Implications and impacts of transforming lognormal variables into normal variables in VAR

In this paper we compare two different approaches to deal with lognormal variables/observations, and hence their errors, in a 3-dimensional variational data assimilation framework. The first approach uses a transform to make the lognormal random variable into a normal random variable and hence we ca...

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Main Author: Steven J. Fletcher
Format: Article
Language:English
Published: Borntraeger 2007-12-01
Series:Meteorologische Zeitschrift
Online Access:http://dx.doi.org/10.1127/0941-2948/2007/0243
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author Steven J. Fletcher
author_facet Steven J. Fletcher
author_sort Steven J. Fletcher
collection DOAJ
description In this paper we compare two different approaches to deal with lognormal variables/observations, and hence their errors, in a 3-dimensional variational data assimilation framework. The first approach uses a transform to make the lognormal random variable into a normal random variable and hence we can use the current data assimilation techniques. The second approach uses the correct distribution for a collection of normal and lognormal random variables through a hybrid distribution which gives a different cost function to minimise. The properties of these two different approaches is that the first finds an analysis median whilst the second find the analysis mode. The two are compared through using the Lorenz 1963 model with different observational error variances and different lengths of time between the observations. It is demonstrated that the second approach out-performs the first here; however, the first is often used in operational centres with a form of bias correction and so we discuss this implication at the end of the paper.
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spelling doaj.art-3654f9e0ca774e0c9a04381974ac702e2024-02-02T14:49:08ZengBorntraegerMeteorologische Zeitschrift0941-29482007-12-0116675576510.1127/0941-2948/2007/024356116Implications and impacts of transforming lognormal variables into normal variables in VARSteven J. FletcherIn this paper we compare two different approaches to deal with lognormal variables/observations, and hence their errors, in a 3-dimensional variational data assimilation framework. The first approach uses a transform to make the lognormal random variable into a normal random variable and hence we can use the current data assimilation techniques. The second approach uses the correct distribution for a collection of normal and lognormal random variables through a hybrid distribution which gives a different cost function to minimise. The properties of these two different approaches is that the first finds an analysis median whilst the second find the analysis mode. The two are compared through using the Lorenz 1963 model with different observational error variances and different lengths of time between the observations. It is demonstrated that the second approach out-performs the first here; however, the first is often used in operational centres with a form of bias correction and so we discuss this implication at the end of the paper.http://dx.doi.org/10.1127/0941-2948/2007/0243
spellingShingle Steven J. Fletcher
Implications and impacts of transforming lognormal variables into normal variables in VAR
Meteorologische Zeitschrift
title Implications and impacts of transforming lognormal variables into normal variables in VAR
title_full Implications and impacts of transforming lognormal variables into normal variables in VAR
title_fullStr Implications and impacts of transforming lognormal variables into normal variables in VAR
title_full_unstemmed Implications and impacts of transforming lognormal variables into normal variables in VAR
title_short Implications and impacts of transforming lognormal variables into normal variables in VAR
title_sort implications and impacts of transforming lognormal variables into normal variables in var
url http://dx.doi.org/10.1127/0941-2948/2007/0243
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