Researchers at the University of São Paulo have developed an AI system called TITAN-LSTM that predicts a storm's route and rainfall intensity up to 30 minutes in advance, updating every five minutes. The tool is designed to give civil defense teams more time to decide on road closures or evacuations before heavy rain hits.
A team at the Institute of Astronomy, Geophysics and Atmospheric Sciences (IAG) of the University of São Paulo (USP) has developed an artificial intelligence system that forecasts the path and intensity of storms up to 30 minutes before they arrive, according to Jornal da USP and the São Paulo Research Foundation (FAPESP). The system, called TITAN-LSTM, updates its forecast every five minutes.
The tool was developed by Andrea Salome Viteri López, a postdoctoral researcher at IAG-USP, under the supervision of professor Carlos Augusto Morales Rodriguez. The work was funded by FAPESP and, according to USP's reporting, also received support from Capes. The results were published on October 1, 2026, in the Journal of Geophysical Research: Machine Learning and Computation, under DOI 10.1029/2026JH001337, per FAPESP's news agency.
How the system works
TITAN-LSTM combines two existing technologies. The first, TITAN, is an open-source algorithm originally developed in the 1990s by the U.S. National Center for Atmospheric Research to track storms using radar images, calculating each storm's area, intensity, direction and speed. The second is a Long Short-Term Memory (LSTM) neural network, a type of AI model built to recognize patterns in sequences of data over time.
Rather than feeding raw radar images into the neural network, the system converts TITAN's storm measurements into numerical sequences, which the LSTM model then uses to project how a storm's area, direction and intensity will evolve. News outlet ND+ reported that López described the goal of the project as producing rainfall forecasts over a very short time horizon.
Training data and testing
The model was trained and tested using data from a polarimetric weather radar at the Ponte Nova dam in Biritiba Mirim, in the metropolitan region of São Paulo, operated by SP Águas (Daee-Spol), covering a 120-kilometer radius between 2016 and 2019, according to FAPESP's news agency. Jornal da USP also reported the radar's location and the same 2016–2019 data period.
Researchers initially tracked more than 32,000 storms in that radar data. After applying selection criteria — including storms that lasted at least 20 minutes and did not merge with others — 439 events remained. Of those, 307 were used to train the neural network and 132 to test it, FAPESP's agency reported.
Reported accuracy
According to figures reported by FAPESP's news agency, TITAN-LSTM performed about three times better than the standalone TITAN method at predicting the area a storm's rain would cover, raising an accuracy index from roughly 0.2 to about 0.7 in the early forecast minutes. The same source reported a near-70% reduction in false alarms and a rainfall-intensity error of about 3 mm per hour at five minutes ahead, rising to 3–4 mm per hour at the 30-minute mark.
Jornal da USP reported that the hybrid model matched the performance of more complex deep-learning approaches while requiring substantially less computing power. ND+ reported that training the model took about 20 minutes on a standard laptop, without the need for supercomputers.
According to Jornal da USP and FAPESP's agency, the goal is to give civil defense agencies more lead time to decide on measures such as closing roads or evacuating people from risk areas before a storm's heaviest rain arrives. The outlets also reported potential use in feeding hydrological and flood-warning models, since knowing rainfall intensity upstream can help anticipate rapid rises in river levels downstream.
The network decides whether that information remains relevant or not.
— Andrea Salome Viteri López, researcher, IAG-USP, as reported by the FAPESP news agency
Jornal da USP reported that the broader aim, as described by the researchers, is to give staff at emergency management centers better conditions to make appropriate decisions during severe weather events.
Limits of the study
The model was trained and tested only on radar data from the São Paulo metropolitan region between 2016 and 2019, and the reported accuracy figures apply to that dataset. The sources reviewed did not report an operational deployment date, nor whether the tool has been adopted by any civil defense agency.
Questions
What is TITAN-LSTM?
It is an AI system developed at USP that combines a radar-based storm-tracking algorithm (TITAN) with an LSTM neural network to forecast a storm's path and rainfall intensity up to 30 minutes ahead.
Who developed the tool?
Andrea Salome Viteri López, a postdoctoral researcher at USP's Institute of Astronomy, Geophysics and Atmospheric Sciences, developed it under the supervision of professor Carlos Augusto Morales Rodriguez, with FAPESP funding.
How accurate is the forecast?
According to FAPESP's news agency, the model's accuracy index for predicting rainfall area rose from about 0.2 to about 0.7 in the early forecast minutes, and false alarms dropped by nearly 70% compared with the standalone TITAN method.
What data was used to build it?
Researchers used radar data from the Ponte Nova dam in Biritiba Mirim, São Paulo, collected between 2016 and 2019, tracking over 32,000 storms and selecting 439 for training and testing the neural network.
Is the tool already in use by civil defense agencies?
The sources reviewed describe the tool's intended use for civil defense decisions such as road closures and evacuations, but do not report an operational deployment date or adoption by a specific agency.
How the world is telling it
PortugueseBrazilian outlets (Jornal da USP, FAPESP's news agency, and regional outlet ND+) frame the story as a homegrown scientific achievement from USP, emphasizing its low computational cost and its potential to improve storm warnings and civil defense response in São Paulo's flood-prone metropolitan region.
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