interpolation_lowlevel: Low-level interpolation functions

In miquelcaceres/meteoland: Landscape Meteorology Tools

Description

Low-level functions to interpolate meteorology (one day) on a set of points.

Usage

interpolation_dewtemperature(Xp, Yp, Zp, X, Y, Z, T, 
                             iniRp = 140000, alpha = 3.0, N = 30, iterations = 3)
interpolation_temperature(Xp, Yp, Zp, X, Y, Z, T, 
                          iniRp = 140000, alpha = 3.0, N = 30, iterations = 3)
interpolation_precipitation(Xp, Yp, Zp, X, Y, Z, P, Psmooth,
                            iniRp = 140000, alpha_event = 6.25, alpha_amount = 6.25,
                            N_event = 20, N_amount = 20, iterations = 3, popcrit = 0.5, 
                            fmax = 0.95)
interpolation_wind(Xp, Yp, WS, WD, X, Y,
                   iniRp = 140000, alpha = 2.0, N = 1, iterations = 3,
                   directionsAvailable = TRUE)

Arguments

Xp, Yp, Zp	Spatial coordinates and elevation (Zp; in m.a.s.l) of target points.
X, Y, Z	Spatial coordinates and elevation (Zp; in m.a.s.l) of reference locations (e.g. meteorological stations).
T	Temperature (e.g., minimum, maximum or dew temperature) at the reference locations (in degrees).
P	Precipitation at the reference locations (in mm).
Psmooth	Temporally-smoothed precipitation at the reference locations (in mm).
WS, WD	Wind speed (in m/s) and wind direction (in degrees from north clock-wise) at the reference locations.
iniRp	Initial truncation radius.
iterations	Number of station density iterations.
alpha, alpha_amount, alpha_event	Gaussian shape parameter.
N, N_event, N_amount	Average number of stations with non-zero weights.
popcrit	Critical precipitation occurrence parameter.
fmax	Maximum value for precipitation regression extrapolations (0.6 equals to a maximum of 4 times extrapolation).
directionsAvailable	A flag to indicate that wind directions are available (i.e. non-missing) at the reference locations.

Value

All functions return a vector with interpolated values for the target points.

Author(s)

Miquel De Cáceres Ainsa, Centre Tecnologic Forestal de Catalunya

References

Thornton, P.E., Running, S.W., White, M. A., 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol. 190, 214–251. doi:10.1016/S0022-1694(96)03128-9.

De Caceres M, Martin-StPaul N, Turco M, Cabon A, Granda V (2018) Estimating daily meteorological data and downscaling climate models over landscapes. Environmental Modelling and Software 108: 186-196.

See Also

defaultInterpolationParams

Examples

data("exampleinterpolationdata")
mxt100 = exampleinterpolationdata@MaxTemperature[,100]
Psmooth100 = exampleinterpolationdata@SmoothedPrecipitation[,100]
P100 = exampleinterpolationdata@Precipitation[,100]
mismxt = is.na(mxt100)
misP = is.na(P100)
Z = exampleinterpolationdata@elevation
X = exampleinterpolationdata@coords[,1]
Y = exampleinterpolationdata@coords[,2]
Zpv = seq(0,1000, by=100)
xp = 360000
yp = 4640000
xpv = rep(xp, 11)
ypv = rep(yp, 11)

interpolation_temperature(xpv, ypv, Zpv, 
                          X[!mismxt], Y[!mismxt], Z[!mismxt], 
                          mxt100[!mismxt])
interpolation_precipitation(xpv, ypv, Zpv, 
                           X[!misP], Y[!misP], Z[!misP], 
                           P100[!misP], Psmooth100[!misP])

miquelcaceres/meteoland documentation built on May 8, 2019, 11:57 p.m.