Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics

Abstract R ∼ 115  km2), Germany, are used as a basis for investigation. A “leave-one-event-out” cross-validation is employed to test the nearest-neighbour approach for the prediction of the area, mean intensity, the x y + +  3 h. Prior to the application, two importance analysis methods (Pearson correlation and partial information correlation) are employed to identify the most important predictors. The results indicate that most of the storms behave similarly, and the knowledge obtained from such similar past storms helps to capture better the storm dissipation and improves the nowcast compared to the Lagrangian persistence, especially for convective events (storms shorter than 3 h) and longer lead times (from 1 to 3 h). The main advantage of the nearest-neighbour approach is seen when applied in a probabilistic way (with the 30 closest neighbours as ensembles) rather than in a deterministic way (averaging the response from the four closest neighbours). The probabilistic approach seems promising, especially for convective storms, and it can be further improved by either increasing the sample size, employing more suitable methods for the predictor identification, or selecting physical predictors.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics ; volume:26 ; number:6 ; year:2022 ; pages:1631-1658 ; extent:28 ; Hydrology and earth system sciences ; 26, Heft 6 (2022), 1631-1658 (gesamt 28)

Beteiligte, Orts- und Zeitangaben

• Urheber: Shehu, Bora ; Haberlandt, Uwe

Weitere Informationen

• DOI: 10.5194/hess-26-1631-2022
• URN: urn:nbn:de:101:1-2022040110161478741464
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:30 MESZ