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Summary: Characterisation of the error structure of radar quantitative precipitation estimation (QPE) is a major issue for applications of radar technology in hydrological modelling. Due to the variety of sources of error in radar QPE and the impact of correction algorithms, the problem can only be addressed practically by comparison of radar QPEs with reference values derived from ground-based measurements. Using the radar and raingauge datasets of the Bollène-2002 experiment, a preliminary investigation of this subject has been carried out within a geostatistical framework in the context of the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory. First, raingauge measurements were critically analysed using variograms to detect erroneous squared differences between pairs of raingauge values. The anisotropic block kriging technique was then used to compute and select the most reliable reference values. The statistical distribution and the spatial and temporal structure of the residuals between radar and reference values was established and analysed. The error variance separation concept was also tested to estimate the variance of the residuals between the radar estimates and the true unknown rainfall. A preliminary version of the radar QPE error model was established for 1-km2 domains and time steps ranging from 1 to 12h using the limited data sample of the Bollène-2002 experiment. The error model is dependent on the time scales considered and needs to be conditioned on the rain rate and rainfall type as well as on the radar range. [ABSTRACT FROM AUTHOR]

The Bollène-2002 Experiment was aimed at developing the use of a radar volume-scanning strategy for conducting radar rainfall estimations in the mountainous regions of France. A developmental radar processing system, called Traitements Régionalisés et Adaptatifs de Données Radar pour l’Hydrologie (Regionalized and Adaptive Radar Data Processing for Hydrological Applications), has been built and several algorithms were specifically produced as part of this project. These algorithms include 1) a clutter identification technique based on the pulse-to-pulse variability of reflectivity Z for noncoherent radar, 2) a coupled procedure for determining a rain partition between convective and widespread rainfall R and the associated normalized vertical profiles of reflectivity, and 3) a method for calculating reflectivity at ground level from reflectivities measured aloft. Several radar processing strategies, including nonadaptive, time-adaptive, and space–time-adaptive variants, have been implemented to assess the performance of these new algorithms. Reference rainfall data were derived from a careful analysis of rain gauge datasets furnished by the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory. The assessment criteria for five intense and long-lasting Mediterranean rain events have proven that good quantitative precipitation estimates can be obtained from radar data alone within 100-km range by using well-sited, well-maintained radar systems and sophisticated, physically based data-processing systems. The basic requirements entail performing accurate electronic calibration and stability verification, determining the radar detection domain, achieving efficient clutter elimination, and capturing the vertical structure(s) of reflectivity for the target event. Radar performance was shown to depend on type of rainfall, with better results obtained with deep convective rain systems (Nash coefficients of roughly 0.90 for point radar–rain gauge comparisons at the event time step), as opposed to shallow convective and frontal rain systems (Nash coefficients in the 0.6–0.8 range). In comparison with time-adaptive strategies, the space–time-adaptive strategy yields a very significant reduction in the radar–rain gauge bias while the level of scatter remains basically unchanged. Because the Z–R relationships have not been optimized in this study, results are attributed to an improved processing of spatial variations in the vertical profile of reflectivity. The two main recommendations for future work consist of adapting the rain separation method for radar network operations and documenting Z–R relationships conditional on rainfall type. [ABSTRACT FROM AUTHOR]