Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning

Vulova, Stenka; Meier, Fred; Fenner, Daniel; Nouri, Hamideh; Kleinschmit, Birgit

doi:10.1109/jstars.2020.3019696

Cited by 43 publications

(21 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Differences in the setup of the stations likely affect results regarding the contribution of scales; yet to what extent is not understood. • Follow-up studies could explore the use of machine learning (ML) techniques that are already used to study and predict UHI spatial patterns (Straub et al, 2019;Gardes et al, 2020;Vulova et al, 2020). Simultaneously, existing ML-based techniques could be improved by considering the mesoscale heterogeneity of the urban environment.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, the use of nontraditional and opportunisticsensing technologies in meteorological and climatological research, such as smartphones (Overeem et al, 2013b;Mass and Madaus, 2014;Droste et al, 2017), cars (Haberlandt and Sester, 2010;Mahoney and O'Sullivan, 2013;Bartos et al, 2019), commercial microwave links (Messer et al, 2006;Zinevich et al, 2009;Overeem et al, 2013a;Chwala and Kunstmann, 2019), wrist-mounted wearables (Nazarian et al, 2020), and privately owned citizen weather stations (CWSs), e.g., Wolters and Brandsma (2012), Bell et al (2015), de Vos et al (2017, Meier et al (2017), Fenner et al (2019, Droste et al (2020), and Mandement and Caumont (2020), have shown to provide additional and reliable information, thus, highlighting a multitude of possible applications in research and beyond (de Vos et al, 2019;Nipen et al, 2020). To study urban air temperatures and the UHI effect, data from CWSs have been used in a variety of studies (Steeneveld et al, 2011;Chapman et al, 2017;Fenner et al, 2017;de Vos et al, 2020;Feichtinger et al, 2020;Venter et al, 2020;Vulova et al, 2020), focusing on different cities. One major advantage of CWSs over traditional meteorological stations is their large number within a single city (Meier et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

Varentsov

Fenner

Meier

et al. 2021

Front. Environ. Sci.

Self Cite

View full text Add to dashboard Cite

Urban climate features, such as the urban heat island (UHI), are determined by various factors characterizing the modifications of the surface by the built environment and human activity. These factors are often attributed to the local spatial scale (hundreds of meters up to several kilometers). Nowadays, more and more urban climate studies utilize the concept of the local climate zones (LCZs) as a proxy for urban climate heterogeneity. However, for modern megacities that extend to dozens of kilometers, it is reasonable to suggest a significant contribution of the larger-scale factors to the temperature and UHI climatology. In this study, we investigate the contribution of local-scale and mesoscale driving factors of the nocturnal canopy layer UHI of the Moscow megacity in Russia. The study is based on air temperature observations from a dense network consisting of around 80 reference and more than 1,500 crowdsourced citizen weather stations for a summer and a winter season. For the crowdsourcing data, an advanced quality control algorithm is proposed. Based on both types of data, we show that the spatial patterns of the UHI are shaped both by local-scale and mesoscale driving factors. The local drivers represent the surface features in the vicinity of a few hundred meters and can be described by the LCZ concept. The mesoscale drivers represent the influence of the surrounding urban areas in the vicinity of 2–20 km around a station, transformed by diffusion, and advection in the atmospheric boundary layer. The contribution of the mesoscale drivers is reflected in air temperature differences between similar LCZs in different parts of the megacity and in a dependence between the UHI intensity and the distance from the city center. Using high-resolution city-descriptive parameters and different statistical analysis, we quantified the contributions of the local- and mesoscale driving factors. For selected cases with a pronounced nocturnal UHI, their respective contributions are of similar magnitude. Our findings highlight the importance of taking both local- and mesoscale effects in urban climate studies for megacities into account. Furthermore, they underscore a need for an extension of the LCZ concept to take mesoscale settings of the urban environment into account.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

Varentsov

Fenner

Meier

et al. 2021

Front. Environ. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Such non-traditional, opportunistic sources of data are, e.g., smartphones (e.g., Overeem et al, 2013b;Mass and Madaus 2014;Droste et al, 2017), smart wearable devices (Nazarian et al, 2021), cars (e.g., Haberlandt and Sester 2010;Bartos et al, 2019), commercial microwave links (e.g., Messer et al, 2006;Overeem et al, 2013a;Chwala and Kunstmann 2019), and privately-owned weather stations, called citizen weather stations (CWS) in the following (e.g., Steeneveld et al, 2011;Wolters and Brandsma 2012;Bell et al, 2013;Madaus et al, 2014;Chapman et al, 2017;de Vos et al, 2017;Venter et al, 2021). Each type of these data sources alone or multiple combined can be used in different meteorological and climatological applications, such as weather forecast (e.g., Mass and Madaus, 2014;Nipen et al, 2020), operational weather monitoring (e.g., de Vos et al, 2019), mesoscale model evaluation (e.g., Hammerberg et al, 2018), hydrometeorological analyses and modelling (e.g., Smiatek et al, 2017;de Vos et al, 2020), high-resolution mapping of air temperature (e.g., Venter et al, 2020;Vulova et al, 2020;Zumwald et al, 2021), thermal-comfort assessment (Nazarian et al, 2021), and urban climate investigations (e.g., Fenner et al, 2017Fenner et al, , 2019Droste et al, 2020;Feichtinger et al, 2020). The potential of CWS data is especially large for cities, where population density and thus also CWS network density is high and where traditional meteorological observations are sparse.…”

Section: Introductionmentioning

confidence: 99%

“…CrowdQC is a statistically-based QC with four main and three optional QC levels that are applied sequentially, removing erroneous data based on the assumption that the whole crowd of CWS knows more than each individual station ("wisdom of the crowd"). Since its release, CrowdQC has successfully been applied in a number of studies to qualitycontrol CWS ta data for further analyses (e.g., Fenner et al, 2019;Feichtinger et al, 2020;Venter et al, 2020Venter et al, , 2021Vulova et al, 2020;Benjamin et al, 2021;Potgieter et al, 2021;Zumwald et al, 2021). Its large-scale applicability was only recently demonstrated by the study of Venter et al (2021), using CrowdQC to quality-control data from >50,000 CWS in 342 urban regions in Europe for a summer month.…”

Section: Introductionmentioning

confidence: 99%

CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

Fenner

Bechtel

Demuzere

et al. 2021

Front. Environ. Sci.

Self Cite

View full text Add to dashboard Cite

In recent years, the collection and utilisation of crowdsourced data has gained attention in atmospheric sciences and citizen weather stations (CWS), i.e., privately-owned weather stations whose owners share their data publicly via the internet, have become increasingly popular. This is particularly the case for cities, where traditional measurement networks are sparse. Rigorous quality control (QC) of CWS data is essential prior to any application. In this study, we present the QC package “CrowdQC+,” which identifies and removes faulty air-temperature (ta) data from crowdsourced CWS data sets, i.e., data from several tens to thousands of CWS. The package is a further development of the existing package “CrowdQC.” While QC levels and functionalities of the predecessor are kept, CrowdQC+ extends it to increase QC performance, enhance applicability, and increase user-friendliness. Firstly, two new QC levels are introduced. The first implements a spatial QC that mainly addresses radiation errors, the second a temporal correction of the data regarding sensor-response time. Secondly, new functionalities aim at making the package more flexible to apply to data sets of different lengths and sizes, enabling also near-real time application. Thirdly, additional helper functions increase user-friendliness of the package. As its predecessor, CrowdQC+ does not require reference meteorological data. The performance of the new package is tested with two 1-year data sets of CWS data from hundreds of “Netatmo” CWS in the cities of Amsterdam, Netherlands, and Toulouse, France. Quality-controlled data are compared with data from networks of professionally-operated weather stations (PRWS). Results show that the new package effectively removes faulty data from both data sets, leading to lower deviations between CWS and PRWS compared to its predecessor. It is further shown that CrowdQC+ leads to robust results for CWS networks of different sizes/densities. Further development of the package could include testing the suitability of CrowdQC+ for other variables than ta, such as air pressure or specific humidity, testing it on data sets from other background climates such as tropical or desert cities, and to incorporate added filter functionalities for further improvement. Overall, CrowdQC+ could lead the way to utilise CWS data in world-wide urban climate applications.

show abstract

“…In urban environments, ET is inversely proportional to the intensity of the urban heat island (UHI) effect (Wang et al, 2020). The UHI effect adversely affects the health and quality of life of urban residents (Kovats and Hajat, 2008;Scherer et al, 2013;Vulova et al, 2020). As most of the world population https://doi.org/10.5194/hess-2021-283 Preprint.…”

Section: Introductionmentioning

confidence: 99%

Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data

Rocha¹,

Vulova²,

Tol

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Evapotranspiration (ET) is a fundamental variable to assess water balance and urban heat island effect. ET is deeply dependent on the land cover as it derives mainly from the processes of soil evaporation and plant transpiration. The majority of well-known process-based models based on the Penman-Monteith equation focus on the atmospheric interfaces (e.g. radiation, temperature and humidity), lacking explicit input parameters to describe the land surface. The model Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) accounts for a broad range of surface-atmosphere interactions to predict ET. However, like most modelling approaches, SCOPE assumes a homogeneous vegetated landscape to estimate ET. Urban environments are highly fragmented, exhibiting a blend of pervious and impervious anthropogenic surfaces. Whereas, high-resolution remote sensing (RS) and detailed GIS information to characterise land surfaces is usually available for major cities. Data describing land surface properties were used in this study to develop a method to correct bias in ET predictions caused by the assumption of homogeneous vegetation by process-based models. Two urban sites equipped with eddy flux towers presenting different levels of vegetation fraction and imperviousness located in Berlin, Germany, were used as study cases. The correction factor for urban environments has increased model accuracy significantly, reducing the relative bias in ET predictions from 0.74 to −0.001 and 2.20 to −0.13 for the two sites, respectively, considering the SCOPE model using RS data. Model errors (i.e. RMSE) were also considerably reduced in both sites, from 0.061 to 0.026 and 0.100 to 0.021, while the coefficient of determination (R2) remained similar after the correction, 0.82 and 0.47, respectively. This study presents a novel method to predict hourly urban ET using freely available RS and meteorological data, independently from the flux tower measurements. The presented method can support actions to mitigate climate change in urban areas, where most the world population lives.

show abstract

Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning

Cited by 43 publications

References 50 publications

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data

Contact Info

Product

Resources

About