Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney

• Published in: The Science of the total environment 2020-03, Vol.709, p.136068-136068, Article 136068
• Main Authors: Yun, Geun Young, Ngarambe, Jack, Duhirwe, Patrick Nzivugira, Ulpiani, Giulia, Paolini, Riccardo, Haddad, Shamila, Vasilakopoulou, Konstantina, Santamouris, Mat
• Format: Article
• Language: English
• Subjects: Desert winds, A.I. forecasting models; LSTM; Regional climate change; Sea breeze; Synoptic conditions; Urban heat island
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2019.136068

The urban heat island is a vastly documented climatological phenomenon, but when it comes to coastal cities, close to desert areas, its analysis becomes extremely challenging, given the high temporal variability and spatial heterogeneity. The strong dependency on the synoptic weather conditions, rather than on city-specific, constant features, hinders the identification of recurrent patterns, leading conventional predicting algorithms to fail. In this paper, an advanced artificial intelligence technique based on long short-term memory (LSTM) model is applied to gain insight and predict the highly fluctuating heat island intensity (UHII) in the city of Sydney, Australia, governed by the dualistic system of cool sea breeze from the ocean and hot western winds from the vast desert biome inlands. Hourly measurements of temperature, collected for a period of 18 years (1999–2017) from 8 different sites in a 50 km radius from the coastline, were used to train (80%) and test (20%) the model. Other inputs included date, time, and previously computed UHII, feedbacked to the model with an optimized time step of six hours. A second set of models integrated wind speed at the reference station to account for the sea breeze effect. The R2 ranged between 0.770 and 0.932 for the training dataset and between 0.841 and 0.924 for the testing dataset, with the best performance attained right in correspondence of the city hot spots. Unexpectedly, very little benefit (0.06–0.43%) was achieved by including the sea breeze among the input variables. Overall, this study is insightful of a rather rare climatological case at the watershed between maritime and desertic typicality. We proved that accurate UHII predictions can be achieved by learning from long-term air temperature records, provided that an appropriate predicting architecture is utilized. [Display omitted] •Predicting the urban heat island in coastal cities close to deserts is challenging.•The city of Sydney is selected as representative and uniquely complex case study.•A long short-term memory forecast model is developed based on 18-year data.•R2 between 0.841 and 0.924 is achieved, with the best performance at the city hot spots.•Negligible benefit is attained by including sea breeze among the inputs.