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Advances in Meteorology
Volume 2016 (2016), Article ID 6089319, 9 pages
Research Article

Mesoscale and Local Scale Evaluations of Quantitative Precipitation Estimates by Weather Radar Products during a Heavy Rainfall Event

1Centre de Recherche de Climatologie, Biogéosciences UMR 6282 CNRS, Université Bourgogne Franche-Comté, 6 bd Gabriel, 21000 Dijon, France
2Météo-France Direction Régionale Centre-Est, 22 rue Louis de Broglie, 21000 Dijon, France
3UFR SVTE, Université Bourgogne Franche-Comté, 6 bd Gabriel, 21000 Dijon, France

Received 8 June 2016; Revised 9 September 2016; Accepted 19 September 2016

Academic Editor: Bin Yong

Copyright © 2016 Basile Pauthier et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A 24-hour heavy rainfall event occurred in northeastern France from November 3 to 4, 2014. The accuracy of the quantitative precipitation estimation (QPE) by PANTHERE and ANTILOPE radar-based gridded products during this particular event, is examined at both mesoscale and local scale, in comparison with two reference rain-gauge networks. Mesoscale accuracy was assessed for the total rainfall accumulated during the 24-hour event, using the Météo France operational rain-gauge network. Local scale accuracy was assessed for both total event rainfall and hourly rainfall accumulations, using the recently developed HydraVitis high-resolution rain gauge network Evaluation shows that (1) PANTHERE radar-based QPE underestimates rainfall fields at mesoscale and local scale; (2) both PANTHERE and ANTILOPE successfully reproduced the spatial variability of rainfall at local scale; (3) PANTHERE underestimates can be significantly improved at local scale by merging these data with rain gauge data interpolation (i.e., ANTILOPE). This study provides a preliminary evaluation of radar-based QPE at local scale, suggesting that merged products are invaluable for applications at very high resolution. The results obtained underline the importance of using high-density rain-gauge networks to obtain information at high spatial and temporal resolution, for better understanding of local rainfall variation, to calibrate remotely sensed rainfall products.