Recently, data-driven weather forecasting methods have received significant
attention for surpassing the RMSE performance of traditional NWP (Numerical
Weather Prediction)-based methods. However, data-driven models are tuned to
minimize the loss between forecasted data and ground truths, often using
pixel-wise loss. This can lead to models that produce blurred outputs, which,
despite being significantly different in detail from the actual weather
conditions, still demonstrate low RMSE values. Although evaluation metrics from
the computer vision field, such as PSNR, SSIM, and FVD, can be used, they are
not entirely suitable for weather variables. This is because weather variables
exhibit continuous physical changes over time and lack the distinct boundaries
of objects typically seen in computer vision images. To resolve these issues,
we propose the advection and convection Error (ACE) metric, specifically
designed to assess how well models predict advection and convection, which are
significant atmospheric transfer methods. We have validated the ACE evaluation
metric on the WeatherBench2 and MovingMNIST datasets.

数据驱动的天气预测方法中的评估指标存在问题，我们提出了适用于天气变量的 ACE 指标来解决这些问题。

ACE 指标：准确天气预报的平流和对流评估

ACE Metric: Advection and Convection Evaluation for Accurate Weather  Forecasting

The advent of data-driven weather forecasting models, which learn from
hundreds of terabytes (TB) of reanalysis data, has significantly advanced
forecasting capabilities. However, the substantial costs associated with data
storage and transmission present a major challenge for data providers and
users, affecting resource-constrained researchers and limiting their
accessibility to participate in AI-based meteorological research. To mitigate
this issue, we introduce an efficient neural codec, the Variational Autoencoder
Transformer (VAEformer), for extreme compression of climate data to
significantly reduce data storage cost, making AI-based meteorological research
portable to researchers. Our approach diverges from recent complex neural
codecs by utilizing a low-complexity Auto-Encoder transformer. This encoder
produces a quantized latent representation through variance inference, which
reparameterizes the latent space as a Gaussian distribution. This method
improves the estimation of distributions for cross-entropy coding. Extensive
experiments demonstrate that our VAEformer outperforms existing
state-of-the-art compression methods in the context of climate data. By
applying our VAEformer, we compressed the most popular ERA5 climate dataset
(226 TB) into a new dataset, CRA5 (0.7 TB). This translates to a compression
ratio of over 300 while retaining the dataset's utility for accurate scientific
analysis. Further, downstream experiments show that global weather forecasting
models trained on the compact CRA5 dataset achieve forecasting accuracy
comparable to the model trained on the original dataset. Code, the CRA5
dataset, and the pre-trained model are available at
this https URL

引入高效的神经编解码器 VAEformer 用于极端压缩气候数据，以显著降低数据存储成本，实现便携的基于人工智能的气象研究。该方法在气候数据压缩方面优于现有最先进的方法，并且经验证明其压缩后的数据对于准确的科学分析具有实用性。通过对最流行的 ERA5 气候数据集（226 TB）进行压缩，得到了新的数据集 CRA5（0.7 TB），压缩比超过 300，同时保留了其用于准确科学分析的效用。

CRA5：高效变分转换器在便携式全球气候和天气研究中的 ERA5 极端压缩

CRA5: Extreme Compression of ERA5 for Portable Global Climate and  Weather Research via an Efficient Variational Transformer

WeatherBench 2 is an update to the global, medium-range (1-14 day) weather
forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to
accelerate progress in data-driven weather modeling. WeatherBench 2 consists of
an open-source evaluation framework, publicly available training, ground truth
and baseline data as well as a continuously updated website with the latest
metrics and state-of-the-art models:
this https URL This paper describes the design
principles of the evaluation framework and presents results for current
state-of-the-art physical and data-driven weather models. The metrics are based
on established practices for evaluating weather forecasts at leading
operational weather centers. We define a set of headline scores to provide an
overview of model performance. In addition, we also discuss caveats in the
current evaluation setup and challenges for the future of data-driven weather
forecasting.

WeatherBench 2 是由 Rasp et al. (2020) 提出的全球中程（1-14 天）天气预测基准的更新版本，旨在加速数据驱动的天气模型的进展。本文描述了评估框架的设计原则，并呈现了当前最先进的物理模型和数据驱动的天气模型的结果。评估指标基于在领先的操作性天气中心评估天气预报的已建立实践，我们定义一组引人注目的分数以提供模型性能的概览。此外，我们还讨论了当前评估设置的注意事项以及数据驱动天气预测未来面临的挑战。