[AAS01-11] Parameterization of Subgrid Momentum Transport Using a Global Cloud Resolving Model and AI
Keywords:global cloud resolving model, Artificial Intelligence, parameterization
A convolutional neural network (CNN) is used as machine learning. Vertical profiles of E and S are estimated by inputting horizontal velocity ( u,v) and ground surface temperature T of the grid considered with surrounding points (total 9 points). To simplify output data, we selected four layers at the altitudes 2, 4, 6, and 8 km and only signs of E and S are considered. Thus, the output profile is classified into the 16 patterns with the binaries of the four layers.
The results show that the accuracy of prediction of the vertical profile pattern of E and S by the learned AI for the test data was about 20 ~ 25%. It was found that the AI parameterization is possible to roughly reproduce the geographical distribution of E and S in the one-month average as a climatological sense.