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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper addresses the problem of the relationship between skill scoresand forecast rainfall relative errors. The problem is approached by usingsynthetic time series of rainfall data representing the observations. It isassumed that the magnitude of the relative error is known. The forecasts areconstructed by adding errors to the observations. We use a threshold todichotomise forecasts and observations to obtain the skill scores. Weperform 1000 simulations for each error magnitude in order to obtain themean values and uncertainties of the scores.  We consider two different precipitation regimes, and we show the influenceof these regimes on the precipitation. We find that the relationship betweenforecast errors and skill scores is strongly influenced by the eventfrequencies, which in turn depend on the precipitation regime. We find thatonly when the event frequency of the two regimes is made similar by changingthe threshold, the relationship between the scores and relative errors issimilar. This suggests that a comparison between two forecast precipitationdatasets should account for the difference (if any) in precipitationregimes

Relationship between forecast precipitation relative errors and skill scores: the case of rare event frequencies