Directional component analysis

Directional component analysis (DCA) is a statistical method used in climate science for identifying representative patterns of variability in space-time data-sets such as historical climate observations, <a href="/facts/Ensemble_forecasting/mRBvTgmD">weather prediction ensembles</a> or <a href="/facts/Climate_ensemble/rgsm0E8t">climate ensembles</a>.
The first DCA pattern is a pattern of weather or climate variability that is both likely to occur (measured using <a href="/facts/Likelihood_function/1L8P4NBX">likelihood</a>) and has a large impact (for a specified linear impact function, and given certain mathematical conditions: see below).
The first DCA pattern contrasts with the first <a href="/facts/Principal_component_analysis/pCbVTqdR">PCA</a> pattern, which is likely to occur, but may not have a large impact, and with a pattern derived from the <a href="/facts/Gradient/TsR7uukj">gradient</a> of the impact function, which has a large impact, but may not be likely to occur.
DCA differs from other pattern identification methods used in climate research, such as <a href="/facts/Empirical_orthogonal_function/rb4v4C2p">EOFs</a>, rotated EOFs and extended EOFs in that it takes into account an external vector, the gradient of the impact.
DCA provides a way to reduce large ensembles from <a href="/facts/Ensemble_forecasting/mRBvTgmD">weather forecasts</a> or <a href="/facts/Climate_ensemble/rgsm0E8t">climate models</a> to just two patterns. 
The first pattern is the ensemble mean, and the second pattern is the DCA pattern, which represents variability around the ensemble mean in a way that takes impact into account.
DCA contrasts with other methods that have been proposed for the reduction of ensembles in that it takes impact into account in addition to the structure of the ensemble.

Directional component analysis open-in-new

Directional component analysis