By Pesach Benson • April 14, 2026
Jerusalem, 14 April, 2026 (TPS-IL) — The most intense and destructive rainstorms in Portugal may also be among the most predictable, according to a new study that challenges long-held assumptions about extreme weather and its apparent chaos.
The research by scientists in Israel and Germany focuses on powerful rain events known as Heavy Precipitation Events, which have increasingly threatened infrastructure, water systems and public safety across the western Iberian Peninsula. Many of these storms are fueled by atmospheric rivers — long, narrow bands of fast-moving moisture in the atmosphere that carry large amounts of water vapor from the ocean and can produce intense rainfall when they reach land.
In December 2022, one such storm battered western Portugal, swelling rivers, flooding streets and leaving widespread damage. To residents, the event appeared erratic and unpredictable, reinforcing concerns about the growing volatility associated with climate change. However, the new findings suggest that some of the most dangerous storms may, in fact, offer clearer warning signs than previously understood.
The study, led by Mr. Ehud Bartfeld and Dr. Assaf Hochman of the Hebrew University of Jerusalem together with Dr. Alexandre M. Ramos of the Karlsruhe Institute of Technology, concludes that the fiercest downpours often develop within large, well-organized atmospheric systems that are inherently more predictable.
Published in the peer-reviewed Weather and Climate Extremes journal, the research finds that storms linked to atmospheric rivers produce rainfall that is, on average, 36 percent more intense than storms without them. Crucially, the increased severity is not primarily due to higher overall moisture levels in the atmosphere, but to stronger low-level winds that channel moisture more efficiently into affected areas.
“It’s not just how much water the atmosphere holds,” the researchers said. “It’s how effectively the system delivers that water to the ground.”
Beyond measuring intensity, the study addresses a central challenge in meteorology: when and why extreme events can be reliably forecast. Using a novel dynamical systems approach, the team analyzed how atmospheric conditions evolve before and during storms, focusing on patterns in both lower and upper layers of the atmosphere.
They identified a clear divide. The most predictable extreme rainfall events were consistently associated with deep, well-structured extra-tropical cyclones forming over the North Atlantic. These systems exhibited pressure anomalies roughly twice as strong as those seen in less predictable storms, along with more coherent jet stream interactions and large-scale atmospheric wave patterns.
In practical terms, these highly organized systems were also significantly more dangerous. The study found that they produced rainfall intensities about 80 percent greater than less predictable events, overturning the assumption that the most severe storms are also the hardest to anticipate.
“The irony is that the most dangerous events are often the ones the atmosphere signals most clearly,” the researchers said. “When the large-scale structure is strong and organized, the system becomes more ‘readable’.”
The December 2022 storm in Portugal provided a key case study. According to the researchers, the alignment of an atmospheric river with a powerful cyclone and a structured jet stream not only generated extreme rainfall but also created clearer atmospheric signals that could support higher forecast confidence.
The findings suggest that combining atmospheric river detection with dynamical systems analysis could significantly improve early warning systems. This approach may help forecasters better distinguish between storms that are likely to behave erratically and those that follow more stable, predictable patterns.
As climate change continues to intensify rainfall extremes in many parts of the world, the ability to identify when the atmosphere is producing strong, organized signals could become critical for disaster preparedness.
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