Global deep-learning weather prediction models have recently been shown to
produce forecasts that rival those from physics-based models run at operational
centers. It is unclear whether these models have encoded atmospheric dynamics,
or simply pattern matching that produces the smallest forecast error. Answering
this question is crucial to establishing the utility of these models as tools
for basic science. Here we subject one such model, Pangu-weather, to a set of
four classical dynamical experiments that do not resemble the model training
data. Localized perturbations to the model output and the initial conditions
are added to steady time-averaged conditions, to assess the propagation speed
and structural evolution of signals away from the local source. Perturbing the
model physics by adding a steady tropical heat source results in a classical
Matsuno--Gill response near the heating, and planetary waves that radiate into
the extratropics. A localized disturbance on the winter-averaged North Pacific
jet stream produces realistic extratropical cyclones and fronts, including the
spontaneous emergence of polar lows. Perturbing the 500hPa height field alone
yields adjustment from a state of rest to one of wind--pressure balance over ~6
hours. Localized subtropical low pressure systems produce Atlantic hurricanes,
provided the initial amplitude exceeds about 5 hPa, and setting the initial
humidity to zero eliminates hurricane development. We conclude that the model
encodes realistic physics in all experiments, and suggest it can be used as a
tool for rapidly testing ideas before using expensive physics-based models.

全球深度学习天气预测模型与物理模型的预测效果相媲美，但这些模型是否包含大气动力学仍不清楚，本文通过四个经典的动力学实验对 Pangu-weather 模型进行检验，发现模型在所有实验中都表现出现实的物理特性，因此认为它可以作为一种快速测试想法的工具而替代昂贵的物理模型。

深度学习天气预测模型的动力测试

Dynamical Tests of a Deep-Learning Weather Prediction Model

In this paper, we present Pangu-Weather, a deep learning based system for
fast and accurate global weather forecast. For this purpose, we establish a
data-driven environment by downloading $43$ years of hourly global weather data
from the 5th generation of ECMWF reanalysis (ERA5) data and train a few deep
neural networks with about $256$ million parameters in total. The spatial
resolution of forecast is $0.25^\circ\times0.25^\circ$, comparable to the ECMWF
Integrated Forecast Systems (IFS). More importantly, for the first time, an
AI-based method outperforms state-of-the-art numerical weather prediction (NWP)
methods in terms of accuracy (latitude-weighted RMSE and ACC) of all factors
(e.g., geopotential, specific humidity, wind speed, temperature, etc.) and in
all time ranges (from one hour to one week). There are two key strategies to
improve the prediction accuracy: (i) designing a 3D Earth Specific Transformer
(3DEST) architecture that formulates the height (pressure level) information
into cubic data, and (ii) applying a hierarchical temporal aggregation
algorithm to alleviate cumulative forecast errors. In deterministic forecast,
Pangu-Weather shows great advantages for short to medium-range forecast (i.e.,
forecast time ranges from one hour to one week). Pangu-Weather supports a wide
range of downstream forecast scenarios, including extreme weather forecast
(e.g., tropical cyclone tracking) and large-member ensemble forecast in
real-time. Pangu-Weather not only ends the debate on whether AI-based methods
can surpass conventional NWP methods, but also reveals novel directions for
improving deep learning weather forecast systems.

本文提出基于深度学习的 Pangu-Weather 系统，用于快速而准确的全球天气预报。通过大量数据和深度神经网络，提高了预报精度，成功地将 AI 方法的预报准确度超越了传统的数值天气预报方法，为未来深度学习天气预报系统的改进提供了新方向。