Identification of Vertical Profiles of Reflectivity for Correction of Volumetric Radar Data Using Rainfall Classification Article - 2010

Pierre-Emmanuel Kirstetter, Hervé Andrieu, Guy Delrieu, Brice Boudevillain

Pierre-Emmanuel Kirstetter, Hervé Andrieu, Guy Delrieu, Brice Boudevillain, « Identification of Vertical Profiles of Reflectivity for Correction of Volumetric Radar Data Using Rainfall Classification  », Journal of Applied Meteorology and Climatology, 2010, pp. 2167-2180. ISSN 1558-8424

Abstract

Nonuniform beam filling associated with the vertical variation of atmospheric reflectivity is an important source of error in the estimation of rainfall rates by radar. It is, however, possible to correct for this error if the vertical profile of reflectivity (VPR) is known. This paper presents a method for identifying VPRs from volumetric radar data. The method aims at improving an existing algorithm based on the analysis of ratios of radar measurements at multiple elevation angles. By adding a rainfall classification procedure defining more homogeneous precipitation patterns, the issue of VPR homogeneity is specifically addressed. The method is assessed using the dataset from a volume-scanning strategy for radar quantitative precipitation estimation designed in 2002 for the Bollène radar (France). The identified VPR is more representative of the rain field than are other estimated VPRs. It has also a positive impact on radar data processing for precipitation estimation : while scatter remains unchanged, an overall bias reduction at all time steps is noticed (up to 6% for all events) whereas performance varies with type of events considered (mesoscale convective systems, cold fronts, or shallow convection) according to the radar-observation conditions. This is attributed to the better processing of spatial variations of the vertical profile of reflectivity for the stratiform regions. However, adaptation of the VPR identification in the difficult radar measurement context in mountainous areas and to the rainfall classification procedure proved challenging because of data fluctuations.

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