40 papers
This paper demonstrates that an ensemble of Graph Neural Networks for regional sea surface temperature forecasting, which introduces diversity through spatially coherent input perturbations like Perlin noise rather than model retraining, achieves well-calibrated probabilistic forecasts with improved uncertainty representation at no additional training cost.
This paper demonstrates that the Continuous-Time Koopman Autoencoder (CT-KAE) serves as a lightweight, stable, and efficient surrogate model for long-horizon ocean state forecasting, outperforming autoregressive Transformer baselines by maintaining bounded errors and consistent large-scale statistics over 2083-day rollouts while enabling resolution-invariant predictions.
This study demonstrates that Ganymede's subsurface ocean circulation generates detectable magnetic signatures, up to 9 nT, which are distinguishable from the planet's intrinsic field at high spherical harmonic degrees, thereby highlighting the necessity for low-altitude orbits for the JUICE probe to observe these ocean dynamics.
This paper presents an open-source dynamical box model to simulate global abiotic sulfur cycling on Earth-like planets, revealing that the absence of life would result in marine sediment sulfate concentrations two orders of magnitude higher and sulfide concentrations four orders of magnitude lower than on present-day Earth.
This study utilizes high-resolution microfluidic experiments to demonstrate that CO2 phase state and flow rate critically govern salt precipitation kinetics and spatial distribution during injection, revealing that supercritical CO2 significantly accelerates evaporation and crystallization compared to liquid or gaseous phases, thereby providing essential quantitative benchmarks for predicting near-wellbore permeability impairment in geological carbon storage.
This paper demonstrates that while internal-external magnetic field separation is unique and stable on a full sphere, it becomes non-unique without prior assumptions and highly unstable even with reasonable assumptions when data is restricted to a subdomain, a challenge explained through a Hardy-Hodge decomposition of spherical vector fields.
This paper introduces "phase variance," a data-driven, frequency-dependent quality-control attribute based on circular statistics that quantifies localized phase dispersion in seismic data without requiring phase unwrapping or global wavelet models, thereby enabling automated assessment of phase reliability to support advanced workflows like AVO and full-waveform inversion.
This paper proposes that hydrodynamic outflows driven by hydrogen recombination in the extended proto-lunar disk atmosphere, distinct from Earth's compact carbon-dominated atmosphere, effectively removed volatile elements from the Moon's precursor material, thereby explaining the observed volatile depletion in lunar rocks relative to Earth.
This paper proposes a stochastic-dynamic model demonstrating that floating oil slicks increase sea surface temperature, amplify temperature variability, and reduce predictability, thereby suggesting that current air-sea coupled climate models may underestimate the impact of oil pollution on global climate uncertainty.
This paper proposes an improved objective function for the optAPM code, featuring a simplified hotspot cost formulation and pre-interpolation of trail data, to reduce modeling errors and enhance the accuracy of absolute plate motion reconstructions over geological timescales.
This paper introduces SMARL, a reinforcement learning framework that uses enstrophy spectrum-based rewards to develop stable, data-efficient subgrid-scale closures for geophysical turbulence, enabling accurate prediction of extreme events with significantly reduced computational costs.
This paper presents experimental evidence and a supporting theoretical model demonstrating that the interaction between surface waves and ambient sub-surface turbulence generates a near-surface Eulerian-mean flow that opposes and partially cancels the Stokes drift, thereby significantly altering the vertical redistribution of momentum and the transport of water-borne materials in the ocean.
Numerical modeling suggests that convection drives large englacial plumes in north Greenland by exploiting softer, more stable ice, a finding that implies significantly lower ice viscosity and reduced basal sliding than previously assumed, thereby offering a pathway to improve future ice-sheet mass balance projections.
This paper introduces a physics-constrained machine learning framework using genetic programming to automatically discover interpretable, closed-form equations for multimodal water retention curves directly from experimental data, overcoming the limitations of traditional superposition-based models.
GreenPhase is an efficient, interpretable, and sustainable deep-learning model based on the Green Learning framework that achieves state-of-the-art earthquake detection and phase picking performance on the STEAD dataset while reducing computational costs by approximately 83% through its unique feed-forward, multi-resolution architecture that eliminates backpropagation.
This study analyzes six years of cosmic muon rate data from a high-latitude station in Svalbard, revealing that seasonal atmospheric temperature variations drive a pronounced annual modulation and demonstrating the effectiveness of tailored temperature correction techniques compared to lower-latitude measurements.
This study demonstrates that radiative-dynamical feedbacks in a dry Martian atmospheric model enable locally released IR-active carbon and metal particles to self-loft and disperse globally, thereby supporting the feasibility of engineered-aerosol warming on Mars despite remaining uncertainties regarding agglomeration and water cycle interactions.
This study demonstrates that Superhydrophobic Sand (SHS) mulch significantly reduces soil evaporation in arid regions by shifting the process from a temperature-controlled regime to a diffusion-limited one, with effectiveness governed by mulch thickness and soil thermal properties.
This study characterizes the 2017 Mukundpura CM2 carbonaceous chondrite from Rajasthan, India, using high-resolution microscopy and Raman spectroscopy to confirm the presence of 3–5 nm nanocrystalline diamonds alongside graphitic carbon and abundant iridium, offering insights into stellar evolution and the geological impact anomalies associated with mass extinctions.
This study reconstructs and analyzes the first four ground-level enhancements (GLEs) from the 1940s by digitizing forgotten cosmic-ray data, revealing distinct temporal evolution patterns and spectral hardness characteristics for these historically missing events.