A novel methodology for estimating rainfall rate from satellite signals is presented. The proposed inversion algorithm yields rain rate estimates by making opportunistic use of the downlink signal and exploiting local ancillary meteorological information (0 °C isotherm height and monthly convectivity index), which can be extracted on a Global basis from Numerical Weather Prediction (NWP) products. The methodology includes different expressions to take the different impact of stratiform and convective rain events on the link into due account. The model accuracy in predicting the rain rate is assessed (and compared to the one of other models), both on a statistical and on an instantaneous basis, by exploiting a full year of data collected in Milan, in the framework of the Alphasat Aldo Paraboni propagation experiment.
This contribution presents a comprehensive methodology for the real-time estimation of the rain intensity from downlink satellite signals. The enhanced system leverages on Extremely Randomized Trees Classifiers to automatically perform rainfall detection along Earth-satellite links and successively employs an improved procedure to determine the corresponding slant-path rain attenuation. The latter quantity is then exploited to yield realtime rainfall rate estimates with 1-minute time resolution. The accuracy of the proposed methodology is tested using Ka and Q band propagation data, collected in two different sites (Milan and Madrid) and in the framework of the propagation experiments. The results demonstrate the reliability of the automated rain event detector, as well as a satisfactory accuracy in estimating the slant-path rain attenuation and the point rainfall rate. The accuracy is assessed both on a statistical and on an instantaneous basis through the evaluation of different error figures and by inspection of individual time series.
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