TY - JOUR
T1 - Polarimetric weather radar retrieval of raindrop size distribution by means of a regularized artificial neural network
AU - Vulpiani, Gianfranco
AU - Marzano, Frank Silvio
AU - Chandrasekar, V.
AU - Berne, Alexis
AU - Uijlenhoet, Remko
PY - 2006
Y1 - 2006
N2 - The raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dualpolarized weather radars. A new neural-network algorithm to estimate the RSD from S-band dual-polarized radar measurements is presented. The corresponding rain rates are then computed assuming a commonly used raindrop diameter speed relationship. Numerical simulations are used to investigate the efficiency and accuracy of this method. A stochastic model based on disdrometer measurements is used to generate realistic range profiles of the RSD parameters, while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. The error analysis, which is performed in order to evaluate the expected errors of this method, shows an improvement with respect to other methodologies described in the literature. A further sensitivity evaluation shows that the proposed technique performs fairly well even for low specific differential phase-shift values.
AB - The raindrop size distribution (RSD) is a critical factor in estimating rain intensity using advanced dualpolarized weather radars. A new neural-network algorithm to estimate the RSD from S-band dual-polarized radar measurements is presented. The corresponding rain rates are then computed assuming a commonly used raindrop diameter speed relationship. Numerical simulations are used to investigate the efficiency and accuracy of this method. A stochastic model based on disdrometer measurements is used to generate realistic range profiles of the RSD parameters, while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. The error analysis, which is performed in order to evaluate the expected errors of this method, shows an improvement with respect to other methodologies described in the literature. A further sensitivity evaluation shows that the proposed technique performs fairly well even for low specific differential phase-shift values.
KW - Artificial neural network
KW - Radar polarimetry
KW - Raindrop size distribution (RSD)
KW - Regularization
UR - http://www.scopus.com/inward/record.url?scp=33750844986&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2006.878438
DO - 10.1109/TGRS.2006.878438
M3 - Article
AN - SCOPUS:33750844986
SN - 0196-2892
VL - 44
SP - 3262
EP - 3274
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 11
M1 - 1717720
ER -