Results 221 to 230 of about 35,760 (258)

Low‐order system for eddy growth in the storm track

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
Reanalysis data for the North Atlantic storm track are used to construct a low‐order model that optimally fits the observed interaction between the mean flow and baroclinic eddies. The resulting model is a certain type of predator–prey model. It explains various properties of the storm track, including why eddies grow linearly with the Eady growth rate,
Maarten H. P. Ambaum, Thomas H. A. Frame
wiley   +1 more source

Reliability measures in knowledge structure theory. [PDF]

open access: yesBr J Math Stat Psychol
de Chiusole D   +3 more
europepmc   +1 more source

Investigating precipitation growth and decay due to orography using radar rainfall observations in Great Britain

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
Precipitation growth and decay due to orography was analyzed using the radar network in Great Britain. Storm direction and speed modulate the intensity of precipitation growth or decay. Windward slopes predominantly exhibit precipitation growth, while leeward areas show precipitation decay.
Chen Li, Miguel Angel Rico‐Ramirez
wiley   +1 more source

Data‐driven emulation of flow‐dependent background‐error variance in variational data assimilation

open access: yesQuarterly Journal of the Royal Meteorological Society, EarlyView.
A machine‐learning approach is developed to emulate flow‐dependent background‐error variance within a variational data assimilation framework. The method reproduces uncertainty information that would normally require a 50‐member ensemble using only five members, while maintaining comparable observation fit and forecast performance.
Wei Pan   +3 more
wiley   +1 more source

VAE‐Based SPC for Botnet Detection in IoT Systems

open access: yesQuality and Reliability Engineering International, EarlyView.
ABSTRACT The rapid proliferation of Internet of Things (IoT) devices has created new security risks, particularly botnet infections. This paper proposes an unsupervised monitoring framework that combines a β$\beta$‐variational autoencoder (VAE) with multivariate statistical process control (SPC) for botnet detection in IoT networks.
Jorge Proaño   +3 more
wiley   +1 more source

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