arXiv:2606.06348v1 Announce Type: new Abstract: The paradigm of global weather forecasting is rapidly shifting with the emergence of Machine Learning Weather Prediction models (MLWP). While these data-driven architectures demonstrate remarkable global skill, regional benchmarks in the Global South remain scarce, leaving their efficacy in complex, highly convective environments largely unverified. This study evaluates the performance of GraphCast operational against the deterministic ECMWF IFS HRES as baseline across four distinct Brazilian climatic sub-regions. Utilizing a scalable, cloud-nati
The rapid advancement and increased accessibility of Machine Learning Weather Prediction models make their regional validation critical, especially in under-studied complex environments like Brazil.
This study provides crucial regional performance benchmarks for MLWP models, highlighting their practical utility and limitations in diverse global climatic conditions beyond their typical development contexts.
The understanding of GraphCast's efficacy is refined, demonstrating its potential for medium-range forecasting in specific Brazilian conditions while also implicitly suggesting areas for further model improvement and regional adaptation.
- · AI Weather Model Developers
- · Brazil Meteorological Agencies
- · Global South Research Institutions
- · Traditional Numerical Weather Prediction Models (in specific contexts)
MLWP models gain credibility for regional applications, potentially accelerating their adoption in countries with diverse climatic zones.
Increased investment in localized MLWP model training and data infrastructure could follow, especially in developing regions looking to enhance forecasting capabilities.
Improved regional weather forecasting driven by MLWP could lead to better disaster preparedness and resource management in agriculture and energy sectors within those regions.
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Read at arXiv cs.LG