Modeling an Urban Heat Island during Extreme Frost in Moscow in January 2017

Yushkov, V. P.; Kurbatova, M. M.; Varentsov, Mikhail; Лезина, Е. А.; Kurbatov, G. A.; Miller, E. A.; Репина, И. А.; Artamonov, A. A.; Kallistratova, M. A.

doi:10.1134/s0001433819050128

Cited by 15 publications

(7 citation statements)

References 52 publications

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“…Such a criterium corresponds to the 50th percentile of the daily maximum ΔT in summer and the 90th percentile in winter. In summer, such UHI intensities are common for Moscow during nocturnal hours, while in winter, they may be observed during the whole day under frosty weather conditions (Yushkov et al, 2019;Varentsov et al, 2020b). Nonetheless, to exclude the effects of direct solar heating on the UHI spatial patterns and possible uncertainties of the CWSs and AAQSs, we considered only the nocturnal and early morning hours, i.e., 21-2 UTC (0-5 local time) for summer and 18-6 UTC (21-9 local time) for winter.…”

Section: Sampling and Preprocessing The Observationsmentioning

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.

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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.

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Section: Sampling and Preprocessing The Observationsmentioning

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.

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“…• 1-31 January 2017, a winter period with diverse weather conditions, including extreme cold temperatures on 7-10 January that were accompanied by the development of an intense winter UHI [70].…”

Section: Model Setup and Study Periodsmentioning

confidence: 99%

“…The considered winter period of January 2017 is characterised by a variety of weather conditions and includes three episodes with intense UHIs (Figure 12). The first episode was observed on a background of extreme frosts between 7-11 January (see detailed case study analysis in [70]), and the two shorter episodes were observed later that month around the 17th and 26th of January. The model reasonably reproduces the observed variations of the rural temperature, including its rapid drops and the lowest extremes (Figure 12a).…”

Section: Model-to-observation Comparison For Wintermentioning

confidence: 99%

Impact of Urban Canopy Parameters on a Megacity’s Modelled Thermal Environment

2020

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Urban canopy parameters (UCPs) are essential in order to accurately model the complex interplay between urban areas and their environment. This study compares three different approaches to define the UCPs for Moscow (Russia), using the COSMO numerical weather prediction and climate model coupled to TERRA_URB urban parameterization. In addition to the default urban description based on the global datasets and hard-coded constants (1), we present a protocol to define the required UCPs based on Local Climate Zones (LCZs) (2) and further compare it with a reference UCP dataset, assembled from OpenStreetMap data, recent global land cover data and other satellite imagery (3). The test simulations are conducted for contrasting summer and winter conditions and are evaluated against a dense network of in-situ observations. For the summer period, advanced approaches (2) and (3) show almost similar performance and provide noticeable improvements with respect to default urban description (1). Additional improvements are obtained when using spatially varying urban thermal parameters instead of the hard-coded constants. The LCZ-based approach worsens model performance for winter however, due to the underestimation of the anthropogenic heat flux (AHF). These results confirm the potential of LCZs in providing internationally consistent urban data for weather and climate modelling applications, as well as supplementing more comprehensive approaches. Yet our results also underline the continued need to improve the description of built-up and impervious areas and the AHF in urban parameterizations.

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“…The results showed that the model could predict the urban thermal environment. Yushkov et al [13] applied the mesoscale weather research and forecasting (WRF) model to describe the mixing process of the atmospheric boundary layer in detail and concluded that the model could only capture the main features of UHIs. Vitanova and Kusaka [14] investigated the characteristics of UHI and found that the degree of urbanization's intensification impacted urban temperature distribution Mirzaei and Haghighat [9] reviewed the mathematical models for the study of UHIs.…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation on the Urban Heat Island Effect by Using a Porous Media Model

Ming

Lian

et al. 2021

Energies

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The urban heat island (UHI) effect resulted from urbanization as well as industrialization has become a major environmental problem. UHI effect aggravates global warming and endangers human health. Thus, mitigating the UHI effect has become a primary task to address these challenges. This paper verifies the feasibility of a three-dimensional turbulent porous media model. Using this model, the authors simulate the urban canopy wind-heat environment. The temperature and flow field over a city with a concentric circular structure are presented. The impact of three factors (i.e., anthropogenic heat, ambient crosswind speed, and porosity in the central area) on turbulent flow and heat transfer in the central business district of a simplified city model with a concentric circular structure were analyzed. It is found that the three-dimensional turbulent porous media model is suitable for estimating the UHI effect. The UHI effect could be mitigated by reducing the artificial heat and improving the porosity of the central city area.

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Modeling an Urban Heat Island during Extreme Frost in Moscow in January 2017

Cited by 15 publications

References 52 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

Impact of Urban Canopy Parameters on a Megacity’s Modelled Thermal Environment

Numerical Investigation on the Urban Heat Island Effect by Using a Porous Media Model

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