Forecast error growth: A stochastic differential equation model
- 1Ecole Normale Supérieure, Labo. de Météorologie Dynamique, Geosciences Dept., Paris, France (ghil@lmd.ipsl.fr)
- 2Atmospheric & Oceanic Sciences Dept., University of California at Los Angeles, Los Angeles, CA, USA (ghil@atmos.ucla.edu)
- 3Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, United States (eviatarbach@protonmail.com)
- 4Department of Mathematics, Imperial College London, United Kingdom (d.crisan@imperial.ac.uk)
There is a history of simple error growth models designed to capture the key properties of error growth in operational numerical weather prediction models. We propose here such a scalar model that relies on the previous ones, but captures the effect of small scales on the error growth via additive noise in a nonlinear stochastic differential equation (SDE). We nondimensionalize the equation and study its behavior with respect to the error saturation value, the growth rate of small errors, and the magnitude of noise. We show that the addition of noise can change the curvature of the error growth curve. The SDE model seems to improve substantially the fit to operational error growth curves, compared to the deterministic counterparts.
How to cite: Ghil, M., Bach, E., and Crisan, D.: Forecast error growth: A stochastic differential equation model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8640, https://doi.org/10.5194/egusphere-egu23-8640, 2023.
