Interpol.T (version 2.1.1)

shape_calibration: Calibrates the shape of the night interpolating curve

Description

Calibrates the shape of the night interpolating curve, either horizontal-axe parabola or line, by changing the exponent z (see reference). It functions according to the comparison of the daily thermal range and the climate (reference) monthly one.

Usage

shape_calibration(meas, date.format = "ymd", cal_times_list, band_min = 0:23, band_max = 0:23, ratio_dtr_range = c(0, 6), nr_cycles = 10, min_mo.length = 21, full.24.hrs.span_min = TRUE, silent = FALSE)

Arguments

meas

measured hourly values file (table), where the first column is the series' ID

date.format

input date format (formats for function chron)

cal_times_list

calibration list of "time" parameters (output of par_calibration)

band_min

band of hours of occurrence of day minimum in the daily series (continuous). See Note

band_max

same for maximum time

ratio_dtr_range

range for seeking the optimal value of ratio_dtr

nr_cycles

number of calibration trials within the calibration ranges (all)

min_mo.length

minimum number of days to calculate any monthly values of dtr (is passed to function Mo.Th.Ra.)

full.24.hrs.span_min

logical, if set to FALSE does not allow to shift minimum time to the late hours of the day

silent

logical, if set to TRUE suppresses any warning issue

Value

A list containing the optimum values of ratio_dtr

References

Eccel, E., 2010: What we can ask to hourly temperature recording. Part II: hourly interpolation of temperatures for climatology and modelling. Italian Journal of Agrometeorology XV(2):45-50 http://www.agrometeorologia.it/documenti/Rivista2010_2/AIAM%202-2010_pag45.pdf,www.agrometeorologia.it

Original algorithm from: Cesaraccio, C., Spano, D., Duce, P., Snyder, R.L., 2001. An improved model for determining degree-day values from daily temperature data. Int. J. Biometeorol. 45: 161-169. http://www.springerlink.com/content/qwctkmlq3tebthek/

See also: Eccel, E., 2010: What we can ask to hourly temperature recording. Part I: statistical vs. meteorological meaning of minimum temperature. Italian Journal of Agrometeorology XV(2):41-43. http://www.agrometeorologia.it/documenti/Rivista2010_2/AIAM%202-2010_pag41.pdf,www.agrometeorologia.it

See Also

par_calibration, Th_interp

Examples

library(Interpol.T)
data(Trentino_hourly_T)

stations <- c("T0001","T0010","T0129")

calibration_shape <- shape_calibration(meas = h_d_t[h_d_t$V1 %in% stations,],
						cal_times_list = calibration_l[stations],
                     band_min = 0:23, band_max = 0:23, ratio_dtr_range = c(0,4),
						min_mo.length=21)