English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We study the occurrence of events, subject to threshold, in a representativeSOC sandpile model and in high-resolution rainfall data. The predictability inboth systems is analyzed by means of a decision variable sensitive to eventclustering, and the quality of the predictions is evaluated by the receiveroperating characteristics (ROC) method. In the case of the SOC sandpile model,the scaling of quiet-time distributions with increasing threshold leads toincreased predictability of extreme events. A scaling theory allows us tounderstand all the details of the prediction procedure and to extrapolate theshape of the ROC curves for the most extreme events. For rainfall data, thequiet-time distributions do not scale for high thresholds, which means that thecorresponding ROC curves cannot be straightforwardly related to those for lowerthresholds.

Data-Driven Prediction of Thresholded Time Series of Rainfall and SOC models