DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models (2411.10010v2)

Abstract: Numerical weather prediction (NWP) centers around the world operate a variety of NWP models. In addition, recent advances in AI-driven NWP models have further increased the availability of NWP outputs. While this expansion holds the potential to improve forecast accuracy, it raises a critical question: which prediction is the most plausible? If the NWP models have comparable accuracy, it is impossible to determine in advance which one is the best. Traditional approaches, such as ensemble or weighted averaging, combine multiple NWP outputs to produce a single forecast with improved accuracy. However, they often result in meteorologically unrealistic and uninterpretable outputs, such as the splitting of tropical cyclone centers or frontal boundaries into multiple distinct systems. To address this issue, we propose DeepMedcast, a deep learning method that generates intermediate forecasts between two or more NWP outputs. Unlike averaging, DeepMedcast provides predictions in which meteorologically significant features -- such as the locations of tropical cyclones, extratropical cyclones, fronts, and shear lines -- approximately align with the arithmetic mean of the corresponding features predicted by the input NWP models, without distorting meteorological structures. We demonstrate the capability of DeepMedcast through case studies and verification results, showing that it produces realistic and interpretable forecasts with higher accuracy than the input NWP models. By providing plausible intermediate forecasts, DeepMedcast can significantly contribute to the efficiency and standardization of operational forecasting tasks, including general, marine, and aviation forecasts.

• The paper introduces DeepMedcast, a deep neural network method that generates intermediate forecasts by integrating outputs from multiple NWP models.
• It employs a U-Net architecture trained on consecutive lead times to maintain forecast coherence and avoid blurring of critical meteorological features.
• Case studies on typhoons, frontal systems, and low-pressure areas demonstrate its ability to reconcile diverse predictions while preserving essential weather structures.

DeepMedcast: Advancing Intermediate Weather Forecasting through Deep Learning

The paper "DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models" elucidates a novel approach to enhance numerical weather prediction (NWP) by integrating multiple model outputs using a deep learning technique. This work is situated in a context where operational centers globally employ various NWP models to improve forecast accuracy. The proliferation of AI-driven models further enriches the diversity of prediction outputs, which, while beneficial, poses the challenge of selecting the most reliable forecast for specific conditions.

Methodology and Framework

The primary contribution of this paper is the introduction of DeepMedcast, a deep neural network (DNN)-based method designed to produce intermediate forecasts—termed "medcasts"—between two or more NWP models. Traditional methods like ensemble averaging often lead to unrealistic field outputs by smoothing critical meteorological features. In contrast, DeepMedcast adeptly maintains the integrity of atmospheric fields, preserving essential structures even around complex systems like cyclones and fronts.

The methodology harnesses a unique training framework whereby DeepMedcast utilizes consecutive forecast lead times from a single NWP model during training. This approach ensures that intermediate predictions remain coherent and free from the blurriness typical of standard averaging practices. The DNN architecture employed is a U-Net, chosen for its efficacy in tasks requiring precise spatial information retention.

Results and Case Studies

The paper demonstrates the efficacy of DeepMedcast through several case studies, which emphasize its ability to generate coherent forecasts in scenarios where traditional methods fall short.

1. Typhoon Forecasts: In cases involving typhoon positions, DeepMedcast successfully reconciles differing predictions from various models, producing a single, coherent forecast without degrading the intensity of critical features like central pressure and wind speeds.
2. Frontal Positions: The method accurately predicts the intermediate positions of fronts, capturing the associated wind shear and thermal boundaries in a manner that averaged outputs cannot achieve.
3. Low-Pressure Systems: DeepMedcast is shown to reconcile significant discrepancies in positioning and intensity forecasts of low-pressure systems, providing consistent outputs aligning with meteorological expectations.
4. Multi-Model Integration: The method's robustness extends to integrating more than two models, as demonstrated in the typhoon KHANUN case paper, providing a unified forecast from four distinct models.

Implications and Future Directions

The findings suggest that DeepMedcast offers substantial improvements in terms of maintaining meteorological realism and providing explainable forecasts, addressing a critical gap in current NWP practices. Its applicability without the need for model-specific retraining presents a significant operational advantage, offering potential cost savings and efficiency improvements in forecast production.

Looking forward, the research identifies several avenues for enhancing DeepMedcast, such as incorporating advanced neural architectures like Transformers and diffusion models to improve representational accuracy further. Additional work to integrate precipitation forecasts within the DeepMedcast framework is also anticipated, considering precipitation's critical role in weather forecasting.

In summary, this paper contributes meaningfully to the field of meteorology by presenting a sophisticated deep learning approach that effectively integrates diverse NWP outputs. The introduction of the medcast concept may prove critical as weather prediction continues to evolve with the integration of advanced AI methodologies.