Results 151 to 160 of about 15,475 (294)

A large-scale dataset for training deep learning segmentation and tracking of extreme weather. [PDF]

Sci Data
Kim S, Graubner A, Kapp-Schwoerer L, Kashinath K, Schindler K.   +4 more
europepmc   +1 more source

Influence of Model Resolution on the Representation and Contribution of Antarctic Intermediate Water to Meridional Salt Transport in the Atlantic

Journal of Geophysical Research: Oceans, Volume 131, Issue 4, April 2026.
Abstract This study investigates the influence of model resolution on the representation of Antarctic Intermediate Water (AAIW) hydrographic properties and the role of eddies in the meridional transport of salt using three simulations from the HadGEM3‐GC3.1 HighResMIP family with varying ocean resolutions: the 1° LL model (parameterized eddies), the 1 ...
Ophélie Meuriot, Camille Lique, Yves Plancherel, Anne Marie Treguier   +3 more
wiley   +1 more source

Projected impacts of climate change on malaria in Africa. [PDF]

Nature
Symons TL, Moran A, Balzarolo A, Vargas C, Robertson M, Lubinda J, Saddler A, McPhail M, Harris J, Rozier J, Browne A, Amratia P, Bertozzi-Villa A, Bhatt S, Cameron E, Golding N, Smith DL, Noor AM, Rumisha SF, Palmer MD, Weiss DJ, Desai N, Potere D, Sukitsch N, Woods W, Gething PW.   +25 more
europepmc   +1 more source

Tidal Modulation of Waves Around the Aleutian Islands

Journal of Geophysical Research: Oceans, Volume 131, Issue 4, April 2026.
Abstract Forming a critical part of the North Pacific Great Circle Route, the narrow passes of the Aleutian Islands are considered among the United States' most treacherous waters for maritime navigation. This study investigates the tidal modulation of surface gravity waves throughout the archipelago using a high‐resolution, unstructured‐grid, coupled ...
F. Cassalho, A. L. Kurapov, S. Moghimi, S. M. Durski, J. Y. Zhang, A. Abdolali, B. Khazaei, Y. Sun, F. Ye, E. Myers   +9 more
wiley   +1 more source

Fatigue Life Analysis of Cyclone Separator Group Structure in a Reactor Device. [PDF]

Materials (Basel)
Shan Y, Sun J, Zhu X, Tian Y, Zhou J, Ding Y, Ding B, Du J, Zhang M.   +8 more
europepmc   +1 more source

Estimating Mediterranean Cyclone Activity via Explainable Machine Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Intense cyclones in the Mediterranean drive most of the region's rainfall and wind‐wave extremes, exerting a significant socio‐economic impact. Currently there are no established analytical tools for estimating Mediterranean cyclone activity from climatological fields.
Guido Ascenso, Luca Proserpio, Leone Cavicchia, Enrico Scoccimarro, Matteo Giuliani, Andrea Castelletti   +5 more
wiley   +1 more source

Intelligent risk assessment and early warning for human-machine-environment coupling in coal preparation plants. [PDF]

Sci Rep
Zhao Y, Hu Y, Shi Q.
europepmc   +1 more source

Intelligent Shanghai Typhoon Model (ISTM): A Diffusion‐Based Downscaling Emulator From AIFS to Kilometer‐Scale Regional Reanalysis for Typhoon Forecasts

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Artificial‐intelligence weather prediction models have recently surpassed numerical models in large‐scale skill, but they still systematically underestimate typhoon intensity due to their reliance on coarse‐resolution training data from ERA5. To overcome this limitation, we constructed a bespoke 9‐km high‐resolution typhoon reanalysis (HiRes ...
Zeyi Niu, Wei Huang, Sirong Huang, Bo Qin, Mengqi Yang, Haofei Sun, Zhaoyang Huo, Haixia Xiao, Kairui Feng   +8 more
wiley   +1 more source

Multi-heat keypoint incorporation in deep learning model to tropical cyclone centering and intensity classifying from geostationary satellite images. [PDF]

Sci Rep
Do TH, Phan ST, Du DT, Dang DQ, Mai KH, Hole LR.   +5 more
europepmc   +1 more source

Deep Learning of Systematic Ocean Model Errors in a Coupled GCM From Data Assimilation Increments

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 4, April 2026.
Abstract We present a novel, data‐driven approach to predict systematic model errors in the ocean component of a coupled general circulation model leveraging deep learning and data assimilation. We examine the skill of the proposed scheme in learning systematic model errors, including their spatial patterns, variance, scales, and test its sensitivity ...
Tarun Verma, F. Lu, A. Adcroft, L. Zanna, A. Gnanadesikan   +4 more
wiley   +1 more source