EGU23-12475, updated on 16 Jan 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Comparing different radar-raingauge precipitation merging methods for Tuscany region

Rossano Ciampalini1, Andrea Antonini2, Alessandro Mazza2, Samantha Melani2, Alberto Ortolani2,3, Ascanio Rosi1,4, Samuele Segoni1, and Sandro Moretti1
Rossano Ciampalini et al.
  • 1Department of Earth Sciences, University of Florence, Italy (
  • 2Consorzio LaMMA, Sesto Fiorentino, Florence, Italy
  • 3CNR-IBE, Sesto Fiorentino, Florence, Italy
  • 4Department of Geosciences, University of Padova, Italy

Radar-based rainfall estimation represents an effective tool for hydrological modelling. Nevertheless, this data type is subject to systemic and natural perturbations that need to be considered before to use it. Because of that and to improve data quality, corrections based on raingauge observations are frequently adopted. Here, we compared the efficacy of different radar-raingauge merging procedures over a regional raingauge-radar network focusing on a selected number of rainfalls events.
We adopted the methods: 1) Kriging with External Drift (KED) interpolation (Wackernagel 1998), 2) Probability-Matching-Method (PMM, Rosenfeld et al., 1994), and 3) a kriging mixed method exploiting the Conditional Merging (CM) process (Sinclair-Pegram, 2005) based on elaborations available at DPCN (Italian National Civil Protection Department). These methods have been applied on the Tuscany Regional Territory using raingauge recorded rainfalls at 15’ time-step and CAPPI (Constant altitude plan position indicator) reflectivity data at 2000/3000/5000 m at 5’ and 10’.
Relationships describing precipitation VS radar reflectivity were integrated and elaborated as part of the development of the merging procedures, while the comparison involved the analysis of variance and diversity coefficients. Kriging-based elaborations showed different spatial patterns depending on the applied procedure, with patterns closer to radar variability when using DPCN, and more reflecting the gauge data structure when adopting KED. The probabilistic method (PMM), instead, had the advantage of integrating the gauge data while preserving the spatial radar patterns, confirming interesting perspectives.

How to cite: Ciampalini, R., Antonini, A., Mazza, A., Melani, S., Ortolani, A., Rosi, A., Segoni, S., and Moretti, S.: Comparing different radar-raingauge precipitation merging methods for Tuscany region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12475,, 2023.