A lower bound for prediction uncertainty in nowcasting/forecasting models

J. Clayton Kerce; Francois Vandenberghe

doi:10.1117/12.740665

12 September 2007 A lower bound for prediction uncertainty in nowcasting/forecasting models

Proceedings Volume 6685, Assimilation of Remote Sensing and In Situ Data in Modern Numerical Weather and Environmental Prediction Models; 668505 (2007) https://doi.org/10.1117/12.740665
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

Abstract

We introduce a formalism for computing the Cramer-Rao lower bound (CRLB) for a general dynamical system, develop an approach to bounding the process noise for a general dynamical system, and discuss the application of this formalism in the context of a prototypical forecasting model. This model consists of a simple transport diffusion process with assimilation updates based on point source measurements. We investigate the use of Krylov subspace techniques for efficient computation of two point correlation functions, and the use of this technique in generating a coarse-grained state covariance.

Citation Download Citation

J. Clayton Kerce and Francois Vandenberghe "A lower bound for prediction uncertainty in nowcasting/forecasting models", Proc. SPIE 6685, Assimilation of Remote Sensing and In Situ Data in Modern Numerical Weather and Environmental Prediction Models, 668505 (12 September 2007); https://doi.org/10.1117/12.740665

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