Typhoon Predictions
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Machine Learning (ML)-Physics Fusion Model Outperforms Both Physics-Only and ML-Only Models in Typhoon Predictions
Announce Type: replace Abstract: Data-driven machine learning (ML) models, such as FuXi, exhibit notable limitations in forecasting typhoon intensity and structure. This study presents a comprehensive evaluation of FuXi-SHTM, a hybrid ML-physics model, using all 2024 western North Pacific typhoon cases. The FuXi-SHTM hybrid demonstrates clear improvements in both track and intensity forecasts compared to the standalone SHTM, FuXi, and ECMWF HRES models.
Japan’s looming typhoon crisis threatens disaster defences and tourism
Japan’s looming typhoon crisis threatens disaster defences and tourism Up to 28 typhoons are predicted to affect Japan this year, with 14 likely to make landfall – close to historical extremes Japan could be heading into one of the worst typhoon seasons in its history, raising fears that stronger, more frequent storms will challenge not only the country’s disaster defences but also a summer travel industry already stretched by packed trains, hotels and itineraries. Tokyo-based Weathernews, a...
ML-Physical Fusion Models Are Accelerating the Paradigm Shift in Operational Typhoon Forecasting
arXiv:2503.00424v2 Announce Type: replace Abstract: In this study, we develop a hybrid operational typhoon forecasting model that integrates the FuXi machine-learning (ML) model with the physics-based Shanghai Typhoon Model (SHTM) into a dual physics-data-driven framework. By employing spectral nudging, the hybrid model named FuXi-SHTM leverages FuXi's robust large-scale forecasting capabilities alongside SHTM's mesoscale strengths, significantly enhancing track, intensity, and precipitation...
Measurement of reactor neutrino oscillation with the first JUNO data
Abstract Neutrino oscillations (see refs. 1,2 and references therein), a quantum effect manifesting at macroscopic scales, are governed by lepton flavour mixing angles and neutrino mass-squared differences3 that are fundamental parameters of particle physics, representing phenomena beyond the Standard Model. Precision measurements of these parameters are essential for testing the completeness of the three-flavour framework, determining the mass ordering of neutrinos and probing possible new...
Japan’s southernmost region of Okinawa braces for Typhoon Jangmi
The tropical storm, sweeping through the southernmost Japanese prefecture of Okinawa, is expected to head towards Amami Oshima island on Tuesday. Japan’s southernmost region of Okinawa was bracing for the arrival of Typhoon Jangmi on Monday, a storm system expected to bring severe rain and winds. The Japan Meteorological Agency said that the Okinawa Islands, the Sakishima Islands and the Daitō Islands, face high risks of landslides and high waves.
Super Typhoon Sinlaku triggered atmospheric gravity waves visible from space
Super Typhoon Sinlaku triggered atmospheric gravity waves visible from space A record-early super typhoon sent giant atmospheric ripples into near-space, offering scientists a new clue for tracking powerful storms. - Date: - June 3, 2026 - Source: - NASA Earth Observatory - Summary: - One of the most powerful typhoons ever recorded this early in the Pacific season did more than unleash flooding and extreme winds—it sent enormous ripples all the way into the upper atmosphere.
How could El Nino reshape tropical storms around the world this year?
How could El Nino reshape tropical storms around the world this year? El Nino tends to reduce hurricanes in the Atlantic while increasing storms in the Pacific Ocean. The Atlantic hurricane season has just begun and runs from Monday to November 30 with storm activity peaking in mid-September.
From Forecast to Action: A Deep Learning Model for Predicting Power Outages During Tropical Cyclones
arXiv:2512.06644v2 Announce Type: replace Abstract: Power outages caused by tropical cyclones (TCs) pose serious risks to electric power systems and the communities they serve. Accurate, high-resolution outage forecasting is essential for enabling both proactive mitigation planning and real-time emergency response. This study introduces the SpatioTemporal Outage ForeCAST (STO-CAST) model, a deep learning framework developed for real-time, regional-scale outage prediction during TC events...