Sensitivity of WRF Model to Urban Parameterizations, With Applications to Chicago Metropolitan Urban Heat Island

2014 ◽  
Author(s):  
Ashish Sharma ◽  
Harindra J. S. Fernando ◽  
Jessica Hellmann ◽  
Fei Chen
Keyword(s):  
Urban Heat Island ◽  
Metropolitan Area ◽  
Agricultural Land ◽  
Wrf Model ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Natural Habitats ◽  
Chicago Metropolitan Area ◽  
Urban Heat ◽  
Urban Parameterizations

Chicago is one of the most populated cites of US. It is located next to a freshwater source, Lake Michigan, and surrounded by productive agricultural land and diverse natural habitats. This study explores the sensitivity of mesoscale urban heat island (UHI) simulations to urban parameterizations, focusing on the Chicago metropolitan area (CMA) and its environs. For this purpose, a series of climate downscaling experiments using the Weather Research and Forecasting (WRF) model at 1 km horizontal resolution. A typical summer hot day in Chicago was considered, which is imitative of a summer day in the late 21st century. This study utilizes National Land Cover Database (NLCD) 2006 classifications to test UHI sensitivity for CMA. Among different urban parameterization schemes, BEP+BEM best reproduces the urban surface temperatures in comparison to other urban schemes. Results show that UHI is more pronounced with BEP and BEP+BEM schemes due to explicit accounting of anthropogenic heat (AH). The study also investigates the effects of urbanization on regional climate by replacing Chicago metropolitan area by agricultural landscape, which yielded increased surface wind speeds due to reduced mechanical and thermal resistance.

scholarly journals Impact of a Detailed Urban Parameterization on Modeling the Urban Heat Island in Beijing Using TEB-RAMS

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Lei Jiang ◽  
Lixin Lu ◽  
Lingmei Jiang ◽  
Yuanyuan Qi ◽  
Aqiang Yang
Keyword(s):  
Land Use ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Metropolitan Area ◽  
Atmospheric Modeling ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Urban Heat ◽  
Four Levels ◽  
Geometric Morphology

The Town Energy Budget (TEB) model coupled with the Regional Atmospheric Modeling System (RAMS) is applied to simulate the Urban Heat Island (UHI) phenomenon in the metropolitan area of Beijing. This new model with complex and detailed surface conditions, called TEB-RAMS, is from Colorado State University (CSU) and the ASTER division of Mission Research Corporation. The spatial-temporal distributions of daily mean 2 m air temperature are simulated by TEB-RAMS during the period from 0000 UTC 01 to 0000 UTC 02 July 2003 over the area of 116°E~116.8°E, 39.6°N~40.2°N in Beijing. The TEB-RAMS was run with four levels of two-way nested grids, and the finest grid is at 1 km grid increment. An Anthropogenic Heat (AH) source is introduced into TEB-RAMS. A comparison between the Land Ecosystem-Atmosphere Feedback model (LEAF) and the detailed TEB parameterization scheme is presented. The daily variations and spatial distribution of the 2 m air temperature agree well with the observations of the Beijing area. The daily mean 2 m air temperature simulated by TEB-RAMS with the AH source is 0.6 K higher than that without specifying TEB and AH over the metropolitan area of Beijing. The presence of urban underlying surfaces plays an important role in the UHI formation. The geometric morphology of an urban area characterized by road, roof, and wall also seems to have notable effects on the UHI intensity. Furthermore, the land-use dataset from USGS is replaced in the model by a new land-use map for the year 2010 which is produced by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS). The simulated regional mean 2 m air temperature is 0.68 K higher from 01 to 02 July 2003 with the new land cover map.

GIS-BASED SPATIAL ANALYSIS OF URBAN HEAT ISLAND IN FUKUOKA METROPOLITAN AREA

2018 ◽  
Vol 74 (5) ◽  
pp. I_1189-I_1194
Author(s):  
Yukun WANG ◽  
Akiko NISHIMURA ◽  
Yuji SUGIHARA ◽  
Guoyun ZHOU ◽  
Yukiko HISADA ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Urban Heat Island ◽  
Metropolitan Area ◽  
Heat Island ◽  
Urban Heat

scholarly journals Quantitative Analysis of Factors Contributing to Urban Heat Island Intensity

2012 ◽  
Vol 51 (5) ◽  
pp. 842-854 ◽  
Author(s):  
Young-Hee Ryu ◽  
Jong-Jin Baik
Keyword(s):  
Urban Heat Island ◽  
Single Layer ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Impervious Surfaces ◽  
Additional Heat ◽  
Urban Geometry ◽  
Causative Factors ◽  
Urban Heat ◽  
Vertical Walls

AbstractThis study identifies causative factors of the urban heat island (UHI) and quantifies their relative contributions to the daytime and nighttime UHI intensities using a mesoscale atmospheric model that includes a single-layer urban canopy model. A midlatitude city and summertime conditions are considered. Three main causative factors are identified: anthropogenic heat, impervious surfaces, and three-dimensional (3D) urban geometry. Furthermore, the 3D urban geometry factor is subdivided into three subfactors: additional heat stored in vertical walls, radiation trapping, and wind speed reduction. To separate the contributions of the factors and interactions between the factors, a factor separation analysis is performed. In the daytime, the impervious surfaces contribute most to the UHI intensity. The anthropogenic heat contributes positively to the UHI intensity, whereas the 3D urban geometry contributes negatively. In the nighttime, the anthropogenic heat itself contributes most to the UHI intensity, although it interacts strongly with other factors. The factor that contributes the second most is the impervious-surfaces factor. The 3D urban geometry contributes positively to the nighttime UHI intensity. Among the 3D urban geometry subfactors, the additional heat stored in vertical walls contributes most to both the daytime and nighttime UHI intensities. Extensive sensitivity experiments to anthropogenic heat intensity and urban surface parameters show that the relative importance and ranking order of the contributions are similar to those in the control experiment.

scholarly journals The Urban Heat Island of the North-Central Texas Region and Its Relation to the 2011 Severe Texas Drought

2013 ◽  
Vol 52 (11) ◽  
pp. 2418-2433 ◽  
Author(s):  
A. M. E. Winguth ◽  
B. Kelp
Keyword(s):  
Urban Heat Island ◽  
Metropolitan Area ◽  
Remotely Sensed ◽  
Severe Drought ◽  
Positive Trend ◽  
Fort Worth ◽  
Heat Island ◽  
Rapid Urbanization ◽  
Maximum Heat ◽  
Urban Heat

AbstractHourly surface temperature differences between Dallas–Fort Worth, Texas, metropolitan and rural sites have been used to calculate the urban heat island from 2001 to 2011. The heat island peaked after sunset and was particularly strong during the drought and heat wave in July 2011, reaching a single-day instantaneous maximum value of 5.4°C and a monthly mean maximum of 3.4°C, as compared with the 2001–11 July average of 2.4°C. This severe drought caused faster warming of rural locations relative to the metropolitan area in the morning as a result of lower soil moisture content, which led to an average negative heat island in July 2011 of −2.3°C at 1100 central standard time. The ground-based assessment of canopy air temperature at screening level has been supported by a remotely sensed surface estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite, highlighting a dual-peak maximum heat island in the major city centers of Dallas and Fort Worth. Both ground-based and remotely sensed spatial analyses of the maximum heat island indicate a northwest shift, the result of southeast winds in July 2011 of ~2 m s−1 on average. There was an overall positive trend in the urban heat island of 0.14°C decade−1 in the Dallas–Fort Worth metropolitan area from 2001 to 2011, due to rapid urbanization. Superimposed on this trend are significant interannual and decadal variations that influence the urban climate.

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

scholarly journals Assessment of Changes in Land Use/Land Cover and Land Surface Temperatures and Their Impact on Surface Urban Heat Island Phenomena in the Kathmandu Valley (1988–2018)

2020 ◽  
Vol 9 (12) ◽  
pp. 726
Author(s):  
Md. Omar Sarif ◽  
Bhagawat Rimal ◽  
Nigel E. Stork
Keyword(s):  
Urban Heat Island ◽  
Land Surface ◽  
Agricultural Land ◽  
Kathmandu Valley ◽  
Heat Island ◽  
City Center ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
The City ◽  
Normal Difference

More than half of the world’s populations now live in rapidly expanding urban and its surrounding areas. The consequences for Land Use/Land Cover (LULC) dynamics and Surface Urban Heat Island (SUHI) phenomena are poorly understood for many new cities. We explore this issue and their inter-relationship in the Kathmandu Valley, an area of roughly 694 km2, at decadal intervals using April (summer) Landsat images of 1988, 1998, 2008, and 2018. LULC assessment was made using the Support Vector Machine algorithm. In the Kathmandu Valley, most land is either natural vegetation or agricultural land but in the study period there was a rapid expansion of impervious surfaces in urban areas. Impervious surfaces (IL) grew by 113.44 km2 (16.34% of total area), natural vegetation (VL) by 6.07 km2 (0.87% of total area), resulting in the loss of 118.29 km2 area from agricultural land (17.03% of total area) during 1988–2018. At the same time, the average land surface temperature (LST) increased by nearly 5–7 °C in the city and nearly 3–5 °C at the city boundary. For different LULC classes, the highest mean LST increase during 1988–2018 was 7.11 °C for IL with the lowest being 3.18 °C for VL although there were some fluctuations during this time period. While open land only occupies a small proportion of the landscape, it usually had higher mean LST than all other LULC classes. There was a negative relationship both between LST and Normal Difference Vegetation Index (NDVI) and LST and Normal Difference Moisture Index (NDMI), respectively, and a positive relationship between LST and Normal Difference Built-up Index (NDBI). The result of an urban–rural gradient analysis showed there was sharp decrease of mean LST from the city center outwards to about 15 kms because the NDVI also sharply increased, especially in 2008 and 2018, which clearly shows a surface urban heat island effect. Further from the city center, around 20–25 kms, mean LST increased due to increased agriculture activity. The population of Kathmandu Valley was 2.88 million in 2016 and if the growth trend continues then it is predicted to reach 3.85 million by 2035. Consequently, to avoid the critical effects of increasing SUHI in Kathmandu it is essential to improve urban planning including the implementation of green city technologies.

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