Abstract
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.
Highlights
The observation, monitoring, and estimation of rainfall are matters of major concern for a number of reasons
The collected data are processed while using an Artificial Neural Network (ANN) to detect rain events, and successively rain attenuation is extracted
This work aimed at developing a rainfall rate estimation algorithm, being composed of two main parts: a rain attenuation to rain rate conversion function, which receives the instantaneous rain attenuation and equivalent rain height as input, and provides the estimated time series of the rain rate as output; a methodology for estimating the equivalent rain height hR, based on the 0◦ isotherm height and on the local monthly trend of the convectivity index
Summary
The observation, monitoring, and estimation of rainfall are matters of major concern for a number of reasons. An accurate measurement of total rainfall amount is critical in climatological studies, since it represents one of the main climate indices. The knowledge of the real-time rain rate during precipitation events is useful in urban areas to estimate water runoff from the drainage system. A very high spatial and temporal variability characterizes rainfall [1,2,3]: it is difficult to obtain accurate measurements with proper spatial density and temporal detail. Each of them presents advantages and limitations [4]: for example, remote sensing measurements aim at estimating the precipitation field over wide areas, but typically with limited spatial resolution and accuracy; on the contrary, point observations, e.g., collected by rain gauges and disdrometers, deliver precise local measurements, but they hardly provide large and dense coverage
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