Posts by Nhat Vo
ORBIT-2 based Weather and Climate Downscaling and Downscaled Global Forecasts on AMD Instinct
- 08 June 2026
Advances in complex modeling, collection of data, and computational capacity over the past several decades have resulted in accurate numerical weather prediction (NWP) models that are run every day as part of operations of large weather agencies [1]. In the past few years, data-driven AI models have emerged as an alternative to classes of NWP, making predictions at similar (or even slightly better) skill levels [2] [3] [4], with drastically lower computational costs at inference-time, effectively democratizing access to weather forecasting. The AI models have been most successful at a class of synoptic models: global weather prediction models at resolutions of \(10-30~\rm{km}\) with lead times starting from \(6-12~\rm{hours}\), because such models could be trained on \(\sim 40\) years of well-curated data such as the ERA5 reanalysis archives [8] of the European Centre for Medium-Range Weather Forecasts (ECMWF). In previous blog posts, we have discussed inference using SOTA synoptic AI models and training such models.
Utilizing AMD Instinct GPU Accelerators for Weather and Precipitation Forecasting with NeuralGCM
- 19 March 2026
In recent years, the landscape of weather forecasting has evolved tremendously, employing cutting-edge AI technologies to enhance prediction accuracy and speed. In previous blogs, we have demonstrated how to run several state-of-the-art AI weather forecasting models, such as Pangu-Weather, GenCast, and Aurora. Following that, this blog focuses on emerging trends in weather forecasting models, particularly the innovative NeuralGCM, which melds the strengths of General Circulation Models (GCMs) and Machine Learning (ML). We will briefly outline the design of NeuralGCM and its hybrid approach for weather and precipitation forecasting. We will then go through the required environments, installation steps, and the inference process for generating forecasts and creating plots to compare the outputs to the ground truth provided by ERA5 data.