2026-04-13T02:15:36Zhttps://recercat.cat/oai/request

oai:recercat.cat:2072/3775312024-12-20T14:02:25Zcom_2072_199267com_2072_4427col_2072_199862

2020-10-14T10:04:57Z urn:hdl:2072/377531 Data-driven prediction of thresholded time series of rainfall and SOC models Deluca, A. Moloney, N.R. Corral, A. We study the occurrence of events, subject to threshold, in a representative SOC sandpile model and in high-resolution rainfall data. The predictability in both systems is analyzed by means of a decision variable sensitive to event clustering, and the quality of the predictions is evaluated by the receiver operating characteristics (ROC) method. In the case of the SOC sandpile model, the scaling of quiet-time distributions with increasing threshold leads to increased predictability of extreme events. A scaling theory allows us to understand all the details of the prediction procedure and to extrapolate the shape of the ROC curves for the most extreme events. For rainfall data, the quiet-time distributions do not scale for high thresholds, which means that the corresponding ROC curves cannot be straightforwardly related to those for lower thresholds. 2020-10-14T10:04:57Z 2024-09-19T13:18:53Z 2020-10-14T10:04:57Z 2024-09-19T13:18:53Z 2014-01-01 info:eu-repo/semantics/preprint http://hdl.handle.net/2072/377531 eng info:eu-repo/semantics/openAccess L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons:http://creativecommons.org/licenses/by-nc-nd/4.0/ RECERCAT (Dipòsit de la Recerca de Catalunya)