Sensitivity of the Simulated Heat Risk in Southeastern Europe to the RegCM Model Configurationxe2x80x94Preliminary Results The spatial distribution of the biometeorological conditions is a topic of many studies in different countries. One of the most important aspects of the weather adverse effect on the human beings is the consequences from too much exposure to the heat conditions. The human body can adapt to temperatures, but to some extent. If the air temperatures become too high, human beings at first feel uncomfortable, but the consequences can be a serious threat to health and even life. The main reasons for this threat are related to the lack of perspiration and cardiovascular problems. Atmospheric numerical models for simulating the heat stress is used in many studies. One of the most affected region in the near past, but also most likely in the future, is the Southeastern Europe, including Bulgaria. Global models are with too low resolution, but still they suggest very strong heat stress especially at the end of the 21th century. According to other studies, results from regional meteorological models suggest similar conclusions. The current research is about the heat stress conditions in the Balkan Peninsula, evaluated from tenxe2x80x93year simulations. They are performed with regional climate model RegCM. The model is run many times with different combinations of physics parameterization of some processes. The aim is to compare the heat stress simulated by different model configurations for the Balkan Peninsula and so to reveal the dependence of heat stress evaluation on the model configuration. That would answer the question of the sensitivity of the model to the parameterization schemes from a biometeorological point of view.

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.