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

Wildfires are increasingly exacerbated as a result of climate change,necessitating advanced proactive measures for effective mitigation. It isimportant to forecast wildfires weeks and months in advance to plan forest fuelmanagement, resource procurement and allocation. To achieve such accuratelong-term forecasts at a global scale, it is crucial to employ models thataccount for the Earth system's inherent spatio-temporal interactions, such asmemory effects and teleconnections. We propose a teleconnection-driven visiontransformer (TeleViT), capable of treating the Earth as one interconnectedsystem, integrating fine-grained local-scale inputs with global-scale inputs,such as climate indices and coarse-grained global variables. Throughcomprehensive experimentation, we demonstrate the superiority of TeleViT inaccurately predicting global burned area patterns for various forecastingwindows, up to four months in advance. The gain is especially pronounced inlarger forecasting windows, demonstrating the improved ability of deep learningmodels that exploit teleconnections to capture Earth system dynamics. Codeavailable at https://github.com/Orion-Ai-Lab/TeleViT.

TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting