Exploring the design space of deep-learning-based weather forecasting systems (2410.07472v1)

Abstract: Despite tremendous progress in developing deep-learning-based weather forecasting systems, their design space, including the impact of different design choices, is yet to be well understood. This paper aims to fill this knowledge gap by systematically analyzing these choices including architecture, problem formulation, pretraining scheme, use of image-based pretrained models, loss functions, noise injection, multi-step inputs, additional static masks, multi-step finetuning (including larger stride models), as well as training on a larger dataset. We study fixed-grid architectures such as UNet, fully convolutional architectures, and transformer-based models, along with grid-invariant architectures, including graph-based and operator-based models. Our results show that fixed-grid architectures outperform grid-invariant architectures, indicating a need for further architectural developments in grid-invariant models such as neural operators. We therefore propose a hybrid system that combines the strong performance of fixed-grid models with the flexibility of grid-invariant architectures. We further show that multi-step fine-tuning is essential for most deep-learning models to work well in practice, which has been a common practice in the past. Pretraining objectives degrade performance in comparison to supervised training, while image-based pretrained models provide useful inductive biases in some cases in comparison to training the model from scratch. Interestingly, we see a strong positive effect of using a larger dataset when training a smaller model as compared to training on a smaller dataset for longer. Larger models, on the other hand, primarily benefit from just an increase in the computational budget. We believe that these results will aid in the design of better weather forecasting systems in the future.

• The paper shows that fixed-grid models like UNet and Segformer outperform grid-invariant architectures in short-term forecasts.
• The paper demonstrates that delta prediction and L1-based loss functions enhance forecasting stability and accuracy during auto-regressive rollouts.
• The paper finds that incorporating inputs like the zenith angle and structured noise improves robustness, while static masks may lead to overfitting.

Overview of Design Choices in Deep-Learning-Based Weather Forecasting

Weather forecasting has leveraged advancements in deep learning, showing potential to outperform traditional numerical models in terms of speed and efficiency. Despite these advancements, many architectural and methodological design decisions remain inadequately justified. The paper "Exploring the design space of deep-learning-based weather forecasting systems" provides a comprehensive evaluation of various design choices, offering valuable insights into their individual contributions to forecasting performance.

Key Findings

The paper assesses a multitude of considerations such as model architectures, pretraining objectives, input formulation, and fine-tuning strategies. Here are some crucial insights derived from the research:

• Architectural Preferences: Fixed-grid models, such as UNet and Segformer, demonstrated superior performance compared to grid-invariant models like graph-based and operator-based frameworks. This suggests that while grid-invariant architectures are appealing for their flexibility, they require further development to match the efficacy of fixed-grid counterparts.
• Delta Prediction: For short-term prediction horizons (e.g., 6 hours), predicting the residual change from the previous state proved more effective than direct predictions. This aligns with the slow temporal evolution of weather states, making delta prediction beneficial.
• Pretraining and Supervised Learning: Supervised training consistently outperformed self-supervised methodologies, including autoencoders. The utility of pre-trained models varied, with architectures like Segformer benefiting more significantly, especially when pre-trained for tasks similar to weather variables.
• Input Design: The inclusion of the zenith angle as an additional input channel consistently enhanced prediction accuracy, providing a temporal context that aids the model. Conversely, the use of multiple past inputs had mixed results, demonstrating potential overfitting in some cases.
• Loss Functions: The research indicates that variants of L1-based losses can be more stable during longer auto-regressive rollouts than the conventional MSE, due to their resilience to outliers.
• Noise Injection: Introducing noise during training, particularly structured noise like Perlin, improved the stability and accuracy of long-term forecasts. This suggests improved robustness from such regularization techniques.
• Additional Static Information: The integration of static masks (e.g., topography and land-sea distinctions) generally hampered performance, indicating potential overfitting or redundancy when combined with other input data.

Methodological Implications

The paper advocates for tailoring design decisions to the specific attributes and requirements of weather forecasting tasks. Fixed-grid models, by virtue of their current superiority, should be prioritized for short-term predictions, while innovations in grid-invariant models are necessary for enhanced flexibility in spatial queries. The investigation into loss functions and input configurations sheds light on possible improvements in model robustness and generalization.

Future Directions

The findings open avenues for further exploration, particularly in the field of grid-invariant architectures and robust, generalized pretraining objectives. Bridging the gap between the inherent variability of weather dynamics and model generalization remains a pertinent challenge. Innovations in incorporating global spatial information without incurring overfitting, perhaps through more sophisticated treatments of input regularization and noise incorporation, are promising.

In conclusion, this systematic exploration enriches our understanding of deep-learning-based weather forecasting systems, providing a foundation for more principled and optimized system designs in future research endeavors.