摘要：We used Convolutional Neural Networks (CNNs) to emulate the physics of the atmosphere
in order to bypass solving partial-differential equations (PDEs) explicitly, which cuts down on
computational cost. This is important because in the past, the models used to produce
reliable weather forecasts required a computationally complex calibration.
We let the CNNs learn on a 4-dimensional (longitude x latitude x height x time) geophysical
dataset, with a separate CNN for each height index. After a series of experiments we
conducted following implementation, we found that zero-padding the data, varying time
scale, and changing sample space had little effect on the CNNs’ performance, and that
there was little correlation between training data size and error. We also observed that
given the same training-data size, the CNNs with a more complex configuration (more sets
of weights) actually performed worse.
