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Titel Modelling of weather radar echoes from anomalous propagation using a hybrid parabolic equation method and NWP model data
VerfasserIn D. Bebbington, S. Rae, J. Bech, B. Codina, M. Picanyol
Medientyp Artikel
Sprache Englisch
ISSN 1561-8633
Digitales Dokument URL
Erschienen In: Natural Hazards and Earth System Science ; 7, no. 3 ; Nr. 7, no. 3 (2007-06-08), S.391-398
Datensatznummer 250004543
Publikation (Nr.) Volltext-Dokument vorhandencopernicus.org/nhess-7-391-2007.pdf
 
Zusammenfassung
Contamination of weather radar echoes by anomalous propagation (anaprop) mechanisms remains a serious issue in quality control of radar precipitation estimates. Although significant progress has been made identifying clutter due to anaprop there is no unique method that solves the question of data reliability without removing genuine data. The work described here relates to the development of a software application that uses a numerical weather prediction (NWP) model to obtain the temperature, humidity and pressure fields to calculate the three dimensional structure of the atmospheric refractive index structure, from which a physically based prediction of the incidence of clutter can be made. This technique can be used in conjunction with existing methods for clutter removal by modifying parameters of detectors or filters according to the physical evidence for anomalous propagation conditions. The parabolic equation method (PEM) is a well established technique for solving the equations for beam propagation in a non-uniformly stratified atmosphere, but although intrinsically very efficient, is not sufficiently fast to be practicable for near real-time modelling of clutter over the entire area observed by a typical weather radar. We demonstrate a fast hybrid PEM technique that is capable of providing acceptable results in conjunction with a high-resolution terrain elevation model, using a standard desktop personal computer. We discuss the performance of the method and approaches for the improvement of the model profiles in the lowest levels of the troposphere.
 
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