«To date, most methods used to
identify atmospheric rivers are based on their water vapor flux or wind speed,» Perez - Munuzuri said.
Not exact matches
The team thinks Lagrangian coherent structures could provide a better way of
identifying and classifying
atmospheric rivers.
In 2016, researchers reported the first use of a deep - learning system to
identify tropical cyclones,
atmospheric rivers and weather fronts: loosely defined features whose identification depends on expert judgement.
Predictive accuracies ranging from 89.4 % to as high as 99.1 % show that trained deep learning neural networks (DNNs) can
identify weather fronts, tropical cyclones, and long narrow air flows that transport water vapor from the tropics called
atmospheric rivers.