The paper addresses the problem of the reconstruction of the rainfall field using weather radar observables. It is well known that at the C band and especially at the X band the reconstruction of the rainfall rate profile along the range using absolute (Z(H)) and differential (Z(DR)) reflectivity measurements is significantly affected by the attenuation coefficients (i.e. alpha(H) and alpha(D)). This problem has been long and extensively studied and iterative attenuation correction techniques based on a cumulative procedure were developed, in which the attenuation at n(th) cell is estimated using the attenuation corrected reflectivity values at previous cell. Usually the attenuation coefficients (alpha(H), alpha(D)) are estimated using non linear parametrizations with (Z(H), Z(DR)), or, if phase measurements are available, using linear parametrizations with the specific differential phase shift K-DP In this work novel approaches based on neural networks (N.N.) have been used.First, to estimate (alpha(H), alpha(D)) from Z(H), Z(DR) and K-DP; the N.N. estimators have shown better performance (often, slightly better) in comparison to the best ones known.Second, N.N. have been implemented to extract the range rainfall rate profile. The input to the network is a vector containing the attenuated measurements of Z(H), Z(DR) and K-DP in a number of range cells while the output is the estimated profile of the rainfall rate. In this way a global compensation of the attenuation is implemented. (C) 2000 Elsevier Science Ltd. All rights reserved.
Galati, G., Pavan, G., Buccini, G. (2000). Neural network applications to the rainfall rate extraction in the presence of attenuation. In First European Conference on Radar Meteorology (pp.1027-1032). THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND : PERGAMON-ELSEVIER SCIENCE LTD [10.1016/S1464-1909(00)00147-7].
Neural network applications to the rainfall rate extraction in the presence of attenuation
Galati, G.;Pavan, G.;
2000-01-01
Abstract
The paper addresses the problem of the reconstruction of the rainfall field using weather radar observables. It is well known that at the C band and especially at the X band the reconstruction of the rainfall rate profile along the range using absolute (Z(H)) and differential (Z(DR)) reflectivity measurements is significantly affected by the attenuation coefficients (i.e. alpha(H) and alpha(D)). This problem has been long and extensively studied and iterative attenuation correction techniques based on a cumulative procedure were developed, in which the attenuation at n(th) cell is estimated using the attenuation corrected reflectivity values at previous cell. Usually the attenuation coefficients (alpha(H), alpha(D)) are estimated using non linear parametrizations with (Z(H), Z(DR)), or, if phase measurements are available, using linear parametrizations with the specific differential phase shift K-DP In this work novel approaches based on neural networks (N.N.) have been used.First, to estimate (alpha(H), alpha(D)) from Z(H), Z(DR) and K-DP; the N.N. estimators have shown better performance (often, slightly better) in comparison to the best ones known.Second, N.N. have been implemented to extract the range rainfall rate profile. The input to the network is a vector containing the attenuated measurements of Z(H), Z(DR) and K-DP in a number of range cells while the output is the estimated profile of the rainfall rate. In this way a global compensation of the attenuation is implemented. (C) 2000 Elsevier Science Ltd. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
