Results 221 to 230 of about 14,931,177 (238)

Dataset of Coastline Positions for Sandy Beaches of England and Wales Derived Using Remote Sensing

Geoscience Data Journal, Volume 13, Issue 4, October 2026.
Two decades (1999–2020) of annual coastline position dataset were generated using COASTSAT toolkit for sandy beaches of England and Wales. The dataset consists of annual coastline position coordinates in the format of GeoJSON and Tabular files. ABSTRACT This article presents a dataset of coastline positions, comprising annual sandy coastline detections
Pavitra Kumar, Nicoletta Leonardi
wiley   +1 more source

Predictive modeling of Super El Niño through integrated local and global climate signals. [PDF]

Sci Rep
Yoon CH, Park J, Cheoun MK.
europepmc   +1 more source

QG‐Python: A Reproducible 1.5‐Layer Quasi‐Geostrophic Modelling Service With Direct Sparse Solvers, Spectral Stability Diagnostics and Filter Sensitivity Data

Geoscience Data Journal, Volume 13, Issue 4, October 2026.
QG‐Python is an open, reproducible 1.5‐layer quasi‐geostrophic modelling service whose published diagnostics show that the classical von Neumann bound underestimates the true Leapfrog stability limit by a factor of 13.7, and that the Robert–Asselin–Williams filter reduces kinetic energy and enstrophy biases from ~21% and ~45% to under 4%.
Elias D. Nino‐Ruiz
wiley   +1 more source

Sea surface temperature modulates El Niño and La Niña driven leptospirosis patterns: Evidence from causal machine learning in Colombia. [PDF]

PLOS Glob Public Health
Gutiérrez JD, Wilches-Vega J, Galvis-Serrano F, Parada-Jurado H, Cortes-Ramírez J.   +4 more
europepmc   +1 more source

Rapid Estimation of Global Sea Surface Temperatures From Sparse Streaming In Situ Observations

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Reconstructing high‐resolution sea surface temperatures (SST) from staggered SST measurements is essential for analyzing earth system processes. However, when SST measurements are sparse, the resulting inferred SST fields are rather inaccurate.
Cassidy All, Kevin Ho, Maya Magnuski, Christopher Nicolaides, Louisa B. Ebby, Mohammad Farazmand   +5 more
wiley   +1 more source

Abrupt shift of El Niño periodicity under CO₂ mitigation. [PDF]

Proc Natl Acad Sci U S A
Iwakiri T, Kug JS, Jin FF, Zhao S, An SI, Kim GI, Park D.   +6 more
europepmc   +1 more source

AI Emulation of Stochastic Sudden Stratospheric Warming With Interpretable Latent Structure

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Rare weather regime transitions pose a challenge for data‐driven modeling due to class imbalance. In this study, we develop a probabilistic deep learning emulator for a prototypical system with regime transitions, the stochastic Holton–Mass (HM) model of stratospheric variability, and analyze the structure of its learned latent space.
C. Daniel Boscu, Daniel Hernandez, Fabio Alvarez Ventura, Justin Finkel, Ashesh Chattopadhyay, Pedram Hassanzadeh, Dorian S. Abbot   +6 more
wiley   +1 more source

The El Niño Southern Oscillation and the salinity of land and water in the United States. [PDF]

PLoS One
Limodio N, De Marco F, Dal Barco I.
europepmc   +1 more source

Site‐Specific Post‐Processing of Spectral Wave Forecast by Learning From Buoy Measurements

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Operational wave prediction systems provide critical information across a range of marine industries and for public safety. Yet operational numerical wave forecasts can exhibit systematic biases, such as in wave height and arrival time of swell events.
Arthur Filoche, Jeff Hansen, Kevin Vinsen, Travis Dawson   +3 more
wiley   +1 more source

North Pacific meridional mode has larger impacts on El Niño evolution than the March Madden-Julian Oscillation. [PDF]

Sci Adv
Liang Y, Xie SP, Fedorov A, Yeager SG.
europepmc   +1 more source