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

Rainy years and dry years in SE Australia are known to be correlated with seasurface temperatures in the specific areas of the Indian Ocean. While over thepast 100 years the correlation had been both positive and negative, itsignificantly increased in strength since the beginning of the 21st century.Over this period, Indian Ocean sea surface temperatures during the wintermonths, together with the El Ni\~no variations, contain sufficient informationto accurately hindcasts springtime rainfall in SE Australia. Using deeplearning neural networks trained on the early 21st century data we performedboth hindcasting and simulated forecasting of the recent spring rainfall in SEAustralia, Victoria and South Australia. The method is particularly suitablefor quantitative testing of the importance of different ocean regions inimproving the predictability of rainfall modelling. The network hindcastingresults with longer lead times are improved when current winter El Ni\~notemperatures in the input are replaced by forecast temperatures from the bestdynamical models. High skill is limited to the rainfall forecasts for theSeptember/October period constructed from the end of June.

Strengthening of Indian Ocean teleconnections permits predictions of springtime rainfall in SE Australia