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

. Rainfall forecasts are an integral part of hydrological forecasting systemsat sub-seasonal to seasonal timescales. In seasonal forecasting, globalclimate models (GCMs) are now the go-to source for rainfall forecasts.For hydrological applications however, GCM forecasts are often biased andunreliable in uncertainty spread, and calibration is therefore requiredbefore use. There are sophisticated statistical techniques for calibratingmonthly and seasonal aggregations of the forecasts. However, calibration ofseasonal forecasts at the daily time step typically uses very simplestatistical methods or climate analogue methods. These methods generally lackthe sophistication to achieve unbiased, reliable and coherent forecasts ofdaily amounts and seasonal accumulated totals. In this study, we propose andevaluate a Rainfall Post-Processing method for Seasonal forecasts (RPP-S), which isbased on the Bayesian joint probability modelling approach for calibratingdaily forecasts and the Schaake Shuffle for connecting the daily ensemblemembers of different lead times. We apply the method to post-process ACCESS-Sforecasts for 12 perennial and ephemeral catchments across Australia and for12 initialisation dates. RPP-S significantly reduces bias in raw forecastsand improves both skill and reliability. RPP-S forecasts are also more skilful andreliable than forecasts derived from ACCESS-S forecasts that have beenpost-processed using quantile mapping, especially for monthly and seasonalaccumulations. Several opportunities to improve the robustness and skill ofRPP-S are identified. The new RPP-S post-processed forecasts will be used inensemble sub-seasonal to seasonal streamflow applications.

hydrology and earth system sciences

A Bayesian modelling method for post-processing daily sub-seasonal to seasonal rainfall forecasts from global climate models and evaluation for 12 Australian catchments