The urban climate of Ghent, Belgium: A case study combining a high-accuracy monitoring network with numerical simulations

Caluwaerts, Steven; Hamdi, Rafiq; Top, Sara; Lauwaet, Dirk; Berckmans, Julie; Degrauwe, Daan; Dejonghe, Herwig; Ridder, Koen De; Troch, Rozemien De; Duchêne, François; Maiheu, Bino; Ginderachter, Michiel Van; Verdonck, Marie-Leen; Vergauwen, Thomas; Wauters, Guy; Termonia, Piet

doi:10.1016/j.uclim.2019.100565

Cited by 28 publications

(31 citation statements)

References 46 publications

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“…The lack of urban weather observations is a major challenge in characterising the urban climate. Several cities have dedicated observational networks, for example, Birmingham (England, Warren et al, 2016), Berlin (Germany;Fenner et al, 2014), Novi Sad (Serbia; Savić et al, 2019), Ghent (Belgium; Caluwaerts et al, 2020) or Amsterdam (the Netherlands; Ronda et al, 2017), but the majority of cities across the world lack such a detailed network. WMO regulations prevent official weather stations being located in cities, since they require relatively open surroundings.…”

Section: Introductionmentioning

confidence: 99%

Assessing the potential and application of crowdsourced urban wind data

Droste

Heusinkveld

Fenner

et al. 2020

Quart J Royal Meteoro Soc

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The use of crowdsourcing – obtaining large quantities of data through the Internet – has been of great value in urban meteorology. Crowdsourcing has been used to obtain urban air temperature, air pressure, and precipitation data from sources such as mobile phones or personal weather stations (PWSs), but so far wind data have not been researched. Urban wind behaviour is highly variable and challenging to measure, since observations strongly depend on the location and instrumental set‐up. Crowdsourcing can provide a dense network of wind observations and may give insight into the spatial pattern of urban wind. In this study, we evaluate the skill of the popular “Netatmo” PWS anemometer against a reference for a rural and an urban site. Subsequently, we use crowdsourced wind speed observations from 60 PWSs in Amsterdam, the Netherlands, to analyse wind speed distributions of different Local Climate Zones (LCZs). The Netatmo PWS anemometer appears to systematically underestimate the wind speed, and episodes with rain or high relative humidity degrade the measurement quality. Therefore, we developed a quality assurance (QA) protocol to correct PWS measurements for these errors. The applied QA protocol strongly improves PWS data to a point where they can be used to infer the probability density distribution of wind speed of a city or neighbourhood. This density distribution consists of a combination of two Weibull distributions, rather than the typical single Weibull distribution used for rural wind speed observations. The limited capability of the Netatmo PWS anemometer to measure near‐zero wind speed causes the QA protocol to perform poorly for periods with very low wind speeds. However, results for a year‐long wind speed climatology of the wind speed are satisfactory, as well as for a shorter period with higher wind speeds.

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Section: Introductionmentioning

confidence: 99%

Assessing the potential and application of crowdsourced urban wind data

Droste

Heusinkveld

Fenner

et al. 2020

Quart J Royal Meteoro Soc

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“…The city of Ghent (Belgium) has implemented since July 2016 the MOCCA network (Monitoring the City's Climate and Atmosphere) to monitor the canopy layer UHI. [14] demonstrated the MOCCA network importance and its complementarity with two modelling approaches (i.e., the SURFEX land surface model and UrbClim boundary layer model). The Birmingham Urban Climate Laboratory (BUCL) is another high-density urban meteorological network (https://www.birmingham.ac.uk/schools/gees/centres/bucl/ maps-data/index.aspx), comprising 25 weather stations and more than 100 air temperature low-cost wireless sensors, which provided hourly air temperature records in near real-time for the city of Birmingham (UK) between June 2012 and December 2014 [33,34].…”

Section: In Situ Meteorological Datamentioning

confidence: 96%

“…The observations should comply with the WMO standards for the stations monitoring the regional climate or other known standards for monitoring the local climate [12,13]. Many synoptic stations worldwide are placed within the administrative limits of a city, but it is very likely that more sensors will capture more relevant information about of the multifaceted urban climate even if they are not placed in standard conditions [14][15][16]. (c) Metadata.…”

Section: Meteorological Data Resourcesmentioning

confidence: 99%

Meteorological and Ancillary Data Resources for Climate Research in Urban Areas

Cheval

Micu

Dumitrescu

et al. 2020

Climate

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An increasing plethora of both meteorological and ancillary data are presently available for climate research and applications in urban areas. The data are often held by local or national institutions (i.e., meteorological services, universities or environmental agencies). This paper outlines a total number of 33 datasets, organized into three main categories of meteorological data resources (14 datasets) and four categories of ancillary data resources (19 datasets), selected for their potential to support urban climate studies, but also for their free accessibility. Such a collection cannot be exhaustive, but we aim to draw the attention of the scientific community to relevant datasets, freely available at temporal and spatial resolutions appropriate for urban climatology. Each dataset contains information about its availability, limitations, and examples of research in urban areas.

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“…In collaboration with related government institutions, private companies, local government, and residents, TOMACS aims to develop a predicting and monitoring early warning system of extreme phenomena, and to implement social experiments on extremeweather-resilient cities. In addition, a number of major field campaigns in different cities around the world have been conducted (1) in USA, for example URBAN 2000 (Allwine et al 2002); Joint Urban 2003 (Allwine et al 2004); Pentagon Shield (Warner et al 2007); Madison Square Garden (Hanna et al 2003); (2) in Europe, for example ESCOMPTE (Mestayer et al 2005); CAPITOUL (Masson et al 2008); BUBBLE (Rotach et al 2005); DAPPLE (Arnold et al 2004), MOCCA (Caluwaerts et al 2020), and for examples in the city of Szeged (Skarbit et al 2017) and Novi Sad (Secerov et al 2015;Šećerov et al 2019). Finally, there is still a need to harmonize collection practice, instrumentation, station location, and quality controls across cities to facilitate collaborative research (Muller et al 2015).…”

Section: Observations In Citiesmentioning

confidence: 99%

The State-of-the-Art of Urban Climate Change Modeling and Observations

et al. 2020

Self Cite

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As an effect of climate change, cities need detailed information on urban climates at decision scale that cannot be easily delivered using current observation networks, nor global and even regional climate models. A review is presented of the recent literature and recommendations are formulated for future work. In most cities, historical observational records are too short, discontinuous, or of too poor quality to support trend analysis and climate change attribution. For climate modeling, on the other hand, specific dynamical and thermal parameterization dedicated to the exchange of water and energy between the atmosphere and the urban surfaces have to be implemented. Therefore, to fully understand how cities are impacted by climate change, it is important to have (1) simulations of the urban climate at fine spatial scales (including coastal hazards for coastal cities) integrating global climate scenarios with urban expansion and population growth scenarios and their associated uncertainty estimates, (2) urban climate observations, especially in Global South cities, and (3) spatial data of high resolution on urban structure and form, human behavior, and energy consumption.

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The urban climate of Ghent, Belgium: A case study combining a high-accuracy monitoring network with numerical simulations

Cited by 28 publications

References 46 publications

Assessing the potential and application of crowdsourced urban wind data

Assessing the potential and application of crowdsourced urban wind data

Meteorological and Ancillary Data Resources for Climate Research in Urban Areas

The State-of-the-Art of Urban Climate Change Modeling and Observations

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