Geospatial and Numerical Methods

Catalog description

Advances in Earth observation—from satellites and airborne sensors to ground-based monitoring networks and the Earth system models—are generating unprecedented four-dimensional (space–time) environmental datasets. This course introduces the geospatial foundations and numerical methods needed to analyze and interpret these data. Topics include geographic coordinate systems and geodesy, map projections and spatial distortions, and the characteristics and scales of satellite, airborne, and in situ observations, followed by quantitative methods for geospatial analysis such as interpolation and gridding, spatial and spectral filtering, multivariate analysis, and handling data gaps and uncertainty. The course then develops numerical techniques for modeling environmental processes, including curve fitting, ordinary and partial differential equations, advection–diffusion systems, numerical integration, stability and convergence, root finding, linear algebra and inverse methods, and stochastic modeling. Hands-on exercises use open-source geospatial tools (e.g., GMT, QGIS, Google Earth) and Python or MATLAB to build practical skills in geospatial analysis and data-driven Earth system applications.