Advanced Climate & Geospatial Modelling
From data to decisions—transforming geospatial intelligence into actionable climate policy.
Our in-house modelling framework combines supercomputing capabilities with a Quantum Geoinformatic System (GIS) to deliver high-resolution, near real-time climate intelligence.
- Integrated Earth System Analysis: Simulates interactions between atmospheric chemistry, aerosols, and meteorological variables using dynamic, data-driven approaches.
- High-Resolution Teleconnection Mapping: Assesses Arctic–regional climate linkages, including monsoon systems, at granular spatial and temporal scales.
- Near Real-Time Data Integration: Utilises platforms such as Google Earth Engine and satellite datasets (e.g., SMAP) for continuously updated insights.
- GeoAI & Advanced Analytics: Applies machine learning techniques—including Segmented Augmented Machine Learning (SAM), Random Forest (RF), and Convolutional Neural Networks (CNN)—alongside spatial methods such as Geographic Weighted Regression (GWR), Moran's I, and spatial lag models.
- Climate Driver Integration: Incorporates major global climate phenomena, including El Niño, La Niña, and the Atlantic Meridional Overturning Circulation (AMOC).
