#EESpublishes: @GC_CUNY @CityCollegeNY Alumna Dr @mar_karimi & Prof #RezaKhanbilvardi on Surface T Variations in Urban Settings

#EESpublishes: @GC_CUNY @CityCollegeNY Alumna Dr @mar_karimi & Prof #RezaKhanbilvardi on Surface T Variations in Urban Settings

Dr. Karimi and Dr. Khanbilvardi coauthored a paper entitled Predicting surface temperature variation in urban settings using real-time weather forecasts in Urban Climate.  Highlight include:

•Three months of field campaign data were collected to understand the inverse effect of UHI in Manhattan
•Measuring spatial and temporal temperature variation within urban setting of Manhattan
•Predicting temperature variability from weather forecast
•The lapse rates being the common dependent for both spatial and temporal variations Within Manhattan

Abstract: Densely populated cities experience adverse effects of Urban Heat Island (UHI) including higher numbers of emergency hospital admissions and heat related illnesses. Studying UHI effects and temperature variations has become even more important as global temperatures continue to rise. To better understand UHIs within New York City, an exploratory study was done using a field campaign to measure high resolution spatial and temporal temperature variations within Manhattan’s urban setting. These time correlated temperature measurements along with weather model data of temperature and relative humidity were used to predict temperature variability using weather forecasts. The amplitude of spatial variations was most dependent on temperature (r = 0.400) and low level lapse rate (r = − 0.258) while temporal variations were most dependent on temperature (r = 0.398), low level lapse rates (r = − 0.361), and mid-level lapse rate (r = − 0.320). Regression of weather variables can be used to predict the amplitude of spatial and temporal variation in temperature within a city for each day. This study directs attention towards high resolution near-surface air temperature analysis and offers a new look at surface thermal properties. The application of the resulting data and modeling is most suitable for forecasting microscale variability in urban settings.