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.
Accurate weather forecasting is crucial for various human activities, from agriculture to transportation. The article introduces two existing paradigms for weather forecasting: Numerical Weather Prediction (NWP) and Deep Learning-based Prediction (DLP). Each paradigm comes with its own strengths and weaknesses.
NWP relies on atmospheric physics to model weather patterns. However, it faces challenges in effectively utilizing data and computational costs. On the other hand, DLP can learn weather patterns directly from large datasets, but it has difficulty incorporating physical laws. The incompatibility of these two paradigms arises because NWP incorporates physical laws that describe the relationship between coordinates and meteorological variables, while DLP focuses solely on learning the relationships between meteorological variables without considering coordinates.
To address these challenges and limitations, the DeepPhysiNet framework is introduced. This framework combines deep learning models with the incorporation of physical laws to improve weather system modeling accuracy and continuity. It consists of two components: physics networks and hyper-networks.
Physics networks, based on multilayer perceptrons (MLPs), are constructed for individual meteorological variables such as temperature, pressure, and wind speed. These networks establish relationships between variables and coordinates by taking coordinates as input and generating variable values as output. The incorporation of physical laws in the form of Partial Differential Equations (PDEs) into the loss function further enhances the accuracy of the predictions.
Hyper-networks, based on deep learning methods, are built to directly learn weather patterns from vast amounts of meteorological data. The output of hyper-networks forms a part of the weights for the physics networks, enabling the incorporation of both physical laws and data-driven learning.
Experimental results have shown that the integration of physical laws into the DeepPhysiNet framework enables it to accomplish multiple tasks simultaneously. It not only enhances forecast accuracy but also provides continuous spatiotemporal resolution results. This level of accuracy and resolution is not achievable by either NWP or DLP alone.
The DeepPhysiNet framework highlights the multi-disciplinary nature of weather forecasting, combining concepts from atmospheric physics, deep learning, and mathematics. By incorporating physical laws into deep learning models, it bridges the gap between the two paradigms and overcomes their respective limitations. This advancement in weather forecasting has the potential to significantly improve decision-making in various industries, such as agriculture, transportation, and disaster management.
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