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The current application of local climate zones (LCZs) often ends with (inter)zonal comparation of land surface temperature (LST) or air temperature (AT). LST evaluation employs an enhanced concept of LCZs together with cluster analysis for LCZs grouped based on LST. The paper attempts to combine them into a complex approach derived from the case study on a medium-sized Central European city (Hradec Králové, the Czech Republic). In particular, the paper addresses the following. (i) The relation of LST and AT, when the daily course of temperature profile ranging clear off the surface up to 2 m was fitted by a rational 2D function. The obtained equation enables derivation of the AT from LST and vice versa. (ii) The differences in thermal response of LCZs based on LST or AT, where the highest average LST and average maximum LST show LCZs 10, 2, 3 and 8, i.e., with a significant proportion of artificial surfaces. The cluster of LCZs with a significant representation of vegetation, LCZs 9, B, D, A and G, have significantly lower LST. (iii) The contribution of LCZs to understanding of LST/AT relation and whether their specific relation could be expected in particular LCZs, when subsequent interaction assessment of LST and AT revealed statistically their significant correlation in LCZs for certain cases. [ABSTRACT FROM AUTHOR]

Intense convective storms usually produce large rainfall volumes in short time periods, increasing the risk of floods and causing damages to population, buildings, and infrastructures. In this paper, we propose a framework to couple visual and statistical analyses of convective thunderstorms at the basin scale, considering both the spatial and temporal dimensions of the process. The dataset analyzed in this paper contains intense convective events that occurred in seven years (2012–2018) in the Seveso-Olona-Lambro basin (North of Italy). The data has been acquired by MeteoSwiss using the Thunderstorm Radar Tracking (TRT) algorithm. The results show that the most favorable conditions for the formation of convective events occur in the early afternoon and during summertime, confirming the key role of the temperature in atmospheric convection. The orography emerged as a driver for convection, which takes place more frequently in mountain areas. The storm paths analysis shows that the predominant direction is from South-West to North-East. Considering storm duration, long-lasting events reach higher values of radar reflectivity and cover more extended areas than short-lasting ones. The results obtained can be exploited for many practical applications including nowcasting, alert systems, and sensors deployment. [ABSTRACT FROM AUTHOR]

Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machines and Random Forest, are compared with Multiple Linear Regression and Ordinary kriging. Several geographic, remote sensing and time variables are used as predictors. The validation is carried out using two different approaches, a leave-one-out cross validation in the spatial domain and a spatio-temporal k-block cross-validation, and four different statistics on a daily basis, allowing the use of ANOVA to compare the results. The main conclusion is that Random Forest produces the best results (R2 = 0.888 ± 0.026, Root mean square error = 3.01 ± 0.325 using k-block cross-validation). Regression methods (Support Vector Machine, Random Forest and Multiple Linear Regression) are calibrated with MODIS data and several predictors easily calculated from a Digital Elevation Model. The most important variables in the Random Forest model were satellite temperature, potential irradiation and cdayt, a cosine transformation of the julian day. [ABSTRACT FROM AUTHOR]