Optimal interpolation client
When the input file is deterministic, or does not contain enough ensemble members (specified by minEns), the deterministic mode is automatically used. In this case, a static covariance structure is used.
The parameter file contains the observation at points as well as information about its uncertainty and covariance structure (for precipitation from radar). The first parameter is always the observation. The second is optional and represents a scaling factor for sigma. A value of 0.5 indicates that a particular observation has half the standard error. For precipitation, a third parameter can be used, which is simply a flag that indicates that the observation error covaries with all other observations for which the flag is set. This is needed for radar values, since these errors are often correlated in space.
OI requires information about the uncertainty of observations and uncertainty of the background. sigma sets the standard error of the observation. For example, sigma=1 sets the standard error to 1 degree (for temperature). The uncertainty can be modified on a station-by-station basis (see Parameter Files below).
The uncertainty of the background comes from the ensemble spread. This spread is often too low and can therefore be inflated by setting the delta option. For example, delta=2 doubles the ensemble spread.
MET Norway uses the following settings for temperature:
d=10000 h=100 sigma=0.5 delta=2 wmin=0.5 elevGradient=-0.0065
MET Norway uses the following settings for precipitation:
d=10000 h=-999 sigma=0.15 sigmaC=0.15 wmin=1 dc=3000 maxElevDiff=-999 elevGradient=0 transform=boxcox
Stations can be further limited by setting maxLocations. For example, if maxLocations=4, then only the 4 locations with highest rho value are used (as in the right figure). This reduces computation time significantly in some cases. It also allows for the correction to be localized in dense areas, while still allowing for a large h value such that corrections can be applied in sparse areas.
Observations decorrelate in he vertical too, and is set by the v option.
The image below illustrates how stations are selected (green) and discarded (red) for a particular gridpoint. In the left panel, all stations within some radius are selected, which is based on the set values for h and minRho.
A Gaussian assumption is used, however the background and observations can be transformed using a box-cox transformation.
OI is designed for both temperature and precipitation and can be specified using type. When type=precipitation, the background and observations are transformed using a box-cox transformation.
The observations and background can be transformed before the OI is applied by using the transform option. Currently, only transform=boxcox is supported. This transformation can be used for precipitation. The sigma option is set in the transformed space.
When OI interpolates the increment between observations, it can in some cases create increments that extrapolate outside the range of values of the increments at the observation points. Gridpp can prevent the extrapolation.
