Empirical correction techniques: analysis and applications to chaotically driven low-order atmospheric models
Journal
Nonlinear Processes in Geophysics
Date Issued
2013-03-07
Author(s)
Trpevski, I
Smilkov, D
Kocarev, Ljupcho
Abstract
Contemporary tools for reducing model error in
weather and climate forecasting models include empirical
correction techniques. In this paper we explore the use of
such techniques on low-order atmospheric models. We first
present an iterative linear regression method for model correction that works efficiently when the reference truth is sampled at large time intervals, which is typical for real world applications. Furthermore we investigate two recently proposed
empirical correction techniques on Lorenz models with constant forcing while the reference truth is given by a Lorenz
system driven with chaotic forcing. Both methods indicate
that the largest increase in predictability comes from correction terms that are close to the average value of the chaotic
forcing.
weather and climate forecasting models include empirical
correction techniques. In this paper we explore the use of
such techniques on low-order atmospheric models. We first
present an iterative linear regression method for model correction that works efficiently when the reference truth is sampled at large time intervals, which is typical for real world applications. Furthermore we investigate two recently proposed
empirical correction techniques on Lorenz models with constant forcing while the reference truth is given by a Lorenz
system driven with chaotic forcing. Both methods indicate
that the largest increase in predictability comes from correction terms that are close to the average value of the chaotic
forcing.
File(s)![Thumbnail Image]()
Loading...
Name
npg-20-199-2013.pdf
Size
1.03 MB
Format
Adobe PDF
Checksum
(MD5):f326fbb1370e98de0375b697822acc58