Accurate quantitative precipitation estimation (QPE) methods are essential for weather forecasting and for prevention of hydrogeological risk. QPE becomes even more important when facing severe precipitation events. In this letter, a comparison among different rainfall estimation methods is presented, using a severe event that occurred in Italy as a case study. In particular, the focus is on a merging method based on the dynamic adaptation of the Z–R relationship according to the spatiotemporal evolution of the observed phenomenon. Through a cross-validation analysis, we quantitatively assess the effectiveness of such a method: compared with the others, it performs better on the average, while it can outperform them in critical rainfall conditions, confirming its potential for localizing and monitoring areas with greatest risks.

Assessing Quantitative Precipitation Estimation Methods Based on the Fusion of Weather Radar and Rain-Gauge Data / Biondi, Alessio; Facheris, Luca; Argenti, Fabrizio; Cuccoli, Fabrizio; Antonini, Andrea; Melani, Samantha. - In: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. - ISSN 1545-598X. - STAMPA. - 21:(2024), pp. 3508005.1-3508005.5. [10.1109/lgrs.2024.3434650]

Assessing Quantitative Precipitation Estimation Methods Based on the Fusion of Weather Radar and Rain-Gauge Data

Biondi, Alessio;Facheris, Luca
;
Argenti, Fabrizio;Cuccoli, Fabrizio;Melani, Samantha
2024

Abstract

Accurate quantitative precipitation estimation (QPE) methods are essential for weather forecasting and for prevention of hydrogeological risk. QPE becomes even more important when facing severe precipitation events. In this letter, a comparison among different rainfall estimation methods is presented, using a severe event that occurred in Italy as a case study. In particular, the focus is on a merging method based on the dynamic adaptation of the Z–R relationship according to the spatiotemporal evolution of the observed phenomenon. Through a cross-validation analysis, we quantitatively assess the effectiveness of such a method: compared with the others, it performs better on the average, while it can outperform them in critical rainfall conditions, confirming its potential for localizing and monitoring areas with greatest risks.
2024
21
1
5
Biondi, Alessio; Facheris, Luca; Argenti, Fabrizio; Cuccoli, Fabrizio; Antonini, Andrea; Melani, Samantha
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1377972
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