Description Usage Arguments Value Author(s) References See Also Examples
Low-level functions to interpolate meteorology (one day) on a set of points.
1 2 3 4 5 6 7 8 9 10 11 | 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)
|
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. |
All functions return a vector with interpolated values for the target points.
Miquel De Cáceres Ainsa, Centre Tecnologic Forestal de Catalunya
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | 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])
|
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