Results 181 to 190 of about 19,870 (265)

Detection of Eye Occurrence in Sequential Satellite Infrared Imagery and Its Application to Improve Deep Learning‐Based Tropical Cyclone Intensity Estimation

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Tropical cyclone (TC) intensity estimation remains a critical yet challenging task, especially for intense storms that are often underestimated by both conventional and deep learning satellite‐based methods. Among various structural features, eye formation is the most distinct transformation during TC development and is closely linked to rapid
Yi Liu, Jing‐Yi Zhuo, Kekuan Chu, Zhe‐Min Tan   +3 more
wiley   +1 more source

Projection of ENSO using observation-informed deep learning. [PDF]

Nat Commun
Zhu Y, Zhang RH, Wang F, Cai W, Li D, Guan S, Li Y.   +6 more
europepmc   +1 more source

Physically Consistent Tropical Cyclone Monitoring From Infrared Satellite Imagery via Knowledge‐Guided Deep Learning

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Tropical cyclones (TCs) pose significant threats to coastal communities and ecosystems underscoring the need for accurate and timely monitoring of their structure and intensity. Although satellite infrared (IR) imagery provides continuous global coverage, traditional methods for estimating TC parameters, such as center location, intensity, and
Chong Wang, Xiaofeng Li
wiley   +1 more source

Intensified tropical cyclone activity in East Asia during the Maunder (solar) Minimum. [PDF]

Proc Natl Acad Sci U S A
Lin KE, Tseng WL, Lin YS, Lee CY, Lin YH, Hsu HH, Wang PK.   +6 more
europepmc   +1 more source

DeepRec: Global Terrestrial Water Storage Reconstruction Since 1941 Using Spatiotemporal‐Aware Deep Learning Model

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Terrestrial water storage (TWS) plays an important role in describing the Earth system, as water availability is decisive for ecosystems and human development. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its Follow‐On (GRACE‐FO) mission have measured TWS anomalies with unprecedented accuracy, enabling a leap in ...
Luis Q. Gentner, Junyang Gou, Mohammad J. Tourian, Lara Börger, Nico Sneeuw, Benedikt Soja   +5 more
wiley   +1 more source

Robust impact of tropical Pacific SST trends on global and regional circulation in boreal winter. [PDF]

NPJ Clim Atmos Sci
Kang JM, Thomas R, Dunstone N, Shaw TA, Woollings T.   +4 more
europepmc   +1 more source

Overview of the Nephele Perturbed Parameter Ensemble for Aerosol‐Cloud Interactions in E3SMv3

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 2, February 2026.
Abstract Aerosol‐cloud interactions (aci) are the leading source of uncertainty in inferring climate sensitivity from the historical record. Earth system models (ESMs) struggle to represent aci because the processes responsible for these phenomena occur at much finer time and space scales than can be resolved by any ESM.
J. M. Nugent, H. Brown, A. Kirby, D. T. McCoy, G. Allen, T. Aerenson, S. M. Burrows, D. Caulton, J. Fan, Y. Feng, A. Gettelman, J. Griswold, D. B. Jones, L. R. Leung, N. Mahfouz, A. Mikkelsen, J. Mülmenstädt, Y. Qian, Y. Shan, J. Shpund, I. Silber, C. Song, X. Song, H. Wang, M. Wu, S. Xie, M. D. Zelinka, D. Zhang, G. J. Zhang, K. Zhang   +29 more
wiley   +1 more source

CMIP6 models cannot capture long-term forced changes in the tropical Pacific sea surface temperature gradient. [PDF]

Nat Commun
Byrne H, Seager R, Smerdon JE.
europepmc   +1 more source

How Convective Mass Flux Responds to Environmental Humidity

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 2, February 2026.
Abstract Our goal in this study is to characterize the relationship between lower tropospheric environmental humidity and convective mass flux in the tropics. To do so, we have created gridded convective mass flux data sets from five global storm‐resolving models (GSRMs). We have three principal findings.
Seth D. Seidel, Nathan P. Arnold, Brandon Wolding   +2 more
wiley   +1 more source

Increasing extreme winds challenge offshore wind energy resilience. [PDF]

Nat Commun
Zhao Y, Tao Y, Chen Y, Yan J, Zeng Z.
europepmc   +1 more source