Not exact matches
After a painstaking analysis that modeled all known sources of acceleration for Juno, including the minute contributions from sunlight warming the spacecraft, Iess's team found a large north - south asymmetry in Jupiter's gravitational field — a clear sign of material
flowing beneath the cloud tops on
deep atmospheric winds.
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.
However, freshened polar surface waters act as a barrier to
atmospheric transfer, diverting products into the
deep return
flow.»