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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.

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.

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)

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

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.

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.

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.

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.

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.

The World Urban Database and Access Portal Tools (WUDAPT) is an international community-based initiative to acquire and disseminate climate relevant data on the physical geographies of cities for modeling and analysis purposes. The current lacuna of globally consistent information on cities is a major impediment to urban climate science toward informing and developing climate mitigation and adaptation strategies at urban scales. WUDAPT consists of a database and a portal system; its database is structured into a hierarchy representing different levels of detail, and the data are acquired using innovative protocols that utilize crowdsourcing approaches, Geowiki tools, freely accessible data, and building typology archetypes. The base level of information (L0) consists of local climate zone (LCZ) maps of cities; each LCZ category is associated with a range of values for model-relevant surface descriptors (roughness, impervious surface cover, roof area, building heights, etc.). Levels 1 (L1) and 2 (L2) will provide specific intra-urban values for other relevant descriptors at greater precision, such as data morphological forms, material composition data, and energy usage. This article describes the status of the WUDAPT project and demonstrates its potential value using observations and models. As a community-based project, other researchers are encouraged to participate to help create a global urban database of value to urban climate scientists.

The local climate in cities differs from the one in rural areas, most prominently characterized by increased surface and air temperatures, known as the “(surface) urban heat island”. As climate has changed and continues to change in all areas of the world, the question arises whether the effects that are noticeable in urban areas are “homemade”, or whether some of them originate from global and regional scale climate changes. Identifying the locally induced changes of urban meteorological parameters is especially relevant for the development of adaptation and mitigation measures. This study aims to distinguish global and regional climate change signals from those induced by urban land cover. Therefore, it provides a compilation of observed and projected climate changes, as well as urban influences on important meteorological parameters. It is concluded that evidence for climate change signals is found predominantly in air temperature. The effect of urban land cover on local climate can be detected for several meteorological parameters, which are air and surface temperature, humidity, and wind. The meteorology of urban areas is a mixture of signals in which the influencing parameters cannot be isolated, but can be assessed qualitatively. Blending interactions between local effects and regional changes are likely to occur.

Beirut, a densely populated city along the Mediterranean coastline, has been witnessing temperature records well above thermal comfort levels especially during the past decade. The significant effect of existing urbanised materials on these rising urban temperatures has been verified through the previous modeling of the urban heat island phenomenon (UHI) of the city using the Town Energy Balance model (TEB). The predominantly dense mix of midrise buildings spread across the city with few trees has also been confirmed by previous local climatic zoning classification. The first aim of this paper was to compare different input data used for the classifications. Two Landsat 8 scenes (optical and thermal, acquired in 2013 and 2014) as well as one Sentinel-1, and one Sentinel-2 scene (both acquired in 2015) were tested as input data. The best results were achieved with both optical sensors, while thermal and SAR data alone did not perform as well. Secondly, the transferability of training area between Beirut and Damascus city was tested. It was found that the diverse biophysical and climatic backgrounds make this difficult resulting in poor accuracies. Finally, long-term surface temperature characteristics from thermal remote sensing images were compared to the LCZ classifications. Although the surface heat island effects could be seen in annual cycle parameters, the patterns were dominated by land cover and topo-climatic effects due to the high vertical extent of the region of interest.

Assessment of surface urban heat islands (SUHI) has been hampered by the lack of a consistent framework to permit consistent interpretation between cities. Local Climate Zones (LCZ) are a universal description of local scale landscape types based on expected variation at neighbourhood scale (≥1km2) in and around cities. In this study, we investigate the suitability of the LCZ scheme for SUHI studies based on 50 cities from across the globe. For comparability we use an annual temperature cycle model for MODIS land surface temperature (LST) at different overpass times and multi-year mean Landsat 8 LST. The SUHI analysis shows significant differences in the intra-urban estimate of SUHI for different built LCZ types. Substantial variability of SUHI within LCZ classes and between cities exists and SUHI patterns vary by time of day. Landsat derived estimates have very high correlations to those from MODIS at a similar time. The use of an LCZ approach combined with annual SUHI estimates provides a promising approach for a consistent and comprehensive SUHI analysis framework subject to further work to assess the spatial scale of matching LST and LCZ data, filter for topographic effects, and include the phenological status.

The downscaling of frequently-acquired geostationary Land Surface Temperature (LST) data can compensate the lack of high spatiotemporal LST data for urban climate studies. In order to be usable, the generated datasets must accurately reproduce the spatiotemporal features of the coarse-scale LST time series with greater spatial detail. This work concerns this issue and exploits the high temporal resolution of the data to address it. Specifically, it assesses the accuracy, correct pattern formation and the spatiotemporal inter-relationships of an urban three-month-long downscaled geostationary LST time series. The results suggest that the downscaling process operated in a consistent manner and preserved the radiometry of the original data. The exploitation of the data inter-relationships for evaluation purposes revealed that the downscaled time series reproduced the smooth diurnal cycle, but the autocorrelation of the downscaled data was higher than the original coarse-scale data. Overall, the evaluation process showed that the generation of high spatiotemporal LST data for urban areas is very challenging, and to deem it successful, it is mandatory to assess the temporal evolution of the urban thermal patterns. The results suggest that the proposed tests can facilitate the evaluation process.

Monitoring of (surface) urban heat islands (UHI) is possible through satellite remote sensing of the land surface temperature (LST). Previous UHI studies are based on medium and high spatial resolution images, which are in the best-case scenario available about four times per day. This is not adequate for monitoring diurnal UHI development. High temporal resolution LST data (a few measurements per hour) over a whole city can be acquired by instruments onboard geostationary satellites. In northern Germany, geostationary LST data are available in pixels sized 3,300 by 6,700 m. For UHI monitoring, this resolution is too coarse, it should be comparable instead to the width of a building block: usually not more than 100 m. Thus, an LST downscaling is proposed that enhances the spatial resolution by a factor of about 2,000, which is much higher than in any previous study. The case study presented here (Hamburg, Germany) yields promising results. The latter, available every 15 min in 100 m spatial resolution, showed a high explained variance (R2: 0.71) and a relatively low root mean square error (RMSE: 2.2 K). For lower resolutions the downscaling scheme performs even better (R2: 0.80, RMSE: 1.8 K for 500 m; R2: 0.82, RMSE: 1.6 K for 1,000 m).

Today, only few spaceborne sensors can deliver the thermal infrared information at medium scale required for urban heat island (UHI) modeling and assessment for applications in urban design. Despite recent advances in assessing urban thermal behavior, new methods for the estimation of UHI parameters are still needed. Relevant information about the thermal behavior is coded in the annual cycle of surface temperatures. In this letter, the robustness of parameters extracted from the annual cycle was tested. Therefore, a larger data set of Landsat surface temperatures was generated by cloud masking, and parameters from disjoint subsets were compared. Furthermore, a large number of virtual samples were generated to assess the accurateness of the extracted parameters. It was found that the mean and amplitude of the annual cycles in (black body) surface temperature can be estimated with an accuracy of approximately 1 K from 35 thermal infrared images. Hence, the multitemporal cycle parameters are an asset to characterize urban thermal landscapes.

Remote sensing has widely been used in urban climatology since it has the advantage of a simultaneous synoptic view of the full urban surface. Methods include the analysis of surface temperature patterns, spatial (biophysical) indicators for urban heat island modelling, and flux measurements. Another approach is the automated classification of urban morphologies or structural types. In this study it was tested, whether Local Climate Zones (a new typology of thermally 'rather' homogenous urban morphologies) can be automatically classified from multisensor and multitemporal earth observation data. Therefore, a large number of parameters were derived from different datasets, including multitemporal Landsat data and morphological profiles as well as windowed multiband signatures from an airborne IFSAR-DHM. The results for Hamburg, Germany, show that different datasets have high potential for the differentiation of urban morphologies. Multitemporal thermal data performed very well with up to 96.3 % overall classification accuracy with a neuronal network classifier. The multispectral data reached 95.1 % and the morphological profiles 83.2 %.The multisensor feature sets reached up to 97.4 % with 100 selected features, but also small multisensoral feature sets reached good results. This shows that microclimatic meaningful urban structures can be classified from different remote sensing datasets. Further, the potential of the parameters for spatiotemporal modelling of the mean urban heat island was tested. Therefore, a comprehensive mobile measurement campaign with GPS loggers and temperature sensors on public buses was conducted in order to gain in situ data in high spatial and temporal resolution.

Urban environments are potentially threatened by changing climate, especially in form of heat and flash flood hazards. Therefore, there is an urgent need to assess future urban vulnerability to these phenomenons. Purpose of this research is to define heat and flash flood indicators, based on land use, cover and morphology ofurban environments. We enhanced a standard scheme of local climate zones with extended urban morphology, which plays a key role in urban heat island effect. As heat hazard indicator we defined number of days, with fixed threshold exceeded temperature. Meanwhile for flash flood hazard we selected Curve Number, which considers soil type, hydrologic condition, land use, cover and percentage of impervious surface. Both of these indicators can be related to discrete urban classes and be ranked by hazard severity. Further step is to model future urban environment withrelation of heat and flash flood hazards, which eventually will be incorporated into future urban vulnerability framework.

The urban heat island is the most analyzed feature in urban climatology and an important application in urban remote sensing. A common approach is the application of thermal-infrared radiometers for inner urban temperature characterization, although several problems remain unsolved. Another approach is the direct determination of thermal surface properties and thematic classification using multitemporal thermal imagery. In this study multitemporal Landsat data is correlated with a long-term urban heat island pattern with R of up to 0.76. Furthermore, parameters of the annual cycle in surface temperatures at acquisition time are fitted and evaluated for different land use classes. The classes show significantly different probability density functions in the annual cycle parameters which therefore can be expected to be suitable features for their classification.

Urban morphology plays a crucial role in the alteration of the local climate, resulting in the formation of Urban Heat Islands. Regarding the steady growth of cities and the impact of global climate change, the risk of overheating is expected to increase. In order to reduce this risk and the resulting health problems, planning measures are needed to adapt to these severe events. Planners however need tools to quantify and evaluate different adaption strategies to judge its effectiveness. This paper presents conceptual thoughts towards the development of such a planning tool, as well as proposing a method to derive potential areas of intra Urban Heat Islands. A first calibration of the landscape metric for city of Hamburg region is presented, which showcases the method.