Effects on Local Temperature and Energy of Oasis City Expansion in Arid Area of Northwest China The land surface model SURFEX 7.3 was used to study climate effect of urban expansion located in oasis in arid area of Northwest China by surface and 2xe2x80x89m urban heat island (UHI) intensity and available energy ratio (      ).  We performed a true regional development scenario and three assumed scenario simulations in 1978, 1993, 2004, and 2014, respectively. The results show that 2xe2x80x89m UHI always displays positive twin peaks during whole day, while surface UHI only displays a positive single peak with several hours during daytime at four seasons in the four years. Moreover, 2xe2x80x89m UHI intensity during night is higher than that during daytime, indicating that UHI intensity is contributed more by xe2x80x9ctrap effectxe2x80x9d from urban complex geometry or anthropogenic heat and that surface UHI according to land surface temperature cannot reflect UHI comprehensively. The oasisurban development resulted in local warming and increasing of       ,  and compared with the original undeveloped environment, local climate in the study area was in a relatively balanced state in 1978 and 1993 due to the xe2x80x9cheating effectxe2x80x9d of urban area and the xe2x80x9ccooling effectxe2x80x9d of oasis, but the offsetting effect from oasis would become weaker after1993.

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.