AI-Driven Spatio-Temporal Tracking of Extreme Weather Anomalies in Medium-Range Forecasts

• Problem Statement Identifying and tracking the exact geographic footprints of extreme weather anomalies (such as severe cyclones, heat domes, or cold waves) within massive global Numerical Weather Prediction (NWP) outputs is computationally intensive and heavily reliant on manual interpretation. In medium-range forecasting (3 to 10 days), atmospheric chaos renders traditional deterministic models highly uncertain.

Furthermore, standard deep learning models (like standard CNNs or U-Nets) suffer from spectral smoothing-they tend to 'average out' spatial data, which destroys the extreme amplitudes (the high-intensity peaks of rainfall or wind speed) that forecasters actually need to track. There is a critical gap between broad, coarse 12 km global ensemble datasets and localized, high-fidelity threat tracking.

• Proposed Solution We propose an automated, state-of-the-art AI tracking and downscaling pipeline that shifts the paradigm from manual weather data sorting to automated, physics-informed anomaly tracking.Instead of relying on a single deterministic forecast run, our system directly processes multivariable, 4D Ensemble Prediction Systems (EPS) data.The system uses a two-stage hybrid AI architecture to solve the spectral smoothing problem:First, it utilizes a graph neural network (GNN) to map atmospheric variables onto a spherical mesh,instantly isolating moving anomalies and calculating their trajectory over a 3- to 10-day forecast window.

Second, it pipes this isolated region into a generative diffusion model to perform statistical downscaling. This physics-constrained generative model mathematically derives a hyper-local 5km subgrid impact zone without flattening or blurring the severe amplitudes of the extreme weather event.

• Technical Methodology & Architecture Spherical Anomaly Tracking (Stage 1 GNN): To eliminate the geographic distortions caused by processing the spherical Earth on flat 2D pixel grids, the system maps the 12 km NCMRWF Global Ensemble (NEPS-G) grids directly onto an icosahedral mesh. The message-passing GNN calculates the Extreme Forecast Index (EFI) against a 30-year historical ERA5 baseline distribution to isolate standard deviations and draw a macro-scale temporal bounding box around the anomaly's trajectory.

Amplitude-Preserving Downscaling (Stage 2 Diffusion): The system passes the cropped, macroscale bounding box into a conditional denoising diffusion probabilistic model. Rather than optimizing for mean errors (which blurs peaks), the diffusion model learns the physical relationships between synoptic-scale features and regional topography. It iteratively generates high-resolution, high-amplitude local weather scenarios, downscaling the 12 km grid into a 5 km grid.

Physics-Informed Constraints: To ensure the model remains scientifically accurate, we embed fluid dynamics and thermodynamic conservation laws directly into the neural network's loss function. The model is mathematically penalized if it generates physically impossible weather states (e.g., severe downpours missing corresponding moisture convergence vectors).

• Datasets and Tools ? AI Frameworks: PyTorch / JAX (engineered with custom, physics-guided loss functions),Deep Graph Library (DGL) for icosahedral mesh networks, and Hugging Face Diffusers for generative downscaling.

? Data Wrangling & Geospatial Tools: Xarray and Dask for processing parallelized, multigigabyte 4D NetCDF/GRIB2 arrays; MetPy for physical meteorological equations;

Cartopy for geographical map projections.

? Training & Testing Datasets:

? Baseline: Historical IMDAA / ERA5 reanalysis data to establish the climatological norm.

? Forecast Inputs: Historical NCUM (12 km deterministic) and NEPS-G (12 km global ensemble) datasets containing documented extreme historical events (e.g., Cyclone Amphan, severe North India heatwaves).

• Expected Outcome & Key Deliverables The Tracking Core: A production-ready Spatio-Temporal GNN module that continuously processes global NWP streams to output dynamic, automated 4D bounding boxes around evolving weather threats.

The Downscaling Core: A generative diffusion module capable of ingesting a 12 km resolution anomaly slice and outputting a probabilistically sound, 5 km resolution sub-grid array that retains extreme value amplitudes.

The Visualization & Alert Dashboard: An automated system that translates the mathematical 5 km centroid arrays into clean, geographic visual layers.

The Alerting API: A lightweight, production-ready REST API that programmatically drops a pinpoint coordinate at the core of the severe anomaly and triggers categorized spatial alerts (low, moderate, and severe) across a precise 5 km geographical impact radius.

• Use Cases & Societal Impact Eliminating Alert Fatigue for the NDRF: Current weather alerts are often too broad, covering entire states or districts, which leads to public complacency. This solution allows meteorologists to issue hyper-localized, highly targeted warnings. It changes a generic'heavy rain in the district' alert into a precise 'high risk of flash flooding within your specific 5 km radius in the next 12 hours' alert, empowering first responders to deploy assets perfectly.

Protecting Rural Economies: Grants farming communities a highly accurate, 3- to 10- day lead time regarding localized catastrophic anomalies like sudden frost, hail, or heat domes. This structural foresight lets farmers alter harvesting schedules or apply cropprotection covers, shielding rural livelihoods from sudden climate shocks.

Democratizing Supercomputing Power: Once this hybrid AI pipeline is trained, it processes live inference data on a standard cloud GPU node in seconds, making high-fidelity climate forecasting highly affordable and easily accessible.