Advances in Atmospheric Sciences & Partner Journals

El Niño’s first and hardest hits: Why South America needs month-by-month preparedness

A 75-year dataset reveals a stark continental divide in El Niño impacts, with Southern Brazil facing torrential rainfall and the North experiencing severe drought. Month-by-month planning can help target preparedness efforts and reduce vulnerability.

SourceAdvances in Atmospheric Sciences & Partner Journals·JournalAtmospheric and Oceanic Science Letters·DateNov 12, 2026

Interpretable deep learning network quantifies dominant predictors of 24-hour tropical cyclone intensity change

A new deep learning network, Kolmogorov–Arnold Networks (KANs), improves 24-hour tropical cyclone intensity forecasting accuracy and provides robust interpretability. The model selects high-impact predictors and transforms data-driven outputs into analytical symbolic expressions.

SourceAdvances in Atmospheric Sciences & Partner Journals·JournalAtmospheric and Oceanic Science Letters·DateOct 26, 2026