AI needs to learn the motion of the ocean, researchers report
Peer-Reviewed Publication
Updates every hour. Last Updated: 20-Oct-2026 12:17 ET (20-Oct-2026 16:17 GMT/UTC)
Artificial intelligence (AI) can help reconstruct the ocean interior, predict waves and forecast the El Niño Southern Oscillation (ENSO) — but can it do so in ways that remain consistent with ocean physics? The answer is yes, according to a recent paper available online now and slated for upcoming publication in Ocean-Land-Atmosphere Research.
Researchers have substantially expanded the known genomic diversity of Aoguangviridae, a family of marine viruses associated with the abundant archaeal order Poseidoniales. By increasing the non-redundant genome set from 59 to 227, the study revealed extensive previously unrecognized diversity and distinct ecological distribution patterns across the global ocean. The researchers also identified auxiliary metabolic genes and evidence of transcriptional activity, providing new insights into the potential ecological roles of these viruses and their interactions with archaeal hosts. The findings were published on 2 September in Ocean-Land-Atmosphere Research.
Researchers undertook a study of the ocean’s topography to better understand its impact on Earth’s climate. They discovered that the shape of the ocean floor does affect the global climate system. Their research was published in the journal Ocean-Land-Atmosphere Research on August 25, 2026.
With heavy reusable launch vehicles becoming a key technology for low-cost and high-frequency space exploration, cable-net recovery schemes have attracted considerable attention due to their strong adaptability to landing dispersion and attitude deviations. However, under multiple sources of interference—including engine plume impingement, trolley motion, and flexible vibrations—the dynamic state information of the flexible cable-net recovery system is difficult to acquire directly, severely constraining the accuracy of closed-loop control and capture reliability. Although Kalman filtering combined with multibody dynamics models can achieve state estimation for complex systems, the flexible cable-net system is inherently a high-dimensional, nonlinear, strongly rigid-flexible coupled constrained multibody system, whose dynamic equations are formulated as index-3 differential-algebraic equations (DAEs). Conventional methods require the conversion of DAEs into ordinary differential equations (ODEs) followed by linearization, a process that is complex and lacks generality; conventional numerical integration methods converge slowly when dealing with such stiff problems, with the Jacobian matrix becoming severely ill-conditioned under large time steps and constraints being prone to violation. Therefore, developing a filtering method that does not require model transformation, can directly utilize the multibody DAE for state prediction, and can maintain constraint satisfaction has become a key technical challenge for state estimation in flexible cable-net recovery.
Apophis will make an extremely close flyby of Earth on April 13, 2029, at a distance of approximately 31,029 km—an event unprecedented for an asteroid of this size—providing a valuable natural experiment for studying tidal interactions, spin-state variations, and surface material migration between the asteroid and Earth. A large number of ground-based observations and space missions are expected to participate, but most observations will be concentrated from Earth's perspective, leaving observational blind spots as Apophis rapidly recedes from Earth's view after the flyby. LUMIO (Lunar Meteoroid Impacts Observer) is a 12U CubeSat of the European Space Agency, planned to be deployed in a quasi-halo orbit around the Earth–Moon L2 point; its primary mission is to observe meteoroid impact flashes on the lunar farside, with a launch expected in 2028. However, three critical questions remain unanswered: whether LUMIO-Cam—a camera designed for observing impact flashes—can detect Apophis as a sub-pixel target, whether it can resolve fluctuations in its light curve, and whether it can observe for a sufficiently long duration to obtain complete spin-period information.