Modern deep learning techniques, which mimic traditional numerical weather
prediction (NWP) models and are derived from global atmospheric reanalysis
data, have caused a significant revolution within a few years. In this new
paradigm, our research introduces a novel strategy that deviates from the
common dependence on high-resolution data, which is often constrained by
computational resources, and instead utilizes low-resolution data (2.5 degrees)
for global weather prediction and climate data analysis. Our main focus is
evaluating data-driven weather prediction (DDWP) frameworks, specifically
addressing sample size adequacy, structural improvements to the model, and the
ability of climate data to represent current climatic trends. By using the
Adaptive Fourier Neural Operator (AFNO) model via FourCastNet and a proposed
time-sliding method to inflate the dataset of the ECMWF Reanalysis v5 (ERA5),
this paper improves on conventional approaches by adding more variables and a
novel approach to data augmentation and processing. Our findings reveal that
despite the lower resolution, the proposed approach demonstrates considerable
accuracy in predicting atmospheric conditions, effectively rivaling
higher-resolution models. Furthermore, the study confirms the model's
proficiency in reflecting current climate trends and its potential in
predicting future climatic events, underscoring its utility in climate change
strategies. This research marks a pivotal step in the realm of meteorological
forecasting, showcasing the feasibility of lower-resolution data in producing
reliable predictions and opening avenues for more accessible and inclusive
climate modeling. The insights gleaned from this study not only contribute to
the advancement of climate science but also lay the groundwork for future
innovations in the field.

该研究通过使用自适应傅立叶神经算子 (AFNO) 模型以及提出的时间滑动方法对 ECMWF 重新分析 v5 (ERA5) 数据集进行扩充，采用低分辨率数据开创性地改进了现有的气象预测方法，从而提高了大气状况预测的准确性，并证实了该模型反映当前气候趋势的能力和预测未来气候事件的潜力，为气候建模领域提供了更具可行性和包容性的路径。

推进数据驱动的天气预报: ERA5 的滑动时间数据增强

Advancing Data-driven Weather Forecasting: Time-Sliding Data  Augmentation of ERA5

Data-driven weather prediction (DDWP) models are increasingly becoming
popular for weather forecasting. However, while operational weather forecasts
predict a wide variety of weather variables, DDWPs currently forecast a
specific set of key prognostic variables. Non-prognostic ("diagnostic")
variables are sometimes modeled separately as dependent variables of the
prognostic variables (c.f. FourCastNet), or by including the diagnostic
variable as a target in the DDWP. However, the cost of training and deploying
bespoke models for each diagnostic variable can increase dramatically with more
diagnostic variables, and limit the operational use of such models. Likewise,
retraining an entire DDWP each time a new diagnostic variable is added is also
cost-prohibitive. We present an two-stage approach that allows new diagnostic
variables to be added to an end-to-end DDWP model without the expensive
retraining. In the first stage, we train an autoencoder that learns to embed
prognostic variables into a latent space. In the second stage, the autoencoder
is frozen and "downstream" models are trained to predict diagnostic variables
using only the latent representations of prognostic variables as input. Our
experiments indicate that models trained using the two-stage approach offer
accuracy comparable to training bespoke models, while leading to significant
reduction in resource utilization during training and inference. This approach
allows for new "downstream" models to be developed as needed, without affecting
existing models and thus reducing the friction in operationalizing new models.

使用两阶段方法，我们训练了一个自编码器来将预测变量嵌入到潜在空间，并进一步利用这些嵌入表示进行诊断变量的预测，从而实现了减少资源使用、提高准确性和降低操作新模型的摩擦。