Results 161 to 170 of about 13,592 (226)

Disentangling Spatial‐Temporal Features for Controllable Factors Learning in Precipitation Nowcasting

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Precipitation nowcasting refers to the high‐resolution forecasting of rainfall and hydrometeors within 0–6 hr according to the official definition of the World Meteorological Organization, which has relied on numerical models for decades. Recently, artificial intelligence (AI) has shown promise in addressing precipitation nowcasting.
Nan Yang   +3 more
wiley   +1 more source

Attention and Geological Knowledge Guided Spectral‐Spatial Networks for Geochemical Anomalies Recognition

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Achieving both accuracy and interpretability in deep learning models for geochemical anomaly recognition constitutes a significant challenge. To overcome this challenge, this study developed a novel interpretable dual‐branch network combining a spectral attention bidirectional RNN (BiRNN) branch and a spatial attention CNN branch guided with ...
Yihui Xiong   +4 more
wiley   +1 more source

AI Challenge for Satellite Tracking and Orbit Resilience Modeling (STORM‐AI): Data Set, Design, and Results

open access: yesSpace Weather, Volume 24, Issue 8, August 2026.
Abstract Precise thermospheric density forecasting is critical for mitigating satellite drag in Low Earth Orbit (LEO), but traditional empirical models such as MSIS and JB2008 can fail during geomagnetic storms. To evaluate whether AI can better capture this transient behavior, the 2025 MIT ARCLab Prize for AI Innovation in Space asked participants to ...
Sergio Sanchez‐Hurtado   +18 more
wiley   +1 more source

Simultaneous Multi–Horizon Forecasting of Dst and Hp60 Geomagnetic Indices via a Multitask LSTM Framework

open access: yesSpace Weather, Volume 24, Issue 8, August 2026.
Abstract This study presents a novel multitask deep learning framework that integrates models designed for simultaneous, multi‐horizon forecasting of Hp60 and Disturbance storm time (Dst) indices with lead times up to 3 hr. Using a Long Short‐Term Memory architecture, the models capture both mid‐latitude variability and the ring current dynamics. Input
Joseph Kagotho Muriithi   +4 more
wiley   +1 more source

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