Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators

<p>Observation operators (OOs) are a central component of any data assimilation system. As they project the state variables of a numerical model into the space of the observations, they also provide an ideal opportunity to correct for effects that are not described or are insufficiently descri...

Full description

Bibliographic Details
Main Authors: E. Jansen, S. Pimentel, W.-H. Tse, D. Denaxa, G. Korres, I. Mirouze, A. Storto
Format: Article
Language:English
Published: Copernicus Publications 2019-08-01
Series:Ocean Science
Online Access:https://www.ocean-sci.net/15/1023/2019/os-15-1023-2019.pdf
_version_ 1818425298032525312
author E. Jansen
S. Pimentel
W.-H. Tse
D. Denaxa
G. Korres
I. Mirouze
A. Storto
author_facet E. Jansen
S. Pimentel
W.-H. Tse
D. Denaxa
G. Korres
I. Mirouze
A. Storto
author_sort E. Jansen
collection DOAJ
description <p>Observation operators (OOs) are a central component of any data assimilation system. As they project the state variables of a numerical model into the space of the observations, they also provide an ideal opportunity to correct for effects that are not described or are insufficiently described by the model. In such cases a dynamical OO, an OO that interfaces to a secondary and more specialised model, often provides the best results. However, given the large number of observations to be assimilated in a typical atmospheric or oceanographic model, the computational resources needed for using a fully dynamical OO mean that this option is usually not feasible. This paper presents a method, based on canonical correlation analysis (CCA), that can be used to generate highly efficient statistical OOs that are based on a dynamical model. These OOs can provide an approximation to the dynamical model at a fraction of the computational cost.</p> <p>One possible application of such an OO is the modelling of the diurnal cycle of sea surface temperature (SST) in ocean general circulation models (OGCMs). Satellites that measure SST measure the temperature of the thin uppermost layer of the ocean. This layer is strongly affected by atmospheric conditions, and its temperature can differ significantly from the water below. This causes a discrepancy between the SST measurements and the upper layer of the OGCM, which typically has a thickness of around 1&thinsp;m. The CCA OO method is used to parameterise the diurnal cycle of SST. The CCA OO is based on an input dataset from the General Ocean Turbulence Model (GOTM), a high-resolution water column model that has been specifically tuned for this purpose. The parameterisations of the CCA OO are found to be in good agreement with the results from the GOTM and improve upon existing parameterisations, showing the potential of this method for use in data assimilation systems.</p>
first_indexed 2024-12-14T14:11:43Z
format Article
id doaj.art-f2a52a9f367b4f95a6ffa5c8e6e0aa8f
institution Directory Open Access Journal
issn 1812-0784
1812-0792
language English
last_indexed 2024-12-14T14:11:43Z
publishDate 2019-08-01
publisher Copernicus Publications
record_format Article
series Ocean Science
spelling doaj.art-f2a52a9f367b4f95a6ffa5c8e6e0aa8f2022-12-21T22:58:17ZengCopernicus PublicationsOcean Science1812-07841812-07922019-08-01151023103210.5194/os-15-1023-2019Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operatorsE. Jansen0S. Pimentel1W.-H. Tse2D. Denaxa3G. Korres4I. Mirouze5A. Storto6Ocean Predictions and Applications (OPA) division, Euro-Mediterranean Center on Climate Change (CMCC), Lecce, ItalyTrinity Western University (TWU), Langley, BC, CanadaTrinity Western University (TWU), Langley, BC, CanadaHellenic Centre for Marine Research (HCMR), Athens, GreeceHellenic Centre for Marine Research (HCMR), Athens, GreeceOcean Modelling and Data Assimilation (ODA) division, Euro-Mediterranean Center on Climate Change (CMCC), Bologna, ItalyOcean Modelling and Data Assimilation (ODA) division, Euro-Mediterranean Center on Climate Change (CMCC), Bologna, Italy<p>Observation operators (OOs) are a central component of any data assimilation system. As they project the state variables of a numerical model into the space of the observations, they also provide an ideal opportunity to correct for effects that are not described or are insufficiently described by the model. In such cases a dynamical OO, an OO that interfaces to a secondary and more specialised model, often provides the best results. However, given the large number of observations to be assimilated in a typical atmospheric or oceanographic model, the computational resources needed for using a fully dynamical OO mean that this option is usually not feasible. This paper presents a method, based on canonical correlation analysis (CCA), that can be used to generate highly efficient statistical OOs that are based on a dynamical model. These OOs can provide an approximation to the dynamical model at a fraction of the computational cost.</p> <p>One possible application of such an OO is the modelling of the diurnal cycle of sea surface temperature (SST) in ocean general circulation models (OGCMs). Satellites that measure SST measure the temperature of the thin uppermost layer of the ocean. This layer is strongly affected by atmospheric conditions, and its temperature can differ significantly from the water below. This causes a discrepancy between the SST measurements and the upper layer of the OGCM, which typically has a thickness of around 1&thinsp;m. The CCA OO method is used to parameterise the diurnal cycle of SST. The CCA OO is based on an input dataset from the General Ocean Turbulence Model (GOTM), a high-resolution water column model that has been specifically tuned for this purpose. The parameterisations of the CCA OO are found to be in good agreement with the results from the GOTM and improve upon existing parameterisations, showing the potential of this method for use in data assimilation systems.</p>https://www.ocean-sci.net/15/1023/2019/os-15-1023-2019.pdf
spellingShingle E. Jansen
S. Pimentel
W.-H. Tse
D. Denaxa
G. Korres
I. Mirouze
A. Storto
Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators
Ocean Science
title Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators
title_full Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators
title_fullStr Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators
title_full_unstemmed Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators
title_short Using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators
title_sort using canonical correlation analysis to produce dynamically based and highly efficient statistical observation operators
url https://www.ocean-sci.net/15/1023/2019/os-15-1023-2019.pdf
work_keys_str_mv AT ejansen usingcanonicalcorrelationanalysistoproducedynamicallybasedandhighlyefficientstatisticalobservationoperators
AT spimentel usingcanonicalcorrelationanalysistoproducedynamicallybasedandhighlyefficientstatisticalobservationoperators
AT whtse usingcanonicalcorrelationanalysistoproducedynamicallybasedandhighlyefficientstatisticalobservationoperators
AT ddenaxa usingcanonicalcorrelationanalysistoproducedynamicallybasedandhighlyefficientstatisticalobservationoperators
AT gkorres usingcanonicalcorrelationanalysistoproducedynamicallybasedandhighlyefficientstatisticalobservationoperators
AT imirouze usingcanonicalcorrelationanalysistoproducedynamicallybasedandhighlyefficientstatisticalobservationoperators
AT astorto usingcanonicalcorrelationanalysistoproducedynamicallybasedandhighlyefficientstatisticalobservationoperators