Accurate weather forecasting holds significant importance to human
activities. Currently, there are two paradigms for weather forecasting:
Numerical Weather Prediction (NWP) and Deep Learning-based Prediction (DLP).
NWP utilizes atmospheric physics for weather modeling but suffers from poor
data utilization and high computational costs, while DLP can learn weather
patterns from vast amounts of data directly but struggles to incorporate
physical laws. Both paradigms possess their respective strengths and
weaknesses, and are incompatible, because physical laws adopted in NWP describe
the relationship between coordinates and meteorological variables, while DLP
directly learns the relationships between meteorological variables without
consideration of coordinates. To address these problems, we introduce the
DeepPhysiNet framework, incorporating physical laws into deep learning models
for accurate and continuous weather system modeling. First, we construct
physics networks based on multilayer perceptrons (MLPs) for individual
meteorological variable, such as temperature, pressure, and wind speed. Physics
networks establish relationships between variables and coordinates by taking
coordinates as input and producing variable values as output. The physical laws
in the form of Partial Differential Equations (PDEs) can be incorporated as a
part of loss function. Next, we construct hyper-networks based on deep learning
methods to directly learn weather patterns from a large amount of
meteorological data. The output of hyper-networks constitutes a part of the
weights for the physics networks. Experimental results demonstrate that, upon
successful integration of physical laws, DeepPhysiNet can accomplish multiple
tasks simultaneously, not only enhancing forecast accuracy but also obtaining
continuous spatiotemporal resolution results, which is unattainable by either
the NWP or DLP.

将物理定律与深度学习模型相结合的 DeepPhysiNet 框架可以实现准确连续的天气系统建模，增强预测精度并获得连续的时空分辨率结果。