Influence of Meteorological Parameters on the Urban Heat Island in Moscow

Abstract

The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the whole urban area together with rural zone have averaged 1.9 and 0.9 °C, respectively, in recent years. The UHI in Moscow has stabilized over the past decade and is not growing. Under conditions of a strong anticyclone, the maximal UHI intensity in space and time reaches 11–12 °C. Low cloudiness and amplitudes of diurnal air temperature, as well as surface temperature, demonstrate the closest relationship with the UHI intensity among other parameters with the correlation coefficient of up to −0.67 for low cloudiness and the maximal UHI intensity. The effect of wind speed, total cloudiness and relative humidity on the UHI is slightly weaker, but still significant. The relationships of all meteorological parameters with the maximal UHI intensity are closer than those with the average one. The multiple correlation coefficient between the maximal UHI intensity and both parameters (low cloudiness and average daily wind speed) is 0.76–0.82. The UHI intensity function of air temperature has a minimum in the range from −4 to 0 °C; its growth both at lower and higher T is due to the influence of anticyclonic weather. The UHI intensity function of wind speed decreases with wind strength. The threshold value at which this function asymptotically approaches its lower limit is 10 m/s in the 40–200 m air layer. The UHI intensity functions of both total and low cloudiness decrease with increasing cloudiness and the differences between them are significant if the cloud cover is more than 50%.

Machine Learning for Simulation of Urban Heat Island Dynamics Based on Large-Scale Meteorological Conditions

“…It is well known that UHI is strongly modulated by wind speed and cloudiness [57]. Such dependences have also been reported for Moscow [3]. In our study, these dependencies are taken into account through the WF, which becomes the most important observational-based predictor.…”

“…The urban-induced meteorological phenomena of Moscow are easy to detect against the homogeneous surroundings and quasi-symmetric urban planning features, which makes the city a convenient site for urban climate research. The city experiences an intense UHI with an increasing magnitude trend over the last decades [45][46][47], with a present-day annual-mean UHI magnitude of 2 K peaking at more than 10 K during calm and clear nights [3,45,46].…”

“…Urban heat island (UHI), i.e., a temperature excess in the cities with respect to their rural or natural surroundings, is the most obvious and most studied feature of urban climate that appears due to land cover modifications and anthropogenic activity. Such temperature excess may exceed 10 K under favorable calm and clear weather conditions [1][2][3] and affects urban dwellers and ecosystems in different ways. The UHI increases heat stress for urban citizens and even heat-related mortality during heat waves [4,5], serves as one of the driving factors for urban-induced impacts on convective processes and related dangerous weather events, including intense precipitation and thunderstorms [6,7], and modifies phenology cycles [8,9].…”

“…Our study aims to conduct a deeper investigation of the possibilities and limitations of state-of-the-art ML models to approximate and predict the observed temporal dynamics of the UHI magnitude based on background meteorological variables. We use long-term meteorological observations available for the mid-latitude megacity of Moscow, Russia, to address the following research questions: (1) to what extent ML models are able to reproduce the UHI magnitude based on predictors characterizing background meteorological conditions; (2) what is the difference between state-of-the-art ML models in the performance of the UHI approximation; and (3) what are the most relevant predictors for the UHI magnitude. The presentation has the following structure: the next Section 2 describes the study area and meteorological data, provides a statement of the machine learning problem, and presents the specific ML models used in our study.…”

Spatiotemporal Variation of Summertime Urban Heat Island (UHI) and Its Correlation with Particulate Matter (PM2.5) over Metropolitan Cities in Alabama

“…The earliest recorded microclimatic phenomenon was documented by Howard in 1833 in the City of London. Other major cities, including New York [40], Moscow [41], and Beijing [10], as well as smaller cities like Eilat [42] and Tonami in Japan [24,43], have also experienced this phenomenon.…”