Correcting for model changes in statistical postprocessing – an approach based on response theory
<p>For most statistical postprocessing schemes used to correct weather forecasts, changes to the forecast model induce a considerable reforecasting effort. We present a new approach based on response theory to cope with slight model changes. In this framework, the model change is seen as a per...
Main Authors: | J. Demaeyer, S. Vannitsem |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2020-05-01
|
Series: | Nonlinear Processes in Geophysics |
Online Access: | https://www.nonlin-processes-geophys.net/27/307/2020/npg-27-307-2020.pdf |
Similar Items
-
Comparison of stochastic parameterizations in the framework of a coupled ocean–atmosphere model
by: J. Demaeyer, et al.
Published: (2018-08-01) -
The Modular Arbitrary-Order Ocean-Atmosphere Model: MAOOAM v1.0
by: L. De Cruz, et al.
Published: (2016-08-01) -
Extratropical Low‐Frequency Variability With ENSO Forcing: A Reduced‐Order Coupled Model Study
by: Stéphane Vannitsem, et al.
Published: (2021-06-01) -
Bias correction and post-processing under climate change
by: S. Vannitsem
Published: (2011-12-01) -
Exploring the Lyapunov instability properties of high-dimensional atmospheric and climate models
by: L. De Cruz, et al.
Published: (2018-05-01)