scholarly journals Modeling of the heat island in the period of extreme frost in Moscow in January 2017

2019 ◽  
Vol 55 (5) ◽  
pp. 13-31
Author(s):  
V. P. Yushkov ◽  
M. M. Kurbatova ◽  
M. I. Varentsov ◽  
E. A. Lezina ◽  
G. A. Kurbatov ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Urban Heat Island ◽  
Mesoscale Model ◽  
Measurement Data ◽  
Temperature Fields ◽  
Heat Island ◽  
Vertical Extent ◽  
Numerical Predictions ◽  
Urban Heat

Using the example of an analysis of an extreme lowering of temperature in Moscow in January 2017, the horizontal and vertical extent of the urban heat island against the background of a strong stable stratification of the atmospheric boundary layer is studied. The possibilities of measuring and monitoring the vertical structure of the atmosphere by means of ground-based remote sensing are investigated. The capabilities of the mesoscale model WRF, adapted for a detailed description of mixing processes in the atmospheric boundary layer, in reproducing the spatial dynamics of the temperature anomaly are demonstrated. The numerical estimates of the amplitude and vertical extent of the urban heat island are compared with the measurement accuracy and the total errors of the numerical predictions. Comparison of measurement data and numerical simulation results on the WRF model, using the example of a winter urban heat island in January 2017, showed that mesoscale synoptic models so far only capture the main features of the urban heat island. But deviations between model and observed temperature fields can reach 5 C.

The Influence of Synoptic Pattern and Atmospheric Boundary Layer on PM10 and Urban Heat Island

2013 ◽  
Vol 22 (5) ◽  
pp. 796-807 ◽  
Author(s):  
Guang-Xing He ◽  
Chuck Wah Francis Yu ◽  
Chan Lu ◽  
Qi-Hong Deng
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Urban Heat Island ◽  
Heat Island ◽  
Synoptic Pattern ◽  
Urban Heat

Urban heat island in the atmospheric boundary layer

2021 ◽  
Author(s):  
Andrey P. Kamardin ◽  
Vladimir A. Gladkikh ◽  
Irina V. Nevzorova ◽  
Sergey L. Odintsov
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Urban Heat Island ◽  
Heat Island ◽  
Urban Heat

“Cold lens” above an urban heat island in the atmospheric boundary layer

2021 ◽  
Author(s):  
Andrey P. Kamardin ◽  
Vladimir A. Gladkikh ◽  
Irina V. Nevzorova ◽  
Sergey L. Odintsov
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Urban Heat Island ◽  
Heat Island ◽  
Urban Heat

A comparative study of urban heat island effects in two Belarusian cities from satellite and ground-based observations

2021 ◽  
Author(s):  
Heorhi Burchanka ◽  
Yahor Prakopchyk ◽  
Tsimafei Schlender ◽  
Aleh Baravik ◽  
Siarhei Barodka
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
City Planning ◽  
Thermal Characteristics ◽  
Data Availability ◽  
Eastern Bloc ◽  
Temperature Fields ◽  
Landsat 8 ◽  
Heat Island ◽  
Urban Heat

<p>This study is devoted to analysis of urban development effects on surface thermal characteristics for the case of Belarusian cities of Minsk and Mahiloŭ. Both cities being situated on the same latitude (53.90 N) and not far from each other (~180 km distance), while also sharing a number of similar features typical for cities in Belarus (and in some other former Eastern Bloc countries as well), Minsk and Mahiloŭ nevertheless differ significantly in terms of their population, size and structure. It is therefore of interest to perform urban climate studies for these two cities in parallel.</p><p>First, we use geoinformation systems (QGIS), centralized city planning databases and Open Street Maps (OSM) vector data to implement description of Minsk and Mahiloŭ urban territories in terms of functional zones, taking into account such features as buildings density and urban area category (industrial, residential, business, recreational and other types).</p><p>Furthermore, we perform analysis of surface temperature fields for both cities from satellite data (Landsat-8) and ground-based observations, the latter including both regular meteorological stations (in urban as well as surrounding rural areas) and a volunteer network of weather and air quality sensors distributed in both cities as part of the AirMQ project [1]. We analyze observations for several months in the 2019-2021 period (depending on data availability), paying special attention to days with specific weather conditions (e.g. blocking anticyclones).</p><p>Analysis demonstrates clear evidence of significant urban heat island effects in thermal regimes of both cities, with specific areas of increased temperature related to urban zoning, industrial and green areas, buildings heights and density. However, the selected method of surface urban heat island (SUHI) detection turns out to be somewhat limited for the purposes of studying the effects of blocking anticyclones on urban heat island phenomena development, thereby calling for application of atmospheric numerical modelling techniques.</p><p>[1] AirMQ project, URL: https://airmq.by/</p>

An Observational Case Study of Synergies between an Intense Heat Wave and the Urban Heat Island in Beijing

2020 ◽  
Vol 59 (4) ◽  
pp. 605-620 ◽  
Author(s):  
Ning An ◽  
Jingjing Dou ◽  
Jorge E. González-Cruz ◽  
Robert D. Bornstein ◽  
Shiguang Miao ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Urban Heat Island ◽  
Heat Wave ◽  
Apparent Temperature ◽  
Heat Index ◽  
Heat Island ◽  
Canopy Layer ◽  
Urban Heat ◽  
Peak Pattern ◽  
Intense Heat

AbstractThe focus of this study is an intense heat episode that occurred on 9–13 July 2017 in Beijing, China, that resulted in severe impacts on natural and human variables, including record-setting daily electricity consumption levels. This event was observed and analyzed with a suite of local and mesoscale instruments, including a high-density automated weather station network, soil moisture sensors, and ground-based vertical instruments (e.g., a wind profiler, a ceilometer, and three radiometers) situated in and around the city, as well as electric power consumption data and analysis data from the U.S. National Centers for Environmental Prediction. The results show that the heat wave originated from dry adiabatic warming induced by the dynamic downslope and synoptic subsidence. The conditions were aggravated by the increased air humidity during subsequent days, which resulted in historically high records of the heat index (i.e., an index representing the apparent temperature that incorporates both air temperature and moisture). The increased thermal energy and decreased boundary layer height resulted in a highly energized urban boundary layer. The differences between urban and rural thermal conditions throughout almost the entire boundary layer were enhanced during the heat wave, and the canopy-layer urban heat island intensity (UHII) reached up to 8°C at a central urban station at 2300 local standard time 10 July. A double-peak pattern in the diurnal cycle of UHIIs occurred during the heat wave and differed from the single-peak pattern of the decadal average UHII cycles. Different spatial distributions of UHII values occurred during the day and night.

scholarly journals Advantages of using a fast urban boundary layer model as compared to a full mesoscale model to simulate the urban heat island of Barcelona

2016 ◽  
Vol 9 (12) ◽  
pp. 4439-4450 ◽  
Author(s):  
Markel García-Díez ◽  
Dirk Lauwaet ◽  
Hans Hooyberghs ◽  
Joan Ballester ◽  
Koen De Ridder ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Simple Model ◽  
Wind Speed ◽  
Mesoscale Model ◽  
Urban Climate ◽  
Urban Canopy ◽  
Forecast Model ◽  
Heat Island ◽  
Layer Model ◽  
Urban Heat

Abstract. As most of the population lives in urban environments, the simulation of the urban climate has become a key problem in the framework of the climate change impact assessment. However, the high computational power required by high-resolution (sub-kilometre) fully coupled land–atmosphere simulations using urban canopy parameterisations is a severe limitation. Here we present a study on the performance of UrbClim, an urban boundary layer model designed to be several orders of magnitude faster than a full-fledged mesoscale model. The simulations are evaluated with station data and land surface temperature observations from satellites, focusing on the urban heat island (UHI). To explore the advantages of using a simple model like UrbClim, the results are compared with a simulation carried out with a state-of-the-art mesoscale model, the Weather Research and Forecasting Model, which includes an urban canopy model. This comparison is performed with driving data from ERA-Interim reanalysis (70 km). In addition, the effect of using driving data from a higher-resolution forecast model (15 km) is explored in the case of UrbClim. The results show that the performance of reproducing the average UHI in the simple model is generally comparable to the one in the mesoscale model when driven with reanalysis data (70 km). However, the simple model needs higher-resolution data from the forecast model (15 km) to correctly reproduce the variability of the UHI at a daily scale, which is related to the wind speed. This lack of accuracy in reproducing the wind speed, especially the sea-breeze daily cycle, which is strong in Barcelona, also causes a warm bias in the reanalysis driven UrbClim run. We conclude that medium-complexity models as UrbClim are a suitable tool to simulate the urban climate, but that they are sensitive to the ability of the input data to represent the local wind regime. UrbClim is a well suited model for impact and adaptation studies at city scale without high computing requirements, but does not replace the need for mesoscale atmospheric models when the focus is on the two-way interactions between the city and the atmosphere.

scholarly journals Spatial Superposition Method via Model Coupling for Urban Heat Island Albedo Mitigation Strategies

2012 ◽  
Vol 51 (11) ◽  
pp. 1971-1979 ◽  
Author(s):  
Humberto Silva ◽  
Jay S. Golden
Keyword(s):  
Urban Heat Island ◽  
Mesoscale Model ◽  
Coupled Model ◽  
Mitigation Strategies ◽  
Turbulence Theory ◽  
Superposition Method ◽  
Heat Island ◽  
Lumped Model ◽  
Hourly Temperature ◽  
Urban Heat

AbstractA spatial superposition design is presented that couples the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with the National Center of Excellence (NCE) lumped urban thermal model for application to the city of Phoenix, Arizona. This technique utilizes an approach similar to Reynolds decomposition from turbulence theory. The presented decomposition takes the NCE model prediction from a mitigated strategy as the mean temperature and the difference between the NCE and MM5 predictions without mitigation strategy as the perturbed temperature. The goal of this coupled model is to provide spatial variability when simulating mitigation strategies for the urban heat island effect, as compared with the spatially invariant lumped model. A validation analysis was performed incorporating a maximum 35% change from the baseline albedo value for the urban environment. It is shown that the coupled model differs by up to 0.39°C with comparable average surface temperature predictions from MM5. The coupled model was also used to perform analysis of three different albedo-driven spatial mitigation schemes. This resulted in the identification that having a lesser number of mitigated points on a square urban grid in Phoenix with the same average albedo leads to a greater reduction in average hourly temperature.

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