Urban climate modeling : case study of Milan city

Urbanization is widely becoming an important issue in recent years due to its effects on urban climate. It results in climate change and mainly urban heat island (UHI) phenomenon, which is defined as the difference between urban and rural temperature. Due to lack of information in terms of forms and thermal characteristics of cities, several researches related to urban climate and UHI measurements have been done. A crucial point in analysis of UHI is to divide the city into urban-rural zones. Accordingly, to simplify UHI measurement, a climate-based classification system called Local Climate Zone (LCZ) has been developed, which classifies the area based on physical and thermal surface properties. The main objective of this thesis is to evaluate the climate of Milan by creating a map of LCZ classification, and using this map in an energy balance model called Local Scale Urban Parameterization Scheme (LUMPS) to simulate surface heat fluxes. LCZ map is obtained using remote sensing data (Landsat 8, Sentinel 2, and Aster) in a pixel-based supervised classification. In our project, we used “random forest” algorithm as the classifier. The methodology and procedures to derive LCZ map have been presented in this thesis, and the overall accuracy of the classification for each satellite image was performed using GRASS (an open source GIS software). Energy budgets achieved using surface cover fractions (from LCZ map) and meteorological data. The results obtained from LUMPS, shows the difference in heat fluxes for various LCZ type. Moreover, hourly average seasonal variation of energy budgets is analyzed in this thesis. Overall, this research proves that surface cover properties directly affect the urban climate. The result of this work is expected to have broad utility for urban climate scientists, especially in weather forecasting and heat mitigation strategies.