  Operational weather forecasting models have advanced for decades on both the
explicit numerical solvers and the empirical physical parameterization schemes.
However, the involved high computational costs and uncertainties in these
existing schemes are requiring potential improvements through alternative
machine learning methods. Previous works use a unified model to learn the
dynamics and physics of the atmospheric model. Contrarily, we propose a simple
yet effective machine learning model that learns the horizontal movement in the
dynamical core and vertical movement in the physical parameterization
separately. By replacing the advection with a graph attention network and the
convection with a multi-layer perceptron, our model provides a new and
efficient perspective to simulate the transition of variables in atmospheric
models. We also assess the model's performance over a 5-day iterative
forecasting. Under the same input variables and training methods, our model
outperforms existing data-driven methods with a significantly-reduced number of
parameters with a resolution of 5.625 deg. Overall, this work aims to
contribute to the ongoing efforts that leverage machine learning techniques for
improving both the accuracy and efficiency of global weather forecasting.
