WeatherBench: A benchmark dataset for data-driven weather forecasting (2002.00469v3)

Abstract: Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could also be used to predict global weather patterns days in advance. First studies show promise but the lack of a common dataset and evaluation metrics make inter-comparison between studies difficult. Here we present a benchmark dataset for data-driven medium-range weather forecasting, a topic of high scientific interest for atmospheric and computer scientists alike. We provide data derived from the ERA5 archive that has been processed to facilitate the use in machine learning models. We propose simple and clear evaluation metrics which will enable a direct comparison between different methods. Further, we provide baseline scores from simple linear regression techniques, deep learning models, as well as purely physical forecasting models. The dataset is publicly available at https://github.com/pangeo-data/WeatherBench and the companion code is reproducible with tutorials for getting started. We hope that this dataset will accelerate research in data-driven weather forecasting.

• The paper introduces WeatherBench, a standardized dataset processed from ERA5 data to support data-driven weather forecasting research.
• It applies baseline methods using linear regression and CNNs to highlight current challenges compared to traditional numerical weather prediction models.
• The study proposes uniform evaluation metrics, enabling consistent benchmarking and fostering innovation in hybrid weather forecasting models.

WeatherBench: A Benchmark Dataset for Data-Driven Weather Forecasting

The paper "WeatherBench: A benchmark dataset for data-driven weather forecasting" introduces a comprehensive dataset aimed at advancing research in data-driven weather forecasting through machine learning methods. The authors address the pressing need for standardized datasets and evaluation metrics in the domain to facilitate inter-comparison and progress.

Summary and Contributions

The central contribution of this paper is the WeatherBench dataset, derived from the ERA5 reanalysis archive, meticulously processed to be compatible with machine learning models. The dataset encompasses multiple spatial resolutions and a broad range of atmospheric variables essential for medium-range forecasting tasks. Additionally, standard evaluation metrics are proposed to ensure consistent and fair comparisons across different forecasting methods.

The paper supplies baseline benchmarks using linear regression, deep learning models, and conventional numerical weather prediction (NWP) setups. The baselines provide a reference point, highlighting the challenges and opportunities in leveraging AI for weather forecasting.

Numerical Results and Analysis

The paper reports that linear regression models and direct convolutional neural networks (CNNs) achieve varying degrees of prediction skill but remain inferior to traditional physical NWP models at coarser resolutions. Specifically, CNN direct forecasts showed improvement over linear models but were still outperformed by lower-resolution physical models, such as IFS at T42 and T63 resolutions. The operational IFS model sets a high benchmark, difficult to surpass with current purely data-driven approaches.

The success of operational models is reinforced by their superior RMSE and ACC scores, underscoring the complexity and sophistication required to emulate such models without violating physical constraints.

Theoretical and Practical Implications

The advent of WeatherBench opens up significant avenues for researchers aiming to innovate in weather forecasting using machine learning. By providing a common dataset and baseline results, the paper lays the groundwork for evaluating diverse machine learning architectures and computational approaches. This could potentially lead to hybrid models that integrate the strengths of physical and data-driven systems.

Challenges and Future Directions

The authors emphasize several challenges inherent in data-driven weather prediction. These include handling complex atmospheric dynamics, ensuring model interpretability, and efficiently processing large datasets under limited computational resources. Additionally, there is a need to explore ensemble prediction methods to address uncertainties inherent in medium-range forecasting.

Future work could focus on improving model architectures, leveraging transfer learning for mitigating data limitation issues, and exploring promising directions such as probabilistic forecasts. Addressing these challenges could inch data-driven models closer to operational utility in weather forecasting.

Conclusion

The WeatherBench dataset marks a pivotal step towards establishing a foundational framework for AI-driven weather forecasting. Its potential to unify research efforts across atmospheric and data science disciplines presents a promising pathway for achieving more efficient and potentially more accurate weather predictions. As the field evolves, datasets like WeatherBench will be instrumental in facilitating breakthrough innovations in this critical area of paper.

With this benchmark, the authors invite the community to build, share, and explore new methods, thus propelling the field of data-driven weather forecasting into the forefront of AI research applications.