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Accurate weather forecasting holds significant importance to humanactivities. 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 poordata utilization and high computational costs, while DLP can learn weatherpatterns from vast amounts of data directly but struggles to incorporatephysical laws. Both paradigms possess their respective strengths andweaknesses, and are incompatible, because physical laws adopted in NWP describethe relationship between coordinates and meteorological variables, while DLPdirectly learns the relationships between meteorological variables withoutconsideration of coordinates. To address these problems, we introduce theDeepPhysiNet framework, incorporating physical laws into deep learning modelsfor accurate and continuous weather system modeling. First, we constructphysics networks based on multilayer perceptrons (MLPs) for individualmeteorological variable, such as temperature, pressure, and wind speed. Physicsnetworks establish relationships between variables and coordinates by takingcoordinates as input and producing variable values as output. The physical lawsin the form of Partial Differential Equations (PDEs) can be incorporated as apart of loss function. Next, we construct hyper-networks based on deep learningmethods to directly learn weather patterns from a large amount ofmeteorological data. The output of hyper-networks constitutes a part of theweights for the physics networks. Experimental results demonstrate that, uponsuccessful integration of physical laws, DeepPhysiNet can accomplish multipletasks simultaneously, not only enhancing forecast accuracy but also obtainingcontinuous spatiotemporal resolution results, which is unattainable by eitherthe NWP or DLP.

DeepPhysiNet: Bridging Deep Learning and Atmospheric Physics for Accurate and Continuous Weather Modeling