A physically-based passive microwave technique is proposed to estimate precipitation intensity and extinction from ground. Multi-frequency radiometric measurements are inverted to retrieve surface rain rate, columnar precipitation contents and rainfall microwave extinction. A new inversion methodology, based on an artificial neurat-network feed-forward algorithm, is evaluated and compared against a previously developed regression technique. Both retrieval techniques are trained by numerical simulations of a radiative transfer model applied to microphysically-consistent precipitating cloud structures. Cloud microphysics is characterized by using parameterized hydrometeor drop size distribution, spherical particle shape and dielectric composition. The radiative transfer equation is solved for plane-parallel seven-layer structures, including liquid, melted, and ice spherical hydrometeors. The proposed neural-network inversion technique is tested and compared with the regression algorithm on synthetic data in order to understand their potential and to select the best frequency set for rainfall rate, columnar contents and extinction estimation. Available ground-based radiometric measurements at 13.0, 23.8, and 31.6 GHz are used for experimentally testing and comparing the neural-network retrieval algorithm. Comparison with rain gauge data and rain extinction measurements, derived from three satellite beacon channels at 18.7, 39.6, and 49.5 GHz acquired at Pomezia (Rome, Italy), are performed and discussed for a selected case of light-to-moderate rainfall. (c) 2005 Elsevier B.V. All rights reserved.

Neural-network approach to ground-based passive microwave estimation of precipitation intensity and extinction

CIOTTI, PIERO
2006

Abstract

A physically-based passive microwave technique is proposed to estimate precipitation intensity and extinction from ground. Multi-frequency radiometric measurements are inverted to retrieve surface rain rate, columnar precipitation contents and rainfall microwave extinction. A new inversion methodology, based on an artificial neurat-network feed-forward algorithm, is evaluated and compared against a previously developed regression technique. Both retrieval techniques are trained by numerical simulations of a radiative transfer model applied to microphysically-consistent precipitating cloud structures. Cloud microphysics is characterized by using parameterized hydrometeor drop size distribution, spherical particle shape and dielectric composition. The radiative transfer equation is solved for plane-parallel seven-layer structures, including liquid, melted, and ice spherical hydrometeors. The proposed neural-network inversion technique is tested and compared with the regression algorithm on synthetic data in order to understand their potential and to select the best frequency set for rainfall rate, columnar contents and extinction estimation. Available ground-based radiometric measurements at 13.0, 23.8, and 31.6 GHz are used for experimentally testing and comparing the neural-network retrieval algorithm. Comparison with rain gauge data and rain extinction measurements, derived from three satellite beacon channels at 18.7, 39.6, and 49.5 GHz acquired at Pomezia (Rome, Italy), are performed and discussed for a selected case of light-to-moderate rainfall. (c) 2005 Elsevier B.V. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/11642
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