Airborne and Terrestrial Observations of the Thermal Environment of Urban Areas Surrounding a HighRise Building during the Japanese Winter We investigated the distribution of air temperature (Ta) and the factors affecting it in lowrise areas surrounding an isolated highrise building during the Japanese winter. The study site was the central part of a regional city in Japan (36xc2xb05xe2x80xb2 N, 140xc2xb012xe2x80xb2 E), lying northeast of the Tokyo metropolitan area. The daytime surface temperature (Ts) in the shade is generally considered to be comparable to Ta; however, according to airborne remote sensing conducted in December 2009 where a multispectral scanner was installed on a fixedwing aircraft, Ts for pavements in the shade of a highrise building was significantly lower than Ta of suburban areas, indicating an influence of cold storage on Ts. Then, we conducted mobile observations using instruments (thermocouple, four component radiometer, and so on) installed on a bicycle in January 2016 to investigate the detailed distribution of Ta and the factors affecting it. The results showed the Ta over the pavements in the shade of the highrise building was lower than the Ta of sunlit areas in the same urban area by xe2x88x922 xc2xb0C and lower than the Ta of suburban areas by xe2x88x921xe2x80x931.5 xc2xb0C, although the advection effect was large due to strong winds around the building. In conclusion, a locally lower Ta compared to the surrounding areas can develop during the day in winter, even in spaces that are open to areas beyond the canopy.

Estimating spatiotemporal air temperature in London (UK) using machine learning and earth observation satellite data Abstract   Urbanisation generates greater population densities and an increase in anthropogenic heat generation. These factors elevate the urbanxe2x80x93rural air temperature (Ta) difference, thus generating the Urban Heat Island (UHI) phenomenon. Ta is used in the fields of public health and epidemiology to quantify deaths attributable to heat in cities around the world: the presence of UHI can exacerbate exposure to high temperatures during summer periods, thereby increasing the risk of heatrelated mortality. Measuring and monitoring the spatial patterns of Ta in urban contexts is challenging due to the lack of a good network of weather stations. This study aims to produce a parsimonious model to retrieve maximum Ta (Tmax) at high spatiotemporal resolution using Earth Observation (EO) satellite data. The novelty of this work is twofold: (i) it will produce daily estimations of Tmax for London at 1 km2 during the summertime between 2006 and 2017 using advanced statistical techniques and satellitederived predictors, and (ii) it will investigate for the first time the predictive power of the gradient boosting algorithm to estimate Tmax for an urban area. In this work, 6 regression models were calibrated with 6 satellite products, 3 geospatial features, and 29 meteorological stations. Stepwise linear regression was applied to create 9 groups of predictors, which were trained and tested on each regression method. This study demonstrates the potential of machine learning algorithms to predict Tmax: the gradient boosting model with a group of five predictors (land surface temperature, Julian day, normalised difference vegetation index, digital elevation model, solar zenith angle) was the regression model with the best performance (Rxc2xb2  0.68, MAE  1.60 xc2xb0C, and RMSE  2.03 xc2xb0C). This methodological approach is capable of being replicated in other UK cities, benefiting national heatrelated mortality assessments since the data (provided by NASA and the UK Met Office) and programming languages (Python) sources are free and open. This study provides a framework to produce a high spatiotemporal resolution of Tmax, assisting public health researchers to improve the estimation of mortality attributable to high temperatures. In addition, the research contributes to practice and policymaking by enhancing the understanding of the locations where mortality rates may increase due to heat. Therefore, it enables a more informed decisionmaking process towards the prioritisation of actions to mitigate heatrelated mortality amongst the vulnerable population.