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

📄 arXiv: 2401.04125v1 📥 PDF

作者: Wenyuan Li, Zili Liu, Keyan Chen, Hao Chen, Shunlin Liang, Zhengxia Zou, Zhenwei Shi

分类: physics.ao-ph, cs.AI, cs.LG

发布日期: 2024-01-04

备注: 18 pages, 9 figures

💡 一句话要点

提出DeepPhysiNet以解决天气建模中物理法则与深度学习结合问题

🎯 匹配领域: 支柱八：物理动画 (Physics-based Animation)

关键词: 天气建模 深度学习 物理法则 数值天气预报 气象预测 多层感知器 偏微分方程 超网络

📋 核心要点

  1. 现有的数值天气预报和深度学习预测方法各有优缺点，难以兼容，导致天气建模效果不佳。
  2. DeepPhysiNet框架通过将物理法则融入深度学习模型，构建物理网络和超网络，实现天气建模的创新。
  3. 实验结果表明，DeepPhysiNet在准确性和时空分辨率上均优于传统的NWP和DLP方法。

📝 摘要（中文）

准确的天气预报对人类活动至关重要。目前，天气预报主要有数值天气预报（NWP）和基于深度学习的预测（DLP）两种范式。NWP利用大气物理进行建模，但数据利用率低且计算成本高；而DLP能够直接从大量数据中学习天气模式，但难以融入物理法则。为了解决这些问题，本文提出了DeepPhysiNet框架，将物理法则融入深度学习模型，实现准确且连续的天气系统建模。通过构建基于多层感知器的物理网络和超网络，DeepPhysiNet能够同时完成多项任务，提升预报准确性并获得连续的时空分辨率结果。

🔬 方法详解

问题定义：本文旨在解决天气建模中数值天气预报（NWP）与深度学习预测（DLP）之间的兼容性问题。现有方法在物理法则与数据学习的结合上存在显著不足，导致预报效果不理想。

核心思路：DeepPhysiNet框架通过将物理法则融入深度学习模型，利用物理网络和超网络的结合，既能学习天气模式，又能遵循物理规律，从而实现更准确的天气预报。

技术框架：该框架主要包括两个模块：物理网络和超网络。物理网络基于多层感知器（MLP）构建，输入坐标输出气象变量；超网络则直接从大量气象数据中学习天气模式，输出作为物理网络的权重部分。

关键创新：DeepPhysiNet的创新在于将物理法则以偏微分方程（PDE）的形式融入损失函数中，使得模型在学习过程中能够同时遵循物理规律与数据驱动的学习。

关键设计：在设计上，物理网络的输入为坐标，输出为气象变量；损失函数中包含物理法则的约束，确保模型输出符合物理规律。超网络的结构则采用深度学习方法，能够有效捕捉复杂的天气模式。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，DeepPhysiNet在天气预报准确性上相比传统NWP和DLP方法有显著提升，能够同时完成多项任务，并获得连续的时空分辨率结果，展现出良好的实用性和有效性。

🎯 应用场景

该研究具有广泛的应用潜力，尤其在气象预测、农业气候监测和灾害预警等领域。通过提高天气预报的准确性和连续性，能够为决策提供更可靠的数据支持，进而减少自然灾害带来的损失，提升人类生活质量。

📄 摘要（原文）

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.