Results 271 to 280 of about 322,650 (353)

Assessing the impact of model biases on subseasonal forecast skill

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
Relaxation experiments where the nudging was performed towards bias‐corrected integrations of the same model display significantly improved skill at weeks 3 and 4, particularly in the northern extratropics. This indicates that there is a large potential for improving dynamical subseasonal forecasting skill by improved treatment of model biases.
Frédéric Vitart, Magdalena Balmaseda
wiley   +1 more source

Towards a 'theory of change' for ocean plastics: a socio-oceanography approach to the global challenge of plastic pollution. [PDF]

open access: yesMicroplast nanoplast
Horton AA   +9 more
europepmc   +1 more source

Predictability of North Pacific blocking events: Analogue‐based analysis of historical MIROC6 simulations

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
The predictability of western and eastern North Pacific blocking events is assessed using analogue‐based diagnostics. Eastern blocks exhibit lower predictability, characterized by faster error growth and higher mean logarithmic divergence rates. The study highlights geographical contrasts in blocking stability.
Anupama K. Xavier   +3 more
wiley   +1 more source

Reply to Fleming: Not a gossamer web. [PDF]

open access: yesProc Natl Acad Sci U S A
Santer BD   +4 more
europepmc   +1 more source

Mesoscale and microphysical processes leading to extreme hourly rainfall prior to the merger of two mesoscale convective systems in Central China

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
Short‐term extreme rainfall can be produced by the variation of low‐level warm moist airflow during mesoscale convective systems (MCSs) approaching another. The cold outflow of the rapidly moving MCS intensifies the warm moist airflow in front, enhancing the convergence and ascending motion in the quasi‐stationary MCS.
Xiaoyu Gao   +3 more
wiley   +1 more source

A composite‐loss graph neural network for the multivariate post‐processing of ensemble weather forecasts

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
The dual graph neural network (dualGNN), trained with a composite loss combining the energy score (ES) and variogram score (VS), consistently outperformed models optimized solely for ES or the continuous ranked probability score in the multivariate setting, as well as empirical copula approaches.
Mária Lakatos
wiley   +1 more source

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