Geospatial state space estimation using an Ensemble Kalman Filter
نویسندگان
چکیده
Incorporating temporal (continuous) data into more common discrete data point geospatial models is necessary for dynamic real time model building. The models are otherwise limited in their use for numerical modelling, simulation and the prediction of climatic states over time. By adopting a Bayesian approach it is shown here to be possible to estimate the dynamic behaviour of unobserved climate patterns forward over time using state-space representations. The recursive state-space Ensemble Kalman Filter (EnKF) is proposed here as a solution to the spatio-temporal modelling problem. This or a more general sequential Monte Carlo method could be used for the estimation procedure but the Ensemble Kalman Filter is observed as producing robust models for the temporal geospatial domain. The EnKF approach is outlined here, with some sample data analysis to illustrate its application and a description of a real-time climate modelling telemetry architecture for data acquisition and model provisioning. Keywords-climate modelling, interpolation, ensemble methods, kalman filter, GIS, Wireless Sensor Networks
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