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Rainfall intensity estimation from weather radar is still significantly uncertain, due to local anomalies, radar beam attenuation, inappropriate calibration of the radar reflectivity factor (Z) to rainfall rate (R) relationship, and sampling errors. The aim of this work is to revise the use of the power-law equation commonly adopted to relate radar reflectivity and rainfall rate to increase the estimation quality in the presence of intense rainfall rates. We introduce a quasi real-time procedure for an adaptive in space and time estimation of the Z-R relation. The procedure is applied in a comprehensive case study, which includes 16 severe rainfall events in the north-west of Italy. The study demonstrates that the technique outperforms the classical estimation methods for most of the analysed events. The determination coefficient improves by up to 30% and the bias values for stratiform events decreases by up to 80% of the values obtained with the classical, non-adaptive, Z-R relations. The proposed procedure therefore shows significant potential for operational uses.

Confounding factors like urbanization and land-use change could introduce uncertainty to the estimation of global temperature trends related to climate change. In this work, we introduce a new way to investigate the nexus between temporal trends of temperature and urbanization data at the global scale in the period from 1992 to 2013. We analyze air temperature data recorded from more than 5000 weather stations worldwide and nightlight satellite measurements as a proxy for urbanization. By means of a range of statistical methods, our results quantify and outline that the temporal evolution of urbanization affects temperature trends at multiple spatial scales with significant differences at regional and continental scales. A statistically significant agreement in temperature and nightlight trends is detected, especially in low and middle-income regions, where urbanization is rapidly growing. Conversely, in continents such as Europe and North America, increases in temperature trends are typically detected along with non-significant nightlight trends.