% for more than 90% of the cities. For the pixel-by-pixel LSTs, the associated MAE is less than 2.0 K in most scenarios. In addition, the contribution from each selected estimator to SUHII estimation is assessed comprehensively. Among all the estimators, the contribution from relative humidity is the greatest, followed by rural surface temperature and surface air temperature. Moreover, for nearly 78% of the cities, the estimators related to day-to-day SUHI variations make a larger contribution than those related to intra-annual SUHI variations. We conclude that our simple yet effective statistical approach for estimating next-day SUHIs can potentially help urban residents to better adapt to urban heat stress.

There is a scientific consensus on the need for spatially detailed information on urban landscapes at a global scale. These data can support a range of environmental services, since cities are places of intense resource consumption and waste generation and of concentrated infrastructure and human settlement exposed to multiple hazards of natural and anthropogenic origin. In the face of climate change, urban data are also required to explore future urbanization pathways and urban design strategies in order to lock in long-term resilience and sustainability, protecting cities from future decisions that could undermine their adaptability and mitigation role. To serve this purpose, we present a 100 m-resolution global map of local climate zones (LCZs), a universal urban typology that can distinguish urban areas on a holistic basis, accounting for the typical combination of micro-scale land covers and associated physical properties. The global LCZ map, composed of 10 built and 7 natural land cover types, is generated by feeding an unprecedented number of labelled training areas and earth observation images into lightweight random forest models. Its quality is assessed using a bootstrap cross-validation alongside a thematic benchmark for 150 selected functional urban areas using independent global and open-source data on surface cover, surface imperviousness, building height, and anthropogenic heat. As each LCZ type is associated with generic numerical descriptions of key urban canopy parameters that regulate atmospheric responses to urbanization, the availability of this globally consistent and climate-relevant urban description is an important prerequisite for supporting model development and creating evidence-based climate-sensitive urban planning policies. This dataset can be downloaded from https://doi.org/10.5281/zenodo.6364594 (Demuzere et al., 2022a).

There is a scientific consensus on the need for spatially detailed information on urban landscapes at a global scale to support a range of environmental services, as cities are acknowledged as places of: intense resource consumption and waste generation and foci of population and infrastructure that are exposed to multiple hazards of natural and anthropogenic origin. In the face of climate change, urban data is also required for future urbanisation pathways and urban design strategies, in order to “lock in” long-term resilience and sustainability, protecting cities from future decisions that could undermine their adaptability. Eventually, these global form-based, contextually specific urban planning and urban design strategies are the ultimate guarantors of successful life cycle costs, payback, and liveability. Moreover, these strategies are needed to identify the relevant data for planning and climate on neighbourhood, city and global scales, and to become part of a basic infrastructure to support a host of studies on exposure to environmental hazards, energy demand, climate adaptation and mitigation solutions and human health, as examples. Therefore, a more holistic urban landscape description is required, that goes beyond the urban mask and that enables the assessment of the spatial impact of urban planning decisions that will alter urban canopy parameters (UCPs) and their climate outcome. The global Local Climate Zone (LCZ) map presented here serves this purpose, as the LCZ typology is the only universal classification that can distinguish urban surfaces on a holistic basis, accounting for the typical combination of micro-scale land-covers and associated physical properties, all being the consequence of historic urbanisation patterns that reflect local terrain, culture, economy, etc. The 100m resolution global LCZ map is generated by feeding an unprecedented amount of labelled training areas (partly sourced from the LCZ Generator - https://lcz-generator.rub.de/) and earth observation imagery into lightweight random forest models. Its quality is assessed using the default bootstrap-based cross validation alongside a thematic benchmark for 150 selected functional urban areas using independent global and open- source data on surface cover, surface imperviousness, anthropogenic heat and building height.Complementing the single cities‘ LCZ maps accessible via the LCZ Generator, the global LCZ map for the first time reveals the world‘s intra-urban heterogeneity heterogeinity. In addition, as each LCZ type is associated with generic numerical descriptions of key UCPs, parameters critical to model atmospheric responses to urbanisation, the availability of this globally consistent and climate-relevant urban description is an essential prerequisite for developing fit-for-purpose integrated climate-sensitive urban planning policies.

Reliable and sufficient knowledge on environmental conditions delivered from micrometeorological and microclimatological data plays a central role in assessing and modelling trends and effects of climate change and adverse weather on the environment. Enormous efforts have already been made to centralise data from ground-based and satellite measurements and to make them available for public use. However, beyond specific initiatives, they are still missing one very important component – micrometeorological data, i.e. data addressing meteorological conditions of microenvironment that is open and available for various application potentials and user groups.Micrometeorological data are usually collected as part of scientific projects and observational networks developed for different purposes, but they often “languish” in reports and institutional data storages. To address this shortfall, FAIRNESS Cost Action will establish micrometeorological knowledge share platform (Micromet_KSP) to communicate: a) compiled inventory of available and quality proven micrometeorological in situ data sets on the European level and beyond, b) measurement and data management recommendations designed to meet FAIR principles and avoid temporal and spatial gaps, c) examples of rural and urban FAIR data sets and d) Q&A exchanged between Action members, stakeholders, specialised user groups and general public.FAIRNESS targets are, primarily, networks of Automated Weather Stations installed in urban, sub-urban and rural areas which are in charge of dedicated projects, specialised agencies, regional or national government offices for specific applications in the sectors of urban-, forest-, and environmental meteorology and agrometeorology. Addressing identified challenges requires an effective transboundary network of researchers, stakeholders, and civil society to identify and fill knowledge gaps, standardize, optimize, and promote new environmental-tailored measurement and control procedures, enhance research effectiveness and improve dissemination.FAIRNESS consortium includes 65 partners from 28 countries in Europe, Asia, Australia, and North America, and invites interested stakeholders and/or data contributors to join the project during its realization (2021-2025).

Urban overheating, driven by global climate change and urban development, is a major contemporary challenge which substantially impacts urban livability and sustainability. Overheating represents a...

Anthropogenic land-use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently extensively investigated within the WCRP CORDEX Flagship Pilot Study (FPS) LUCAS – "Land Use and Climate Across Scales" using an ensemble of different Regional Climate Models (RCMs) coupled with diverse Land Surface Models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM-LSM simulations. To account for these needs, we generated the LUCAS LUC Version 1.0 at 0.1° resolution for Europe Hoffmann et al. (2021b,c). The plant functional type distribution for the year 2015 (i.e. LANDMATE PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details about the conversion method based on a cross-walking procedure and the evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (submitted). Subsequently, we applied the land-use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25° resolution as input for CMIP6 experiments, to derive realistic LULC distribution at high spatial resolution and at annual timesteps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a Land Use Translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to remotely-sensed PFT time series. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the Coupled Modelling Intercomparison Project Phase 6 (CMIP6). The resulting LULCC maps can be applied as land use forcing to the next generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions world-wide.

This study uses the statistical and meta-analysis methods to comprehensively review 324 LCZ papers during 2012-2020, 202 of which are categorized as LCZ mapping papers. We present a bibliometric analysis of LCZ mapping papers from literature statistics, research topics, city distribution, institutions and cooperation, and research projects.

The Local Climate Zone (LCZ) scheme is a classification system providing a standardization framework to present the characteristics of urban forms and functions, especially for urban heat island (UHI) research. Landsat-based 100 m resolution LCZ maps have been classified by the World Urban Database and Portal Tool (WUDAPT) method using a random forest (RF) machine learning classifier. Some studies have proposed modified RF and convolutional neural network (CNN) approaches. This study aims to compare CNN with an RF classifier for LCZ mapping in great detail. We designed five schemes (three RF-based schemes (S1–S3) and two CNN-based ones (S4–S5)), which consist of various combinations of input features from bitemporal Landsat 8 data over four global mega cities: Rome, Hong Kong, Madrid, and Chicago. Among the five schemes, the CNN-based one with the incorporation of a larger neighborhood information showed the best classification performance. When compared to the WUDAPT workflow, the overall accuracies for entire land cover classes (OA) and for urban LCZ types (i.e., LCZ1-10; OAurb) increased by about 6–8% and 10–13%, respectively, for the four cities. The transferability of LCZ models for the four cities were evaluated, showing that CNN consistently resulted in higher accuracy (increased by about 7–18% and 18–29% for OA and OAurb, respectively) than RF. This study revealed that the CNN classifier classified particularly well for the specific LCZ classes in which buildings were mixed with trees or buildings or plants were sparsely distributed. The research findings can provide a basis for guidance of future LCZ classification using deep learning.

Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or on generalization ability and a flexible ATC modelling framework for different numbers of thermal observations is lacking. Here, we propose a hybrid ATC model (ATCH) that considers both prediction accuracy and generalization ability; our approach combines multiple harmonics with a linear function of LST-related factors, including surface air temperature (SAT), NDVI, albedo, soil moisture, and relative humidity. Based on the proposed ATCH, various parameter-reduction approaches (PRAs) are designed to provide model derivatives which can be adapted to different scenarios. Using Terra/MODIS daily LST products as evaluation data, the ATCH is compared with the original sinusoidal ATC model (termed the ATCO) and its variants, and with two frequently-used gap-filling methods (Regression Kriging Interpolation (RKI) and the Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST)), under clear-sky conditions. In addition, under overcast conditions, the LSTs generated by ATCH are directly compared with in-situ LST measurements. The comparisons demonstrate that the ATCH increases the prediction accuracy and the overall RMSE is reduced by 1.8 and 0.7 K when compared with the ATCO during daytime and nighttime, respectively. Moreover, the ATCH shows better generalization ability than the RKI and behaves better than the RSDAST when the LST gap size is spatially large and/or temporally long. By employing LST-related controls (e.g., the SAT and relative humidity) under overcast conditions, the ATCH can better predict the LSTs under clouds than approaches that only adopt clear-sky information as model inputs. Further attribution analysis implies that incorporating a sinusoidal function (ASF), the SAT, NDVI, and other LST-related factors, provides respective contributions of around 16%, 40%, 15%, and 30% to the improved accuracy. Our analysis is potentially useful for designing PRAs for various practical needs, by reducing the smallest contribution factor each time. We conclude that the ATCH is valuable for further improving the quality of LST products and can potentially enhance the time series analysis of land surfaces and other applications.

Urban climate risks have a wide range of impacts on the health of more than 50% of the world’s population, which is a critical issue relating to climate change. To support urban climate study and categorise different urban environments and their atmospheric impacts in a consistent way, the Local Climate Zone (LCZ) classification scheme has been developed. The World Urban Database and Access Portal Tools project aims to map the LCZ of cities across the globe. However, previous classification approaches based on satellite images have limitations regarding the characterisation of three-dimensional features such as building heights. This study aims to apply convolutional neural networks to classify LCZ types based on ground-level images, which can provide more detail of the urban environments. Validation results have shown an overall accuracy of 69.6%. The new method outperformed previous satellite-based studies for classifying the LCZ types Compact Mid-rise, Sparsely Built, Heavy Industry, and Bare Rock or Paved.

Using the cloud-computing resources of Google's Earth Engine (EE) and a range of satellite sensors (input features) this paper for the first time explores the potential of up-scaling the current Local Climate Zone mapping efforts to regional and global scales. Using a transferability framework, we test whether information from one city contains valuable information to categorise a different city, simultaneously exploring the role of the input features and the characteristics of individual cities. It was found that the accuracies of the EE approach are comparable to the standard WUDAPT method, making EE a viable alternative approach. The results from the city-to-city experiments are generally poor when compared to the single city benchmark experiments, indicating that the collection of site-specific training areas remains relevant. However, LCZ mapping accuracies are considerably improved when a) the source of the training data is from a city in the same ecoregion as the city of interest and b) if the training areas from several cities are combined. These results support the claim that the LCZ framework is a universal urban typology and indicate that, provided a continued optimisation of input features and quality of training areas, up-scaling to regional or global levels is feasible.

Since their introduction in 2012, Local Climate Zones (LCZs) emerged as a new standard for characterizing urban landscapes, providing a holistic classification approach that takes into account micro-scale land-cover and associated physical properties. In 2015, as part of the community-based World Urban Database and Access Portal Tools (WUDAPT) project, a protocol was developed that enables the mapping of cities into LCZs, using freely available data and software packages, yet performed on local computing facilities. The LCZ Generator described here further simplifies this process, providing an online platform that maps a city of interest into LCZs, solely expecting a valid training area file and some metadata as input. The web application (available athttps://lcz-generator.rub.de) integrates the state-of-the-art of LCZ mapping, and simultaneously provides an automated accuracy assessment, training data derivatives, and a novel approach to identify suspicious training areas. As this contribution explains all front- and back-end procedures, databases, and underlying datasets in detail, it serves as the primary “User Guide” for this web application. We anticipate this development will significantly ease the workflow of researchers and practitioners interested in using the LCZ framework for a variety of urban-induced human and environmental impacts. In addition, this development will ease the accessibility and dissemination of maps and their metadata.

Since their introduction in 2012, Local Climate Zones (LCZs) emerged as a new standard for characterising urban landscapes, providing a holistic classification approach that takes into account micro-scale land-cover and associated physical properties. In 2015, as part of the community-based World Urban Database and Access Portal Tools (WUDAPT) project, a protocol was developed that enables the mapping of cities into LCZs, using freely available data and software packages, yet performed on local computing facilities. The ‘LCZ Generator’ described here further simplifies this process, providing an online platform that maps a city of interest into LCZs, solely expecting a valid training area file and some metadata as input. The tool integrates the state-of-the-art of LCZ mapping, and simultaneously provides an automated accuracy assessment, training data derivatives and a novel approach to identify suspicious training areas. In addition, this development will ease the dissemination of maps and metadata. We anticipate this development will significantly ease the accessibility and workflow of researchers and practitioners interested in using the LCZ framework for a variety of urban-induced human and environmental impacts.

Land-use and land cover (LULC) are continuously changing due to environmental changes and anthropogenic activities. Many observational and modeling studies show that LULC changes are important drivers altering land surface feedbacks and land-atmosphere exchange processes that have substantial impact on climate on the regional and local scale. Yet, most long-term regional climate modeling studies do not account for these changes. Therefore, within the WCRP CORDEX Flagship Pilot Study LUCAS (Land Use Change Across Scales) a new workflow was developed to generate high-resolution annual land cover change time series based on past reconstructions and future projections. First, the high-resolution global land cover dataset ESA-CCI LC (~300 m resolution) is aggregated and converted to a 0.1° resolution, fractional plant functional type (PFT) dataset. Second, the land use change information from the land-use harmonized dataset (LUH2), provided at 0.25° resolution as input for CMIP6 experiments, is translated into PFT changes employing a newly developed land use translator (LUT). The new LUT was first applied to the EURO-CORDEX domain. The resulting LULC maps for past and future - the LUCAS LUC dataset - can be applied as land use forcing to the next generation RCM simulations for downscaling CMIP6 by the EURO-CORDEX community and in the framework of FPS LUCAS. The dataset includes land cover and land management practices changes important for the regional and local scale such as urbanization and irrigation. The LUCAS LUC workflow is applied to further CORDEX domains, such as Australasia and North America. The resulting past and future land cover changes will be presented, and challenges regarding the application of the new workflow to different regions will be addressed. In addition, issues related to the implementation of the dataset into different RCMs will be discussed.

Für wirksame Maßnahmen in der Klimaanpassung ist es vielfach essenziell, dass klimatische Informationen in raum-zeitlich hochaufgelöster Form verfügbar sind. Diese ermöglichen es, Orte für Maßnahmen zu identifizieren sowie ein langfristiges Monitoring vor und nach der Umsetzung von Maßnahmen durchzuführen, um ihre Wirksamkeit feststellen zu können. Zudem erhöht die Einbindung lokaler Stakeholder die Chancen auf Akzeptanz und erfolgreiche Umsetzung. Städte sind, sowohl weltweit gesehen als auch in Deutschland, Orte mit besonders hohem Risiko gegenüber Naturgefahren wie beispielsweise Hitzewellen und deren gesundheitlichen Auswirkungen auf Menschen. Zudem finden wir in Städten eine hohe räumliche Variabilität klimatischer Bedingungen. Ausgerechnet für Städte sind hochauflösende Klimainformationen aber in aller Regel nicht verfügbar. Städte wurden traditionell kaum mit Messstationen ausgestattet und die Errichtung und der dauerhafte Betrieb dichter Messnetze sind mit hohen finanziellen Hürden verbunden. Gerade in Städten könnten Daten privater Wetterstationen, die in den letzten Jahren enormen Zuwachs erhalten haben, einen wichtigen Beitrag leisten, da hier oft mehrere hunderte bis tausende solcher Wetterstationen kontinuierlich Daten erfassen. Diese Daten können über das Internet abgerufen werden und stehen vielfältigen potentiellen Anwendungen zur Verfügung. Für die Verwendung der Daten sind Qualitätsprüfungsverfahren von enormer Wichtigkeit, um typische Fehler zu erkennen und herauszufiltern. Nach Anwendung solcher Verfahren sind weit mehr als die Hälfte der privaten Wetterstationsdaten für stadtklimatologische Analysen, die Planung von Klimaanpassungsmaßnahmen und lokales Monitoring nutzbar. So kann beispielsweise die beobachtete hohe Variabilität in qualitätsgeprüften Lufttemperaturdaten für Konzepte und Fragen der Klimaanpassung genutzt werden. Zusätzlich kann durch gezielte Kooperation mit Stakeholdern vor Ort bei der Aufstellung neuer Stationen ein vertieftes Verständnis für Wetter und Klima aufgebaut werden. In diesem Beitrag sollen Hürden und Möglichkeiten privater Wetterstationen und deren Daten im Rahmen der Klimaanpassung vorgestellt und diskutiert werden. Mithilfe von Beispielen soll zudem exemplarisch deren Anwendbarkeit demonstriert und vertieft werden.

Die Bereitstellung von atmosphärischen Messdaten über lange Zeiträume mit hoher räumlicher Auflö­sung und Abdeckung stellt eine große Herausforderung in der Klimatologie dar. Dies gilt insbesondere für Städte, da sich Klimamessstationen typischerweise im Außenbereich befinden, sodass das hetero­gene Stadtklima nicht erfasst werden kann. In den letzten Jahren haben sich private Heimwetterstationen (CWS), deren Daten über das Internet abgerufen werden können, weltweit stark verbreitet. Diese Stationen befinden sich vor allem in Städten und können so traditionelle Messnetze ergänzen. CWS, vor allem der Firma "Netatmo", ermöglichen eine räumlich und zeitlich hohe Auflö­sung, jedoch führen inkorrekte Metadaten, ungeeignete Messstandorte und nicht erfasste Positions­änderungen zu fehlerhaften Daten. Während deshalb Daten einzelner Stationen für wissen­schaftliche Analysen ungeeignet sind, erlaubt die ist die große Menge an Daten nach umfänglicher Qualitäts­kontrolle die Beantwortung stadtklimatologischer Fragestellungen. In dieser Bachelorarbeit wurde ein System entwickelt, welches kontinuierlich die Netatmo-Daten für 200 Städte der Welt sammelt. Zur Sicherung der Datenqualität wurde eine Methodik entwickelt, die Positionsänderungen z.B. durch Umzug oder Verkauf erkennt und markiert sowie neue Stationen der Datenbank hinzufügt. Messdaten aktiver Stationen werden monatlich in stündlicher Auflösung mit Extremwerten für die Parameter der Lufttemperatur und -feuchte abgerufen. Für den Großraum Rhein-Ruhr wurden exemplarisch die Daten einer Nacht mit austauscharmer Wetterlage qualitätsgeprüft und mit Verfahren unterschiedlicher Komplexität analysiert. Dazu wurde neben einfachen Interpolationsverfahren auch ein Random Forest Modell trainiert, das auf Basis thermaler und multispektraler Fernerkundungsdaten, eines digitalen Höhenmodells und stadtklimato­logisch relevanter Parametern wie dem Sky View Factor, die Lufttemperatur in 100 m Auflösung modelliert. Solche hochauflösenden Karten der Lufttemperatur könnten beispielsweise dazu verwendet werden, geeignete Orte für wirksame Klimaanpassungsmaßnahmen zu identifizieren. Die Datenbank bietet zudem eine Grundlage für weitere Untersuchungen und kann unmittelbar größere Mengen auf crowdsourcing basierender Daten mit gesicherten Metadaten bereitstellen.

This is a comment to the paper "Magnitude of urban heat islands largely explained by climate and population" by Manoli et al. (2019, Nature 573 p. 55-60; https://doi.org/10.1038/s41586-019-1512-9)

As a unique classification scheme for urban forms and functions, the local climate zone (LCZ) system provides essential general information for any studies related to urban environments, especially on a large scale. Remote sensing data-based classification approaches are the key to large-scale mapping and monitoring of LCZs. The potential of deep learning-based approaches is not yet fully explored, even though advanced convolutional neural networks (CNNs) continue to push the frontiers for various computer vision tasks. One reason is that published studies are based on different datasets, usually at a regional scale, which makes it impossible to fairly and consistently compare the potential of different CNNs for real-world scenarios. This article is based on the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using this dataset, we studied a range of CNNs of varying sizes. In addition, we proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this base network, we propose fusing multilevel features using the extended Sen2LCZ-Net-MF. With this proposed simple network architecture, and the highly competitive benchmark dataset, we obtain results that are better than those obtained by the state-of-the-art CNNs, while requiring less computation with fewer layers and parameters. Large-scale LCZ classification examples of completely unseen areas are presented, demonstrating the potential of our proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also intensively investigated the influence of network depth and width, and the effectiveness of the design choices made for Sen2LCZ-Net-MF. This article will provide important baselines for future CNN-based algorithm developments for both LCZ classification and other urban land cover land use classification. Code and pretrained models are available at https://github.com/ChunpingQiu/benchmark-on-So2SatLCZ42-dataset-a-simple-tour.

Land surface temperature (LST) is used as a critical indicator for various environmental issues because it links land surface fluxes with the surface atmosphere. Moderate-resolution imaging spectroradiometers (MODIS) 1 km LSTs have been widely utilized but have the serious limitation of not being provided under cloudy weather conditions. In this study, we propose two schemes to estimate all-weather 1 km Aqua MODIS daytime (1:30 p.m.) and nighttime (1:30 a.m.) LSTs in South Korea for humid summer days. Scheme 1(S1) is a two-step approach that first estimates 10 km LSTs and then conducts the spatial downscaling of LSTs from 10 km to 1 km. Scheme 2(S2), a one-step algorithm, directly estimates the 1 km all-weather LSTs. Eight advanced microwave scanning radiometer 2 (AMSR2) brightness temperatures, three MODIS-based annual cycle parameters, and six auxiliary variables were used for the LST estimation based on random forest machine learning. To confirm the effectiveness of each scheme, we have performed different validation experiments using clear-sky MODIS LSTs. Moreover, we have validated all-weather LSTs using bias-corrected LSTs from 10 in situ stations. In clear-sky daytime, the performance of S2 was better than S1. However, in cloudy sky daytime, S1 simulated low LSTs better than S2, with an average root mean squared error (RMSE) of 2.6 °C compared to an average RMSE of 3.8 °C over 10 stations. At nighttime, S1 and S2 demonstrated no significant difference in performance both under clear and cloudy sky conditions. When the two schemes were combined, the proposed all-weather LSTs resulted in an average R2 of 0.82 and 0.74 and with RMSE of 2.5 °C and 1.4 °C for daytime and nighttime, respectively, compared to the in situ data. This paper demonstrates the ability of the two different schemes to produce all-weather dynamic LSTs. The strategy proposed in this study can improve the applicability of LSTs in a variety of research and practical fields, particularly for areas that are very frequently covered with clouds.

Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes., Measurement(s) local climate zones Technology Type(s) digital curation Factor Type(s) building footprint • building height • impervious surface area • Sky view factor • anthropogenic heat flux • population Sample Characteristic - Environment anthropogenic environment • city Sample Characteristic - Location contiguous United States of America Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12599966

Understanding the diurnal dynamics of surface urban heat islands (SUHIs) is an indispensable step towards their full interpretation at multiple time scales. However, because of the tradeoff between the spatial and temporal resolutions of satellite-derived land surface temperature (LST) data, the climatology, variety, and taxonomy of diurnal SUHI (DSUHI) patterns remain largely unknown for numerous cities with different bioclimates. By combining daily MODIS LST data with a newly developed four-parameter diurnal temperature cycle (DTC) model, we selected 354 Chinese megacities located in different bioclimatic zones to examine the characteristics of the DSUHI descriptors and systematically investigate the prevalent DSUHI temporal patterns. The DSUHI variations demonstrate that both the daily maximum and minimum SUHI intensity (SUHII) can occur during most periods of the day, although these intensities are more likely to occur in the early morning and noon/afternoon. Our results also reveal that both strong SUHIs (SUHII > 3 K) and surface urban cool islands (SUCIs) (SUHII

The land surface temperature (LST) drives many terrestrial biophysical processes and varies rapidly in space and time primarily due to the earth’s diurnal and annual cycles. Models of the diurnal and annual LST cycle retrieved from satellite data can be reduced to several gap-free parameters that represent the surface’s thermal characteristics and provide a generalized characterization of the LST temporal dynamics. In this letter, we use such an approach to map Europe’s annual and diurnal LST dynamics. In particular, we reduce a five-year time series (2009–2013) of diurnal LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to 48 sets of half-hourly annual cycle parameters (ACPs), namely, the mean annual LST, the yearly amplitude of LST, and the LST phase shift from the spring equinox. The derived data provide a complete representation of how mainland Europe responds to the heating of the sun and the nighttime LST decay and reveal how Europe’s biogeographic regions differ in that respect. We further argue that the SEVIRI ACP can provide an observation-based spatially consistent background for studying and characterizing the thermal behavior of the surface and also a data set to support climate classification at a finer spatial resolution.

The diurnal and annual cycles of Earth drive the land surface temperature (LST) variations in space and time but their combined effects are conveyed differently from place to place as a function of climate, local weather conditions, and surface characteristics. Fitting a model of the diurnal or annual LST cycle (DTC and ATC, respectively) to satellite data yields several parameters that summarize the thermal dynamics of each pixel. These parameters are distinct for each place and reveal how the ATC and DTC differ between pixels. Currently, due to the lack of LST with high spatial and temporal resolution, it is impossible to characterize both cycles at kilometer or sub-kilometer scale. This work addresses this problem and presents a method for simultaneously quantifying the annual and diurnal LST dynamics at kilometer scale. The proposed method is based on Annual Cycle Parameters (ACP) retrieved from geostationary satellite data for every 30 min between 00:00 and 23:30 local time and uses statistical downscaling to enhance their coarse spatial resolution. We apply our method to continental Europe for the period 2009–2013 and validate it using independent ACP retrieved from satellite and in-situ LST. Our results reveal how the climate and surface conditions influence the shape of the ACP diurnal profiles over Europe and illustrate for the first time the excellent agreement between satellite-derived ACP and ACP derived from in-situ LST. Even though our method is limited by the spatial coverage of the geostationary data, it provides a novel way for describing Earth's thermal response to solar heating.

Timely meteorological data of high accuracy and spatiotemporal resolution can contribute to sustainable urban planning and management in terms of human thermal comfort, heat wave warning, air pollution prediction, as well as building and transport management among others. Today, the scientific knowledge about the specific urban climate is not fully exploited by these applications. Of particular relevance is thereby the thermal impact which currently is either observed in situ or modelled. Both approaches have advantages but also shortcomings in terms of costs and data needs. In this study we investigated air temperatures estimations from satellite data as alternative source. In particular, we focused on dense time series of land surface temperature as predictors for urban and rural air temperature. We applied empirical models which used LST in predefined time intervals to estimate air temperature using a simple but robust method for empirical modelling. The root mean square errors of the model substantially decreased with the multi-temporal predictors and were below 2 K for most stations. Additionally, the representation of the diurnal cycle improved and the annual cycle was well represented. This method is seen as a great enhancement of existing satellite based air temperature monitoring methods especially for urban applications.

From the current perspectives of urban health and environmental justice research, health is the result of a combination of individual, social and environmental factors. Yet, there are only few attempts to determine their joint influence on health and well-being. Grounded in debates surrounding conceptual models and based on a data set compiled for the city of Hamburg, this paper aims to provide insights into the most important variables influencing urban health. Theoretically, we are primarily referring to the conceptual model of health-related urban well-being (UrbWellth), which systemizes urban influences in four sectors. The systematization of the conceptual model is empirically confirmed by a principal component analysis: the factors derived from the data correspond well with the deductively derived model. Additionally, a multiple linear regression analysis was used to identify the most important variables influencing the participant&rsquo, s self-rated health (SRH): rating of one&rsquo, s social network, rating of neighborhood air quality, rating of neighborhood health infrastructure, heat stress (day/outdoors), cold stress (night/indoors). When controlling for age, income and smoking behavior, these variables explain 12% of the variance of SRH. Thus, these results support the concept of UrbWellth empirically. Finally, the study design helped to identify hotspots with negative impact on SRH within the research areas.

Surface Urban Heat Islands (SUHIs) show large temporal variability, which introduces considerable uncertainty into studies based on a single or a few acquisitions. Besides short-term and more random influences, they exhibit considerable systematic diurnal and seasonal variation, which has been investigated in several recent studies. Here an enhanced method for seasonal SUHI analysis based on time series analysis is presented and tested in the southwest United States using MODIS day- and nighttime land surface temperature data. Despite large scatter at the daily time scale, the method is able to identify and parameterize seasonal signals in time series. The seasonal patterns vary substantially between cities. Coastal cities show the largest nighttime SUHI intensity in spring and the lowest in late summer, while continental cities have their SUHI intensity peaks in September and October. During summer, some cities show a daytime cool island. Our study underlines the need to consider temporal patterns in SUHI assessment. The method proved useful to investigate the SUHI seasonality.

The World Urban Database and Access Portal Tools (WUDAPT) project has grown out of the need for better information on the form and function of cities globally. Cities are described using Local Climate Zones (LCZ), which are associated with a range of key urban climate model parameters and thus can serve as inputs to high resolution urban climate models. We refer to this as level 0 data for each city. The LCZ level 0 product is produced using freely available Landsat imagery, crowdsourced training areas from the community, and the open source SAGA software. This paper outlines the protocol by which LCZ maps generated by different members of the community are produced and evaluated. In particular, the quality assessment comprises cross-validation, review, and cross-comparison with other data sets. To date, the results from the different quality assessments show that the LCZ maps are generally of moderate quality, i.e. 50–60% overall accuracy (OA), but this is much higher when considering all built-up classes together or using weights that take the morphological and climatic similarity of certain classes into account. The training data contributed by researchers from around the world also vary in quality and in the interpretation of the landscape, which affects the final quality of the LCZ maps. The acceptable level of quality needed will depend heavily on the application of the data. However, initial modelling studies that use the level 0 products as inputs showed improved performance in simulating the urban climate when replacing the default surface descriptions with the WUDAPT level 0 data. This is also promising for the application of level 0 data in regional and global climate and weather models and supports the assumption that the current level 0 products are already of sufficient quality for certain applications. Moreover, there are various ongoing developments to improve the methods used to produce LCZ maps and their accuracy.

International audience; This paper proposes a method for generating maps of Local Climate Zones (LCZs) within a GIS using administrative and 2.5D building databases. The LCZs are computed from morphological indicators and building typology, on vector reference spatial units that correspond to urban islets,i.e. blocks of buildings surrounded by nearby roads. The main originality is that, while mean building height criteria correspond exactly to the LCZ classification, a k-means statistical method is used to determine, for each city, the limits between compact and open (and sparsely built) LCZsfor high-, mid- and low-rise LCZs, respectively. For example, in SO12 LCZ look-up tables and the WUDAPT-L0 mapping approach, “compact” LCZs correspond to a building density of over 40%.The resulting groups for Nantes, Toulouse and Paris for mid-height, treated with the proposed statistical method, are 36%, 37% and 33.8% respectively. The LCZ maps for these three cities are compared to the WUDAPT LCZ maps, the latter being obtained from satellite imagery at a re-solution of 100 m. MApUCE LCZ maps show more spatial details, due to their finer resolution, and more variety in urban LCZs within each conurbation. This is very important for modeling micro-climatic effects on town peripheries.

The WUDAPT (World Urban Database and Access Portal Tools project goal is to capture consistent information on urban form and function for cities worldwide that can support urban weather, climate, hydrology and air quality modeling. These data are provided as urban canopy parameters (UCPs) as used by weather, climate and air quality models to simulate the effects of urban surfaces on the overlying atmosphere. Information is stored with different levels of detail (LOD). With higher LOD greater spatial precision is provided. At the lowest LOD, Local Climate Zones (LCZ) with nominal UCP ranges is provided (order 100 m or more). To describe the spatial heterogeneity present in cities with great specificity at different urban scales we introduce the Digital Synthetic City (DSC) tool to generate UCPs at any desired scale meeting the fit-for-purpose goal of WUDAPT. 3D building and road elements of entire city landscapes are simulated based on readily available data. Comparisons with real-world urban data are very encouraging. It is customized (C-DSC) to incorporate each city's unique building morphologies based on unique types, variations and spatial distribution of building typologies, architecture features, construction materials and distribution of green and pervious surfaces. The C-DSC uses crowdsourcing methods and sampling within city Testbeds from around the world. UCP data can be computed from synthetic images at selected grid sizes and stored such that the coded string provides UCP values for individual grid cells.

The urban heat island (UHI) effect influences the heating and cooling (H&C) energy demand of buildings and should be taken into account in H&C energy demand simulations. To provide information about this effect, the PLANHEAT integrated tool—which is a GIS-based, open-source software tool for selecting, simulating and comparing alternative low-carbon and economically sustainable H&C scenarios—includes a dataset of 1 × 1 km hourly heating and cooling degrees (HD and CD, respectively). HD and CD are energy demand proxies that are defined as the deviation of the outdoor surface air temperature from a base temperature, above or below which a building is assumed to need heating or cooling, respectively. PLANHEAT’s HD and CD are calculated from a dataset of gridded surface air temperatures that have been derived using satellite thermal data from Meteosat-10 Spinning Enhanced Visible and Near-Infrared Imager (SEVIRI). This article describes the method for producing this dataset and presents the results for Antwerp (Belgium), which is one of the three validation cities of PLANHEAT. The results demonstrate the spatial and temporal information of PLANHEAT’s HD and CD dataset, while the accuracy assessment reveals that they agree well with reference values retrieved from in situ surface air temperatures. This dataset is an example of application-oriented research that provides location-specific results with practical utility.

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives. ispartof: PLOS ONE vol:14 issue:4 ispartof: location:United States status: published

In the context of developing a low-carbon economy by 2050, the European Union (EU) member states have committed to improve energy efficiency by at least 27% by 2030. To empower municipalities and local authorities addressing this goal, the H2020 PLANHEAT research project develops an open-source software tool for developing economically sustainable energy plans for low-carbon heating and cooling. To take into account the Urban Heat Island (UHI) effect on energy demand, the PLANHEAT software tool uses a geospatial dataset of hourly 1 km Heating and Cooling Degrees that is derived from satellite thermal data and information from weather models. This article describes the methodology used for producing this dataset and presents the first results for the city of Antwerp in Belgium. The PLANHEAT tool will be released as a QGIS plugin in June 2019.

High-quality land use and land cover (LULC) information is of crucial importance for the performance of regional climate models (RCMs), in particular at high spatial resolutions down to convection permitting scales below 4 km. Several satellite-based high-resolution products are currently available for implementation into RCMs. One of the most recent products is the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) dataset. While the ESA CCI LC has been assessed globally, an evaluation against regional, independent LULC datasets is necessary to identify LULC inaccuracies in the respective region of interest and to give regional climate modelers estimates for the uncertainty in the land use forcing. In the present work the ESA CCI LC dataset is compared to the COoRdination and INformation on the Environment (CORINE) Land Cover (CLC). Agreement between the datasets is assessed by proportional area comparison (PAC). The resulting agreement measures are compared to the results of a majority approach (MA) to explore possible differences between the methods. Three timesteps of ESA CCI LC matching the timesteps of CLC are assessed to take a change in agreement over time into account. In addition to the quantification of agreement, spatial patterns of possible issues with ESA CCI LC are identified through utilization of geospatial information systems (GIS). Using the PAC, the agreement of ESA CCI LC with CLC is found to be ∼76 % for the research area (RA). Although the agreement decreases slightly using the PAC, no substantial differences in agreement measures were found compared to the results of the MA. Dominant LULC categories agriculture and forest show an agreement of over 80 % with CLC. A few major issues were found for grassland, wetlands, settlements, and water bodies in the RA of which some might influence RCM performance if the dataset is implemented without adjustment. We highly recommend to apply the PAC to other regions in Europe and further globally to investigate if the found issues are also found elsewhere. The use of more independent regional and specified datasets for validation but also for possible improvement of the ESA CCI LC dataset is suggested.

Most previous studies of surface urban heat islands (SUHIs) have focused solely on their controlling factors on a seasonal/annual timescale, while the controls on daily variations are largely unknown. By extracting the daily variations of nighttime SUHI features using the Gaussian model and investigating their correlations with various explanatory factors, we have attempted to determine the controls on SUHIs on a daily-basis over Chinese cities. Specific controls of weather conditions on the intensity, extent, shape, and centroid of the SUHIs were identified. Our results show that: (1) SUHI intensity (SUHII) was considerably more sensitive to weather conditions than the SUHI footprint (i.e., extent, shape, and centroid). (2) Meteorological variables including relative humidity, accumulated precipitation, and aerosol optical depth, had the greatest impact on SUHI intensity; whereas factors related to temperature fluctuations (day-to-day fluctuations of surface and air temperature) were the main factors influencing SUHI extent, shape, and the direction in which SUHI centroid varies. (3) Antecedent precipitation substantially impacted the subsequent SUHIs under clear-skies, changing both the SUHI itself and its sensitivity to other factors. Typically, the clear-sky SUHIs directly following rainfall showed a higher dependence on the relative humidity, soil moisture and aerosol, but were less affected by wind. (4) The meteorological contributions to the daily nighttime SUHIIs varied among Chinese cities with different bioclimatic conditions. In general, they were stronger in temperate zones than in subtropical zones. Our results provide an improved understanding of the controls on SUHIs on a daily timescale, as well as a foundation for predicting daily SUHIs based on the influencing meteorological variables.

Nowadays, the absence of suitable data that describes the urban landscape in climate relevant terms for climatic models is a significant impediment to progress, even if the physics that underpins these models is universal. To address this data gap the World Urban Database and Access Portal Tools (WUDAPT) project focuses on creating a global database on cities suited for urban climate studies. The first phase of WUDAPT has established a protocol using the Local Climate Zones classification system to partition the urban landscape of cities into neighbourhood types that can inform parameter selection in model applications. In this paper, we explore the potential of these data for use in the application of the Weather Research Forecasting (WRF) model, which incorporates Building Effect Parameterization and Building Energy Model (BEP-BEM) schemes. The test is conducted for Madrid (Spain) during winter and summer and the results of using LCZ derived data are compared with those using CORINE land-cover data. The results are indicative but show that the LCZ scheme improves model performance. The paper emphasizes the need for further work to extend the value of these models for decisions on urban planning. However, such work will need useful urban data to make progress.

There are a number of models available for examining the interaction between cities and the atmosphere over a range of scales, from small scales - such as individual facades, buildings, neighbourhoods - to the effect of the entire conurbation itself. Many of these models require detailed morphological characteristics and material properties along with relevant meteorological data to be initialised. However, these data are difficult to obtain given the heterogeneity of built forms, particularly in newly emerging cities. Yet, the need for models which can be applied to urban areas (for instance to address planning problems) is increasingly urgent as the global population becomes more urban. In this paper, a modeling approach which derives the required land cover parameters for a mid-complex urban energy budget and water budget model (SUEWS) in a consistent manner is evaluated in four cities (Dublin, Hamburg, Melbourne and Phoenix). The required parameters for the SUEWS model are derived using local climate zones (LCZs) for land cover, and meteorological observations from off-site synoptic stations. More detailed land cover and meteorological data are then added to the model in stages to examine the impact on model performance with respect to observations of turbulent fluxes of sensible (QH) and latent (QE) heat. Replacing LCZ land cover with detailed fractional coverages was shown to marginally improve model performance, however the performance of model coupled with ‘coarse’ LCZ data was within the same range of error (20–40Wm−2 for QE and 40–60Wm−2 for QH) as high resolution data.

There is an urgent need for more detailed spatial information on cities globally that has been acquired using a standard method to facilitate comparison and the transfer of scientific and practical knowledge between places. As part of the world urban database and access portal tools (WUDAPT) initiative, a simple workflow has been developed to perform this task. Using freely available satellite imagery (Landsat) and software (SAGA), WUDAPT characterizes settlements using the local climate zone (LCZ) scheme, which decomposes the city into distinctive neighborhoods ( ${ > } \text{1}\ \hbox{km}^2$ ) based on typical properties (e.g., green proportion and built fraction). In this paper, the methodology is extended to examine the effect of adding synthetic aperture radar (SAR) data, which is now freely available from Sentinel 1, for generating LCZs. Using the city of Khartoum as a case study, the results show that combining multispectral and SAR data improves the overall performance of several classifiers, with random forest (RF) performing the best overall.

Local climate zones (LCZs) divide the urban landscape into homogeneous types based on urban structure (i.e.,morphology of streets and buildings), urban cover (i.e., permeability of surfaces), construction materials, and human activities (i.e., anthropogenic heat). This classification scheme represents a standardized way of capturing the basic urban form of cities and is currently being applied globally as part of the world urban database and portal tools (WUDAPT) initiative. This paper assesses the transferability of the LCZ concept to two Ukrainian cities, i.e., Kyiv and Lviv, which differ in urban form and topography, and considers three ways to validate and verify this classification scheme. An accuracy of 64% was achieved for Kyiv using an independent validation dataset while a comparison of the LCZ maps with the GlobeLand30 land cover map resulted in a match that was greater than 75% for both cities. There was also good correspondence between the urban classes in the LCZ maps and the urban points of interest in OpenStreetMap (OSM). However, further research is still required to produce a standardized validation protocol that could be used on a regular basis by contributors to WUDAPT to help produce more accurate LCZ maps in the future.

Local Climate Zones (LCZ) form a systematic classification scheme that is used nowadays for zoning and categorization of the internal structure of urban areas for various applications including urban climatology. This paper applies local climatic zoning mapping for Beirut, a dense city situated along the Mediterranean Sea, and provides details on the dominating urban form and materials of the city thus contributing to the identification of the main parameters that can promote the formation of an urban heat island (UHI) in the city. The thermal impact of the LCZ is evaluated based on previous Town Energy Balance (TEB) model results.

Urban areas are especially vulnerable to high temperatures, which will intensify in the future due to climate change. Therefore, both good knowledge about the local urban climate as well as simple and robust methods for its projection are needed. This study has analysed the spatio-temporal variance of the mean nocturnal urban heat island (UHI) of Hamburg, with observations from 40 stations from different suppliers. The UHI showed a radial gradient with about 2 K in the centre mostly corresponding to the urban densities. Temporarily, it has a strong seasonal cycle with the highest values between April and September and an inter-annual variability of approximately 0.5 K. Further, synoptic meteorological drivers of the UHI were analysed, which generally is most pronounced under calm and cloud-free conditions. Considered were meteorological parameters such as relative humidity, wind speed, cloud cover and objective weather types. For the stations with the highest UHI intensities, up to 68.7 % of the variance could be explained by seasonal empirical models and even up to 76.6 % by monthly models.