Results 241 to 250 of about 32,789 (349)

Future Climate Projections of Hazardous Convective Weather Using an Ensemble of Environment‐Informed, Convection‐Permitting Dynamical Downscaling Simulations

open access: yesJournal of Geophysical Research: Atmospheres, Volume 131, Issue 7, 16 April 2026.
Abstract Convection‐permitting dynamical downscaling (CPDD) allows for an explicit representation of the convective storms that generate tornadoes, hail, severe thunderstorm winds, and locally heavy precipitation. Possible changes in such hazardous convective weather (HCW) due to human‐induced climate change are therefore projected with higher ...
Songning Wang   +4 more
wiley   +1 more source

Generalized Additive Model With Dynamic Coefficients for Spatiotemporal Ozone Predictions

open access: yesEnvironmetrics, Volume 37, Issue 3, April 2026.
ABSTRACT Accurate prediction of surface‐level ozone concentrations is critical for air quality management and public health protection. This study develops a flexible spatiotemporal statistical modeling framework to predict daily mean O3 concentrations across Italy by integrating satellite‐derived ozone estimates with ground‐based observations and high‐
Abdollah Jalilian   +3 more
wiley   +1 more source

Evolving synchronization of the Gulf Stream and Kuroshio-Oyashio Extension in a changing climate. [PDF]

open access: yesSci Adv
Joh Y   +7 more
europepmc   +1 more source

DCENT‐I: A Globally Infilled Extension of the Dynamically Consistent ENsemble of Temperature Dataset

open access: yesGeoscience Data Journal, Volume 13, Issue 2, April 2026.
DCENT‐I infills data gaps in DCENT, producing spatially coherent temperature fields (top) and a slightly higher GMST warming estimate (bottom). Top: December 1877 temperature anomalies (°C; 1961–1990 December baseline) from DCENT (left) and DCENT‐I (right). Bottom: GMST before (DCENT, blue) and after (DCENT‐I, red) infilling.
Duo Chan   +8 more
wiley   +1 more source

GloMarGridding: A Python Toolkit for Flexible Spatial Interpolation in Climate Applications

open access: yesGeoscience Data Journal, Volume 13, Issue 2, April 2026.
Global surface climate datasets contain structural uncertainty that is difficult to attribute to individual processing steps. We present GloMarGridding, a Python package that isolates the spatial interpolation component using Gaussian Process Regression (or kriging) to generate spatially complete fields and uncertainty estimates. The techniques used in
Richard C. Cornes   +6 more
wiley   +1 more source

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