A software framework for construction of process-based stochastic spatio-temporal models and data assimilation

Abstract

Process-based spatio-temporal models simulate changes over time using equations that represent real world processes. They are widely applied in geography and earth science. Software implementation of the model itself and integrating model results with observations through data assimilation are two important steps in the model development cycle. Unlike most software frameworks that provide tools for either implementation of the model or data assimilation, this paper describes a software framework that integrates both steps. The software framework includes generic operations on 2D map and 3D block data that can be combined in a Python script using a framework for time iterations and Monte Carlo simulation. In addition, the framework contains components for data assimilation with the Ensemble Kalman Filter and the Particle filter. Two case studies of distributed hydrological models show how the framework integrates model construction and data assimilation.