Recently, the FourCastNet Neural Earth System Model (NESM) has shown
impressive results on predicting various atmospheric variables, trained on the
ERA5 reanalysis dataset. While FourCastNet enjoys quasi-linear time and memory
complexity in sequence length compared to quadratic complexity in vanilla
transformers, training FourCastNet on ERA5 from scratch still requires large
amount of compute resources, which is expensive or even inaccessible to most
researchers. In this work, we will show improved methods that can train
FourCastNet using only 1% of the compute required by the baseline, while
maintaining model performance or par or even better than the baseline.

在本研究中，我们将展示可以仅使用基准模型计算资源的 1% 来训练 FourCastNet，同时保持模型性能甚至比基准模型更好。

FourCastNeXt: 限制计算资源下提升 FourCastNet 训练效果

FourCastNeXt: Improving FourCastNet Training with Limited Compute

FourCastNet, short for Fourier Forecasting Neural Network, is a global
data-driven weather forecasting model that provides accurate short to
medium-range global predictions at $0.25^{\circ}$ resolution. FourCastNet
accurately forecasts high-resolution, fast-timescale variables such as the
surface wind speed, precipitation, and atmospheric water vapor. It has
important implications for planning wind energy resources, predicting extreme
weather events such as tropical cyclones, extra-tropical cyclones, and
atmospheric rivers. FourCastNet matches the forecasting accuracy of the ECMWF
Integrated Forecasting System (IFS), a state-of-the-art Numerical Weather
Prediction (NWP) model, at short lead times for large-scale variables, while
outperforming IFS for variables with complex fine-scale structure, including
precipitation. FourCastNet generates a week-long forecast in less than 2
seconds, orders of magnitude faster than IFS. The speed of FourCastNet enables
the creation of rapid and inexpensive large-ensemble forecasts with thousands
of ensemble-members for improving probabilistic forecasting. We discuss how
data-driven deep learning models such as FourCastNet are a valuable addition to
the meteorology toolkit to aid and augment NWP models.

FourCastNet 是一个全球数据驱动的天气预报模型，利用神经网络技术在短时间内生成高分辨率、快速时间尺度的变量预测，包括风速、天气等，速度快，且适用于扩大概率预测的大范围应用。