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The PCA regression transformation was developed to update basin snow models when they drift away from realistic output. Snow updating uses historic and current snow water equivalence (SWE). Historic and current data come from monitoring stations within or near the basin, and from simulations of SWE in the basins. Historic observed data are used for PCA for a basin, and can potentially include time series from many monitoring stations. Current data are current daily SWE values, and are also either simulated (modelled basin) or observed (from monitoring stations within or near the basin). PCA finds the strongest underlying relationships between the historic observed station time series, and produces a linear equation. Current SWE values can then be input into the equation, and a PCA estimate of current basin SWE is produced.
Input/output timeseries
In this function four nonequidistant input time series must be identified:
1. historicalObserved
2. historicalSimulated
3. currentObserved
4. currentSimulated
In the snow updating use of the PCA regression transformation, these time series are subsamples of a daily time series to produce one data point per month. See the dayMonth sample Wiki entry for page for more details.
In this function two output time series must be identified:
1. A time series with the PCA-estimated parameter value calculated by the algorithm
2. A time series with the associated RMSE calculated by the algorithm
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