AN INVESTIGATION OF REMOTELY SENSED URBAN HEAT ISLAND CLIMATOLOGY

Satellite remotely sensed temperatures are widely used for urban heat island (UHI) studies. However, the abilities of satellite surface and atmospheric data to assess the climatology of UHI face many unknowns and challenges. This research addresses the problems and potential for satellite remotely sensed UHI climatology by examining three different issues. The first issue is related to the temporal aggregation of land surface temperature (LST) and the potential biases that are induced on the surface UHI (SUHI) intensity. Composite LST data usually are preferred to avoid the missing values due to clouds for long-term UHI monitoring. The impact of temporal aggregation shows that SUHI intensities are more notably enhanced in the daytime than nighttime with an increasing trend for larger composite periods. The cause of the biases is highly related to the amount and distribution of clouds. The second issue is related to model validation and the appropriate use of LST for comparison to modeled radiometric temperatures in the urban environment. Sensor view angle, cloud distribution, and cloud contaminated pixels can confound comparisons between satellite LST and modeled surface radiometric temperature. Three practical sampling methods to minimize the confounding factors are proposed and evaluated for validating different aspects of model performance. The third issue investigated is to assess to what extent remotely sensed atmospheric profiles collected over the urban environment can be used to examine the UHI. The remotely sensed air and dew-point temperatures are compared with the ground observations, showing an ability to capture the temporal and spatial dynamics of atmospheric UHI at a fine scale. Finally, a new metric for quantifying the urban heat island is proposed. The urban heat island curve (UHIC), is developed to represent UHI intensity by integrating the urban surface heterogeneity in a curve. UHIC illustrates the relationship between the air temperature and the urban fractions, and emphasizes the temperature gradients, consequently decreasing the impact of the data biases. This research illustrates the potential for satellite data to monitor and increase our understanding of UHI climatology

Observed urban heat island characteristics in Akure, Nigeria

A climatological analysis of the differences in air temperature between rural and urban areas (Tu-r) corroborates the existence of an urban heat island (UHI) in Akure (7º 25’ N, 5º 20’ E), a tropical city in the south western part of Nigeria. The investigations which have been conducted out of a year-long experiment from fixed point observations focuses on the description of the climatology of urban canopy heat island in the Akure and the analysis of the results were presented. The results show that the nocturnal heat island was more frequent than the daytime heat island as it exists from less intense to higher intensity categories throughout the study period. Nocturnal heat Island intensity was observed to be stronger during the dry season. Although of lower intensity, daytime heat Island exists throughout the day except for few hours in the months of November and December that exhibits a reverse thermal contrast. The daytime heat island is observed to be intense in the wet months than the dry months, which may be caused by the evaporative cooling of wet surfaces. On the average, the urban/ rural thermal differences are positive, varying from 4°C at nocturnal hours during dry months to an approximate of 2°C around noon during wet months. This paper explain the aspects of heat islands and their relation to other causative agents such as the sky view factor (SVF) and also discusses its potential impact on energy demand.Key words: Urban heat island, sky view factor, energy demand

How can we use MODIS land surface temperature to validate long-term urban model simulations?

This is the authors accepted manuscript. The published version is available here: http://dx.doi.org/10.1002/2013JD021101.High spatial resolution urban climate modeling is essential for understanding urban climatology and predicting the human health impacts under climate change. Satellite thermal remote-sensing data are potential observational sources for urban climate model validation with comparable spatial scales, temporal consistency, broad coverage, and long-term archives. However, sensor view angle, cloud distribution, and cloud-contaminated pixels can confound comparisons between satellite land surface temperature (LST) and modeled surface radiometric temperature. The impacts of sensor view angles on urban LST values are investigated and addressed. Three methods to minimize the confounding factors of clouds are proposed and evaluated using 10years of Moderate Resolution Imaging Spectroradiometer (MODIS) data and simulations from the High-Resolution Land Data Assimilation System (HRLDAS) over Greater Houston, Texas, U.S. For the satellite cloud mask (SCM) method, prior to comparison, the cloud mask for each MODIS scene is applied to its concurrent HRLDAS simulation. For the max/min temperature (MMT) method, the 50 warmest days and coolest nights for each data set are selected and compared to avoid cloud impacts. For the high clear-sky fraction (HCF) method, only those MODIS scenes that have a high percentage of clear-sky pixels are compared. The SCM method is recommended for validation of long-term simulations because it provides the largest sample size as well as temporal consistency with the simulations. The MMT method is best for comparison at the extremes. And the HCF method gives the best absolute temperature comparison due to the spatial and temporal consistency between simulations and observations.Funded by National Aeronautics and Space Administration. Grant Number: (NNX10AK79G

Sources of Atmospheric Fine Particles and Adsorbed Polycyclic Aromatic Hydrocarbons in Syracuse, New York

Land surface temperature (LST) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor have been widely utilized across scientific disciplines for a variety of purposes. The goal of this dissertation was to utilize MODIS LST for three spatial modeling applications within the conterminous United States (CONUS). These topics broadly encompassed agriculture and human health. The first manuscript compared the performance of all methods previously used to interpolate missing values in 8-day MODIS LST images. At low cloud cover (\u3c30%), the Spline spatial method outperformed all of the temporal and spatiotemporal methods by a wide margin, with median absolute errors (MAEs) ranging from 0.2°C-0.6°C. However, the Weiss spatiotemporal method generally performed best at greater cloud cover, with MAEs ranging from 0.3°C-1.2°C. Considering the distribution of cloud contamination and difficulty of implementing Weiss, using Spline under all conditions for simplicity would be sufficient. The second manuscript compared the corn yield predictive capability across the US Corn Belt of a novel killing degree day metric (LST KDD), computed with daily MODIS LST, and a traditional air temperature-based metric (Tair KDD). LST KDD was capable of predicting annual corn yield with considerably less error than Tair KDD (R2 /RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). The superior performance can be attributed to LST’s ability to better reflect evaporative cooling and water stress. Moreover, these findings suggest that long-term yield projections based on Tair and precipitation alone will contain error, especially for years of extreme drought. Finally, the third manuscript assessed the extent to which daily maximum heat index (HI) across the CONUS can be estimated by MODIS multispectral imagery in conjunction with land cover, topographic, and locational factors. The derived model was capable of estimating HI in 2012 with an acceptable level of error (R 2 = 0.83, RMSE = 4.4°F). LST and water vapor (WV) were, by far, the most important variables for estimation. Expanding this analytical framework to a more extensive study area (both temporally and spatially) would further validate these findings. Moreover, identifying an appropriate interpolation and downscaling approach for daily MODIS imagery would substantially increase the utility of the corn yield and HI models

A WRF-UCM-SOLWEIG framework of 10m resolution to quantify the intra-day impact of urban features on thermal comfort

City-scale outdoor thermal comfort diagnostics are essential for understanding actual heat stress. However, previous research primarily focused on the street scale. Here, we present the WRF-UCM-SOLWEIG framework to achieve fine-grained thermal comfort mapping at the city scale. The background climate condition affecting thermal comfort is simulated by the Weather Research and Forecasting (WRF) model coupled with the urban canopy model (UCM) at a local-scale (500m). The most dominant factor, mean radiant temperature, is simulated using the Solar and Longwave Environmental Irradiance Geometry (SOLWEIG) model at the micro-scale (10m). The Universal Thermal Climate Index (UTCI) is calculated based on the mean radiant temperature and local climate parameters. The influence of different ground surface materials, buildings, and tree canopies is simulated in the SOLWEIG model using integrated urban morphological data. We applied this proposed framework to the city of Guangzhou, China, and investigated the intra-day variation in the impact of urban morphology during a heat wave period. Through statistical analysis, we found that the elevation in UTCI is primarily attributed to the increase in the fraction of impervious surface (ISF) during daytime, with a maximum correlation coefficient of 0.80. Tree canopy cover has a persistent cooling effect during the day. Implementing 40% of tree cover can reduce the daytime UTCI by 1.5 to 2.0 K. At nighttime, all urban features have a negligible contribution to outdoor thermal comfort. Overall, the established framework provides essential input data and references for studies and urban planners in the practice of urban (micro)climate diagnostics and planning

A GIS extension model to calculate urban heat island intensity based on urban geometry

This paper presents a simulation model, which was incorporated into a Geographic Information System (GIS), in order to calculate the maximum intensity of urban heat islands based on urban geometry data. The method-ology of this study stands on a theoretical-numerical basis (Okeâ s model), followed by the study and selection of existing GIS tools, the design of the calculation model, the incorporation of the resulting algorithm into the GIS platform and the application of the tool, developed as exemplification. The developed tool will help researchers to simulate UHI in different urban scenarios.CAPES -Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Climate Change and Environmental Sustainability- Volume 5

This volume of Climate Change and Environmental Sustainability covers topics on greenhouse gas emissions, climatic impacts, climate models and prediction, and analytical methods. Issues related to two major greenhouse gas emissions, namely of carbon dioxide and methane, particularly in wetlands and agriculture sector, and radiative energy flux variations along with cloudiness are explored in this volume. Further, climate change impacts such as rainfall, heavy lake-effect snowfall, extreme temperature, impacts on grassland phenology, impacts on wind and wave energy, and heat island effects are explored. A major focus of this volume is on climate models that are of significance to projection and to visualise future climate pathways and possible impacts and vulnerabilities. Such models are widely used by scientists and for the generation of mitigation and adaptation scenarios. However, dealing with uncertainties has always been a critical issue in climate modelling. Therefore, methods are explored for improving climate projection accuracy through addressing the stochastic properties of the distributions of climate variables, addressing variational problems with unknown weights, and improving grid resolution in climatic models. Results reported in this book are conducive to a better understanding of global warming mechanisms, climate-induced impacts, and forecasting models. We expect the book to benefit decision makers, practitioners, and researchers in different fields and contribute to climate change adaptation and mitigation