Polarimetric weather radar retrieval of raindrop size distribution by means of a regularized artificial neural network

Gianfranco Vulpiani, Frank Silvio Marzano, V. Chandrasekar, Alexis Berne, Remko Uijlenhoet

Research output: Contribution to journalArticleScientificpeer-review

26 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number1717720
Pages (from-to)3262-3274
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume44
Issue number11
DOIs
Publication statusPublished - 2006
Externally publishedYes

Keywords

  • Artificial neural network
  • Radar polarimetry
  • Raindrop size distribution (RSD)
  • Regularization

Fingerprint Dive into the research topics of 'Polarimetric weather radar retrieval of raindrop size distribution by means of a regularized artificial neural network'. Together they form a unique fingerprint.

Cite this