A Physics-Incorporated Deep Learning Framework for Parameterization of
Atmospheric Radiative Transfer
Abstract
\justifying The atmospheric radiative transfer
calculations are among the most time-consuming components of the
numerical weather prediction (NWP) models. Deep learning (DL) models
have recently been increasingly applied to accelerate radiative transfer
modeling. Besides, a physical relationship exists between the output
variables, including fluxes and heating rate profiles. Integration of
such physical laws in DL models is crucial for the consistency and
credibility of the DL-based parameterizations. Therefore, we propose a
physics-incorporated framework for the radiative transfer DL model, in
which the physical relationship between fluxes and heating rates is
encoded as a layer of the network so that the energy conservation can be
satisfied. It is also found that the prediction accuracy was improved
with the physic-incorporated layer. In addition, we trained and compared
various types of deep learning model architectures, including fully
connected (FC) neural networks (NNs), convolutional-based NNs (CNNs),
bidirectional recurrent-based NNs (RNNs), transformer-based NNs, and
neural operator networks, respectively. The offline evaluation
demonstrates that bidirectional RNNs, transformer-based NNs, and neural
operator networks significantly outperform the FC NNs and CNNs due to
their capability of global perception. A global perspective of an entire
atmospheric column is essential and suitable for radiative transfer
modeling as the changes in atmospheric components of one layer/level
have both local and global impacts on radiation along the entire
vertical column. Furthermore, the bidirectional RNNs achieve the best
performance as they can extract information from both upward and
downward directions, similar to the radiative transfer processes in the
atmosphere.