The weather forecasting system is important for science and society, and
significant achievements have been made in applying artificial intelligence
(AI) to medium-range weather forecasting. However, existing AI-based weather
forecasting models still rely on analysis or reanalysis products from the
traditional numerical weather prediction (NWP) systems as initial conditions
for making predictions, preventing them from being fully independent systems.
As a crucial component of an end-to-end global weather forecasting system, data
assimilation is vital in generating initial states for forecasting. In this
paper, we present an AI-based data assimilation model, i.e., Adas, for global
weather variables, which learns to generate the analysis from the background
and sparse observations. Different from existing assimilation methods, Adas
employs the gated convolution module to handle sparse observations and the
gated cross-attention module for capturing the interactions between
observations and background efficiently, which are guided by the confidence
matrix to represent the availability and quality of observations. Then, we
combine Adas with the advanced AI-based weather forecasting model (i.e.,
FengWu) and construct the first end-to-end AI-based global weather forecasting
system: FengWu-Adas. Experiments demonstrate that Adas can assimilate the
simulated global observations with the AI-generated background through a
one-year simulation and generate high-quality analysis stably in a cyclic
manner. Based on the generated analysis, FengWu-Adas exhibits skillful
performance and outperforms the Integrated Forecasting System (IFS) in weather
forecasting over seven days.

全球天气预报系统中关键的数据同化模型 Adas 与 AI 天气预报模型 FengWu 的组合系统 FengWu-Adas 表现出高水平的预报技能，在七天的天气预报中超过了集成预报系统 (IFS)。

走向一种全端到端人工智能驱动的全球天气预报系统

Towards an End-to-End Artificial Intelligence Driven Global Weather  Forecasting System

Extreme weather amplified by climate change is causing increasingly
devastating impacts across the globe. The current use of physics-based
numerical weather prediction (NWP) limits accuracy due to high computational
cost and strict time-to-solution limits. We report that a data-driven deep
learning Earth system emulator, FourCastNet, can predict global weather and
generate medium-range forecasts five orders-of-magnitude faster than NWP while
approaching state-of-the-art accuracy. FourCast-Net is optimized and scales
efficiently on three supercomputing systems: Selene, Perlmutter, and JUWELS
Booster up to 3,808 NVIDIA A100 GPUs, attaining 140.8 petaFLOPS in mixed
precision (11.9%of peak at that scale). The time-to-solution for training
FourCastNet measured on JUWELS Booster on 3,072GPUs is 67.4minutes, resulting
in an 80,000times faster time-to-solution relative to state-of-the-art NWP, in
inference. FourCastNet produces accurate instantaneous weather predictions for
a week in advance, enables enormous ensembles that better capture weather
extremes, and supports higher global forecast resolutions.

该论文基于数据驱动的深度学习技术，提出了 FourCastNet 这一可以比当前基于物理的数值天气预报快 80000 倍并且准确性优于状态 -，预测全球天气、产生中程预报的系统。同时，FourCastNet 还可以生成准确的即时天气预测，提高了全球预测的分辨率。

FourCastNet: 使用自适应傅里叶神经算子加速全球高分辨率天气预报

FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators

We present a data-driven approach for forecasting global weather using graph
neural networks. The system learns to step forward the current 3D atmospheric
state by six hours, and multiple steps are chained together to produce skillful
forecasts going out several days into the future. The underlying model is
trained on reanalysis data from ERA5 or forecast data from GFS. Test
performance on metrics such as Z500 (geopotential height) and T850
(temperature) improves upon previous data-driven approaches and is comparable
to operational, full-resolution, physical models from GFS and ECMWF, at least
when evaluated on 1-degree scales and when using reanalysis initial conditions.
We also show results from connecting this data-driven model to live,
operational forecasts from GFS.

使用图神经网络的数据驱动方法预测全球天气，通过学习当前 3D 大气状态的六小时步长以及多步连接，可以产生数天后的精准预报，测试表现优于以前的数据驱动方法，并且与 GFS 和 ECMWF 的物理模型的全分辨率相当可比。