ProbForecastGOP {ProbForecastGOP} | R Documentation

## Probabilistic Weather Forecasts using the GOP method

### Description

This function generates probabilistic forecasts of weather fields using the Geostatistical Output Perturbation method as described by Gel, Raftery and Gneiting (2004).

### Usage

    ProbForecastGOP(day, obs, forecast, id, coord1, coord2, cut.points=NULL,
    max.dist=NULL, nbins=300, variog.model="exponential", max.dist.fit=NULL,
    init.val=NULL, fix.nugget=FALSE, coord1.grid, coord2.grid, forecast.grid,
    n.sim=99, out="SIM", n.displ=4, qt.displ=c(10,50,90))

### Arguments

`day` | numeric vector containing the day of observation.
`obs` | numeric vector containing the observed weather quantity.
`forecast` | numeric vector containing the forecasted weather quantity.
`id` | vector with the id of the metereological stations.
`coord1` | vector containing the longitudes of the metereological stations.
`coord2` | vector containing the latitudes of the metereological stations.
`cut.points` | numeric vector containing the cutpoints used for variogram binning.
`max.dist` | a numerical value giving the upper bound for the distance considered in the variogram computation.
`nbins` | a numerical value giving the number of bins for variogram binning.
`variog.model` | character string giving the name of the parametric model to fit to the empirical variogram. Implemented models are `exponential`, `spherical`, `gauss`, `gencauchy`, and `matern`.
`max.dist.fit` | number giving the maximum distance considered when fitting the variogram.
`init.val` | numeric vector giving the initial values for the parameters in the variogram model. The number of initial values to be entered depends on the parametric model specified. If the variog.model specified is `exponential`, `spherical` or `gauss`, then the parameters required are, in order, the nugget effect, the variance and the range. If the variog.model specified is `gencauchy`, the parameters required are, in order, the nugget effect, the variance, the range, the smoothness parameter `a` and the long-range parameter `b`. If the variog.model specified is `matern` the parameters required are, in order, the nugget effect, the variance, the range, and the smoothness parameter `a`. For more details on the valid range for the parameters and for the equation of the variogram models listed above, look below at the section "Details".
`fix.nugget` | logical field indicating whether the nugget should be considered fixed or not. If `TRUE` the nugget effect will be assumed to be constant, and a value for the fixed nugget effect can be also provided. If the value provided is different from the one entered in the `init.val` field, then the value of the nugget effect is taken to be the one entered in the `init.val` field. If `FALSE` the nugget effect will be estimated along with the other parameters in the variogram model.
`coord1.grid` | numeric vector containing the longitudes of the grid points for the forecast field.
`coord2.grid` | numeric vector containing the latitudes of the grid points for the forecast field.
`forecast.grid` | numeric vector containing the forecast grid.
`n.sim` | number of realizations to be simulated.
`out` | character string indicating the output produced by the function. Possible values are `VARIOG`, `FIT` and `SIM`. If `VARIOG` is specified, the function computes the empirical variogram of the forecast errors averaged over time and displays a plot of the empirical variogram. If `FIT` is specified, the function, in addition, fits a parametric model to the empirical variogram, returns estimates of the parameters and display both the empirical and the fitted variogram. If `SIM` is specified, the function simulates also realizations of the weather random field using the parametric variogram model specified. The function displays the simulated weather fields and some specified percentiles.
`n.displ` | number of realizations to be displayed on screen.
`qt.displ` | numeric vector containing the quantiles to be displayed.

### Details

This function generates probabilistic weather field forecasts by generating ensemble members using the Geostatistical Output Perturbation method presented by Gel, Raftery and Gneiting (2004).

The Geostatistical Output Perturbation method consists of the following steps:

1. determination of the residuals, by regressing the forecasts on the observed weather quantity and computation of their empirical variogram.

2. estimation of the parameters of the variogram model fitted to the empirical variogram.

3. simulation of weather random fields using the estimated parametric variogram model.

- Computation of the empirical variogram of the residuals

The empirical variogram of the residuals is calculated by determining, for each day, the distance among all pairs of stations that have been observed in the same day and by calculating for each day the sum of all the squared differences in the residuals within each bin. These sums are then averaged over time, with weights for each bin given by the sum over time of the number of pairs of stations within the bin.

The formula used is:

gamma(h) = sum_d frac{1}{2N_{(h,d)}} (sum_h (Y(i+h,d)-Y(i,d))^2)

- Estimation of the parameters of the parametric variogram model fitted to the empirical variogram.

The function estimates the parameters in the variogram model by minimizing the weighted least-square loss function

LOSS(theta) = sum_k n_k [ ( frac{hat{gamma_k} - gamma_k(theta)}{gamma_k(theta)} )^2 ]

where n_k is the number of pairs contributing to the variogram computation in the kth bin, hat{gamma_k} is the value of the empirical variogram in the kth bin and gamma_k(theta) is the value of the estimated parametric variogram model at the midpoint of the kth bin.

- Parametric variogram models -

The parametric variogram models implemented are: exponential, spherical, gaussian, generalized Cauchy and Whittle-Matern.

- `exponential`

The equation of the exponential variogram with parameters the nugget effect, rho, the variance, σ^{2}, and the range, r, is given by:

gamma(d) = rho+σ^{2} cdot (1-exp(- frac{d}{r}))

where d is the distance between the two locations and gamma(d) is the value of the exponential variogram at distance d. The nugget effect, rho, is a non-negative number, while the variance, σ^2, and the range, r, are positive numbers.

- `spherical`

The equation of the spherical variogram with parameters the nugget effect, rho, the variance, σ^2, and the range, r, is given by:

gamma(d) = rho+σ^{2} cdot (frac{3}{2} cdotfrac{d}{r}-frac{1}{2} cdot frac{d^3}{r^3})

where d is the distance between the two locations and gamma(d) is the value of the spherical variogram at distance d. The nugget effect, rho, is a non-negative number, while the variance, σ^2, and the range, r, are positive numbers.

- `gauss`

The equation of the gaussian variogram with parameters the nugget effect, rho, the variance, σ^2, and the range, r, is given by:

gamma(d) = rho+σ^{2} cdot (1-exp(- frac{d^2}{r^2} ))

where d is the distance between the two locations and gamma(d) is the value of the gaussian variogram at distance d. The nugget effect, rho, is a non-negative number, while the variance, σ^2, and the range, r, are positive numbers.

- `gencauchy`

The equation of the generalized Cauchy variogram with parameters the nugget effect, rho, the variance, σ^2, the range, r, the smoothness parameter, a, and the long-range parameter, b, is given by:

gamma(d) = rho+σ^{2} cdot (1-(1+frac{d^a}{r^a})^{- frac{b}{a}})

where d is the distance between the two locations and gamma(d) is the value of the generalized Cauchy variogram at distance d. The nugget effect, rho is a non-negative number, the variance, σ^2 and the range, r, are positive numbers, the smoothness parameter, a, is a number in (0,2], and the long-range parameter, b, is a positive number.

- `matern`

The equation of the Whittle-Matern variogram with parameters the nugget effect, rho, the variance, σ^2, the range, r, and the smoothness parameter, a, is given by:

gamma(d) = rho+σ^{2} cdot (1-(frac{2^{1-a}}{Γ(a)}cdot frac{d^a}{r^a} cdot K_{a}(frac{d}{r}))

where d is the distance between the two locations, gamma(d) is the value of the Whittle-Matern variogram at distance d, Γ is the gamma function and K_a is the Bessel function of the third kind with parameter a. The nugget effect, rho, is a non-negative number, the variance, σ^2, the range, r, and the smoothness parameter, a, are positive numbers.

- Defaults-

If the vector with the cutpoints is not specified, the cutpoints are determined so that there are `nbins` bins with approximately the same number of pairs per bin. If both the vector with the cutpoints and the number of bins, `nbins`, are unspecified, the function by default determines the cutpoints so that there are 300 bins with approximately the same number of pairs per bin.

The default value for the maximum distance considered in the variogram computation is the 90-th percentile of the distances between all stations.

The default value for the maximum distance is frac{1}{2 cdot sqrt{2}} times the maximum distance considered in the empirical variogram.

Default for the initial values of the parameters is `NULL`, in which case the initial values for the parameters are determined internally and depend on the empirical variogram values. By default, the nugget effect is not considered constant. Thus, the default value for the `fix.nugget` field is `FALSE`.

The default for `out` is `SIM`: by default, 99 weather random fields are simulated, and 4 of them are displayed. If no vector of percentiles, is provided, the 10th, 50th and 90th percentiles are determined and displayed.

### Value

The function returns both a numerical and a graphical summary. The graphical and numerical summary provided depend on the specification given in the field `out`.
The graphical summary consists of a plot of the empirical variogram, if `out` is equal to `VARIOG`.
It consists of a plot of the empirical variogram with superimposed the fitted parametric model, if `out` is equal to `FIT`.
If `out` is equal to `SIM`, the graphical summary consists of several plots: a plot of the empirical variogram with superimposed the fitted parametric model, plots of the simulated weather fields, and plots of specified percentiles of the weather random field. The plots are printed on multiple pages, and before displaying each page, the user will be asked for input.
The numerical summary include the following:

`bias.coeff` | additive and multiplicative bias obtained by linear regression of the forecasted weather quantity on the observed weather quantity. Always provided.
`se.bias.coeff` | standard errors of the bias coefficients. Always provided.
`res.var` | variance of the forecast errors. Always provided.
`bin.midpoints` | numeric vector with midpoints of the bins used in the empirical variogram computation. Always provided.
`number.pairs` | numeric vector with the number of pairs per bin. Always provided.
`empir.variog` | numeric vector with the empirical variogram values. Always provided.
`model` | name of the parametric model fitted to the empirical variogram. Provided only if `out` is equal to `FIT` or `SIM`.
`nugget` | Estimate of the nugget effect. Provided only if `out` is equal to `FIT` or `SIM`.
`variance` | Estimate of the variance. Provided only if `out` is equal to `FIT` or `SIM`.
`range` | Estimate of the range. Provided only if `out` is equal to `FIT` or `SIM`.
`additional.par` | Numeric vector with the estimates of the additional parameters required by the parametric variogram model (only if the model specified is either `whittlematern` or `gencauchy`). This is provided only if `out` is equal to `FIT` or `SIM`.
`sim.fields` | 3-dimensional array where each layer contains the values of the simulated weather field at the gridded locations. Provided only if `out` is equal to `SIM`.
`pct.fields` | 3-dimensional array where each layer contains the specified percentiles of the weather field. Provided only if `out` is equal to `SIM`.

### Note

Computation of the empirical variogram might require substantial computing time. Hence, it is advisable to run the function once with `out` equal to `VARIOG`, save part of the output and use it as input for the `Variog.fit` function to estimate a parametric variogram model. Finally, `Field.sim` will simulate realizations of the weather field from the specified geostatistical model.

### Author(s)

Gel, Y., Raftery, A. E., Gneiting, T., Berrocal, V. J. veronica@stat.washington.edu.

### References

Gel, Y., Raftery, A. E., Gneiting, T. (2004). Calibrated probabilistic mesoscale weather field forecasting: The Geostatistical Output Perturbation (GOP) method (with discussion). Journal of the American Statistical Association, Vol. 99 (467), 575–583.

Gel, Y., Raftery, A. E., Gneiting, T., Berrocal, V. J. (2004). Rejoinder. Journal of the American Statistical Association, Vol. 99 (467), 588–590.

Cressie, N. A. C. (1993). Statistics for Spatial Data (revised ed.). Wiley: New York.

Gneiting, T., Schlather, M. (2004). Stochastic models that separate fractal dimension and the Hurst effect. SIAM Review, 46, 269–282.

Stein, M. L. (1999). Interpolation of Spatial Data - Some Theory for Kriging. Springer-Verlag: New York.

Schlather, M. (2001). Simulation and analysis of Random Fields. R News 1(2), 18–20.

Nychka, D. (2004). The fields package. Available at: http:lib.stat.cmu.edu/R/CRAN/doc/package/fields.pdf.

### See Also

`Emp.variog` for computation of empirical variogram of forecast errors averaged over time, `Variog.fit` for estimation of parameters in a geostatistical model using Weighted Least Square, `Field.sim` for realizations of weather fields with the specified covariance structure.

### Examples

    ## Loading data
    library(fields)
    library(RandomFields)
    data(slp)
    day <-slp$date.obs
    id <- slp$id.stat
    coord1 <- slp$lon.stat
    coord2 <- slp$lat.stat
    obs <- slp$obs
    forecast <-slp$forecast

    data(gridlong)
    coord1.grid <- gridlong$gridded.lon

    data(gridlat)
    coord2.grid <- gridlat$gridded.lat

    data(forecast.grid)
    forecast.grid <- forecast.grid$gridded.forecast

    ## Specified cutpoints, default values for all the other fields.
    ## Only empirical variogram computation
    empirical.variog <- ProbForecastGOP(day=day,obs=obs,forecast=forecast,
    id=id,coord1=coord1,coord2=coord2,cut.points=seq(0,1000,by=2),
    max.dist=NULL,nbins=NULL,variog.model="exponential",max.dist.fit=NULL,
    init.val=NULL,fix.nugget=FALSE,coord1.grid=coord1.grid,
    coord2.grid=coord2.grid,forecast.grid=forecast.grid,n.sim=10,
    out="VARIOG",n.displ=4,qt.displ=c(5,50,95))

    ## Unspecified cutpoints.
    ## Fit of a parametric variogram to an empirical variogram.
    fitted.variog <- ProbForecastGOP(day=day,obs=obs,forecast=forecast,id=id,
    coord1=coord1,coord2=coord2,cut.points=NULL,max.dist=NULL,nbins=NULL,
    variog.model="exponential",max.dist.fit=NULL,init.val=NULL,fix.nugget=FALSE,
    coord1.grid=coord1.grid,coord2.grid=coord2.grid,forecast.grid=forecast.grid,
    n.sim=10,out="FIT",n.displ=4,qt.displ=c(5,50,95))

    ## Unspecified cutpoints.
    ## Whole routine.
    simulation.fields <-
    ProbForecastGOP(day=day,obs=obs,forecast=forecast,id=id,coord1=coord1,
    coord2=coord2,cut.points=NULL,max.dist=NULL,nbins=NULL,variog.model=NULL,
    max.dist.fit=NULL,init.val=NULL,fix.nugget=FALSE,coord1.grid=coord1.grid,
    coord2.grid=coord2.grid,forecast.grid=forecast.grid,n.sim=4,out="SIM",
    n.displ=4,qt.displ=c(5,50,95))

[Package ProbForecastGOP version 1.3 Index]