Hourly evolution of intra-urban temperature variability across the local climate zones. The case of Madrid

Peiró, Miguel Núñez; Sánchez, Carmen Sánchez-Guevara; González, Francisco Javier Neila

doi:10.1016/j.uclim.2021.100921

Cited by 31 publications

(8 citation statements)

References 107 publications

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“…Between 2016 and 2019, a continuous monitoring campaign was carried out at 20 fixed urban sites with the aim to study the temporal patterns of the UHI in Madrid [97]. In the present study, we use part of that experimental data to define the outputs of our ANN models.…”

Section: Study Area: the City Of Madridmentioning

confidence: 99%

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

et al. 2021

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

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Section: Study Area: the City Of Madridmentioning

confidence: 99%

“…Both erroneous and missing values were left blank in the analysed dataset. Further details about the monitoring campaign and QC procedures can be found in [97].…”

Section: Study Area: the City Of Madridmentioning

confidence: 99%

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

et al. 2021

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“…Many different approaches have been tested to better understand local climate at the urban scale, such as in-situ observations using both xed 11,12 and mobile stations (e.g. vehicle-based sensors 13 or wearable devices 14,15 ), satellite data from Landsat 16 , MODIS 17 , ASTER 18 or Sentinel 19 , among others, or climate simulation models at high resolution (≈1 km 2 ) 20,21 .…”

Section: Introductionmentioning

confidence: 99%

Citizen data for global mapping of atmospheric urban heat islands

Peiró

Khosla

McCulloch

et al. 2023

Preprint

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Urban heat islands are compounding the dangerous impacts of urban heat exposure globally. Cities across geographies require immediate and advanced urban climate information with enough resolution to detect risks and prioritise local climate adaptation interventions for their people. This research analyses the applicability of 550,000 citizen weather stations globally, a network 12 times larger than professionally operated weather stations, to measure urban climate at the highest spatio-temporal resolution and support climate adaptation policies globally. Cities with higher citizen data availability worldwide are identified, and a first open-source procedure in Python is proposed that cities around the world can use. The approach is validated in London, integrating temperature data from Netatmo and Wunderground platforms. The validation tests showed how pre-processing techniques improve citizen data accuracy, decreasing mean temperature deviation from 0.98ºC to 0.48ºC, inside the precision range of sensors. Data analytic methods identified significant implications of urban overheating in the case of London, increasing annual cooling degree hours by 60% in some urban areas, with different night and daytime hot spots. Aerodynamics and imperviousness affected urban overheating at night and daytime differently, requiring different localised interventions. Such large-scale application and scalability of open-access and readily available citizen data networks can efficiently support and monitor localised climate adaptation to mitigate the impacts of rising heat.

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“…The primary motivation for developing LCZs was to improve the description of sites in observational studies in a move away from the historical urban-rural differentiation when investigating urban heat. The classification has been widely used to determine appropriate urban and rural sites for traditional urban heat island intensity calculations (Siu and Hart, 2013) and to explore variability in intra-urban air temperatures in observation studies (Núñez-Peiró et al, 2021;Potgieter et al, 2021). LCZs have also become commonly used in urban climate modelling, with LCZ classes being integrated into urban land cover classification in mesoscale models (Brousse et al, 2016;Zonato et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Transformation in City-Descriptive Input Data for Urban Climate Models

Lipson

Nazarian

Hart

et al. 2022

Front. Environ. Sci.

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In urban climate studies, datasets used to describe urban characteristics have traditionally taken a class-based approach, whereby urban areas are classified into a limited number of typologies with a resulting loss of fidelity. New datasets are becoming increasingly available that describe the three-dimensional structure of cities at sub-metre micro-scale resolutions, resolving individual buildings and trees across entire continents. These datasets can be used to accurately determine local characteristics without relying on classes, but their direct use in numerical weather and climate modelling has been limited by their availability, and because they require processing to conform to the required inputs of climate models. Here, we process building-resolving datasets across large geographical extents to derive city-descriptive parameters suitable as common model inputs at resolutions more appropriate for local or meso-scale modelling. These parameter values are then compared with the ranges obtained through the class-based Local Climate Zone framework. Results are presented for two case studies, Sydney and Melbourne, Australia, as open access data tables for integration into urban climate models, as well as codes for processing high-resolution and three-dimensional urban datasets. We also provide an open access 300 m resolution building morphology and surface cover dataset for the Sydney metropolitan region (approximately 5,000 square kilometres). The use of building resolving data to derive model inputs at the grid scale better captures the distinct heterogenetic characteristics of urban form and fabric compared with class-based approaches, leading to a more accurate representation of cities in climate models. As consistent building-resolving datasets become available over larger geographical extents, we expect bottom-up approaches to replace top-down class-based frameworks.

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Hourly evolution of intra-urban temperature variability across the local climate zones. The case of Madrid

Cited by 31 publications

References 107 publications

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

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

Citizen data for global mapping of atmospheric urban heat islands

A Transformation in City-Descriptive Input Data for Urban Climate Models

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