By Dr. Johnny Ruangmei, Joint Chief Executive Officer, Nagaland State Disaster Management Authority (NSDMA), Home Department, Govt. of Nagaland, Nagaland, India 

Abstract

This journal article is written to explore the development of scalable, adaptive computing frameworks for weather forecasting. This journal article proposes a computational framework that leverages deep learning, real-time stream processing, and hybrid AI models to enable intelligent decision-making in weather forecasting. For weather forecasting, it presents transformer-based models optimised for short-term nowcasting using satellite and radar data, blended with physical priors to preserve model robustness and realism.

In the era of data-driven science, environmental stability represents as one of the most pressing global concerns. Precision high-resolution weather forecasting is critical for disaster preparedness, agriculture, aviation, and climate resilience. The domain of precision weather prediction demands real-time, high-accuracy computational systems to translate data into actionable insights.

Objectives of this journal article.

This journal article aims to propose for a unified computational framework for precision scientific computing that:

1. Processes real-time weather data using low-latency, high-throughput neural models.
2. Achieves state-of-the-art accuracy in short-term precision weather nowcasting.

This journal article may spark new thinking and insight in the development of a computational framework for precision weather data forecasting and prediction, utilising advanced deep learning and spatiotemporal modelling techniques to enable high-resolution, real-time atmospheric insights.

Precision weather forecasting using advanced deep learning and spatiotemporal modelling—especially with inputs like cloud thickness—is a cutting-edge field with multiple components. This model is an integrated model with networks of sensors, ground weather data, satellite data, and advanced computing capability.

• The interpolation of multiple data and the convergence of surface and spatial data are the key ingredients of data for its accuracy.
• For the precision weather forecasting (climate modelling, extreme event prediction), the Hybrid Quantum-Classical NWP is used.
• The goal of the experiment is to improve the resolution of rainfall prediction. The method used for this experiment is to replace classical parameterisations (e.g., cloud microphysics) with Quantum Neural Networks (QNNs) and to train on ERA5 reanalysis data (ECMWF). This is a basic data code which can be deployed. Precision weather forecasting aims to deliver highly accurate, localised, and timely predictions by leveraging advanced computational techniques. Unlike traditional regional or global weather models, precision forecasting emphasises accurate, granular insights—often down to the neighbourhood or street level—facilitating actionable outcomes for agriculture, aviation, disaster management, and urban planning.

Conclusion: Future Directions

This journal article points its works to demonstrate higher and accurate computing capability for the development of a computational framework for precision weather data forecasting and prediction, utilizing advanced deep learning and spatiotemporal modelling techniques to enable high-resolution, real-time atmospheric insights.

Disclaimer: The views expressed in this piece are those of the author/s and do not necessarily reflect the views or policies of AIDMI.