msTrans_abs {musica}

Multiscale quantile mapping bias correction

Description

Applies standard quantile mapping at custom time scales.

Usage

msTrans_abs(dta, agg_by = month, wet_int_thr = 0.1, maxiter = 10,
  tol = 1e-04, qstep = 0.001, period = c("G1", "Y1", "M3", "M1", "D1"))

Arguments

dta

List with components FROM (simulated data for the control period), TO (observed data) and NEWDATA (data to be corrected). Each component is a data.table with columns DTM (date) and the climate variables (typically PR - precipitation and TAS - temperature)

agg_by

Function for specification of the period (season, month) to be additionaly included in output, see Details

wet_int_thr

Numeric value specifying the minimum depth to be considered wet

maxiter

Maximum number of iterations, see Details

tol

Stoping criterion of the iteration cycle, see Details

qstep

A numeric value between 0 and 1. The quantile mapping is fitted only for the quantiles defined by quantile(0,1,probs=seq(0,1,by=qstep). Passed to doQmapQUANT.

period

Specification of the aggregation lengths the correction is applied at with 'D' - day(s), 'M' - month(s), 'Y' - year(s) and 'G1' - the overall mean

Details

The procedure utilizes standard quantile mapping from the qmap-package, but at multiple time scales. Since correction at particular temporal scale influences values at other aggregations, the procedure is applied iterativelly until the maximum number of iterations (maxiter) is reached or the difference between succesive iteration step is smaller than tol. Differences between corrected and uncorrected variable at longer time scales are used to modify daily values after each iteration step (see e.g. Mehrorta and Sharma, 2016; Pegram et al. 2009). To make further assessment of the decomposed objects easier, indicator of period within the year (e.g. quarter or month) as specified by agg_by argument is included in the output.

Value

data.table with corrected data

References

Hanel, M., Kozin, R., 2016. Bias and projected changes in climate model simulations at multiple time scales: consequences for hydrological impact assessment. Environmental Modelling and Software, submitted.

Mehrotra, R., Sharma, A., 2016. A multivariate quantile-matching bias correction approach with auto-and cross-dependence across multiple time scales: Implications for downscaling. Journal of Climate 29, 3519-3539.

Pegram, G.G., et al., 2009. A nested multisite daily rainfall stochastic generation model. Journal of Hydrology 371, 142-153.

Examples

data("basin_PT")
scen = basin_PT$sim_scen
ctrl = basin_PT$sim_ctrl
obs = basin_PT$obs_ctrl
dta = list(TO = obs, FROM = ctrl, NEWDATA = scen)
## Not run: 
msTrans_abs(dta,  maxiter = 10, period = 'D1')

## End(Not run)