% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CST_RFTemp.R
\name{RFTemp}
\alias{RFTemp}
\title{Temperature downscaling of a CSTools object using lapse rate
correction (reduced version)}
\usage{
RFTemp(data, lon, lat, oro, lonoro, latoro, xlim = NULL, ylim = NULL,
  lapse = 6.5, lon_dim = "lon", lat_dim = "lat", time_dim = NULL,
  nolapse = FALSE, verbose = FALSE, compute_delta = FALSE,
  method = "bilinear", delta = NULL)
}
\arguments{
\item{data}{Temperature array to downscale.
The input array is expected to have at least two dimensions named
"lon" and "lat" by default
(these default names can be changed with the \code{lon_dim} and
\code{lat_dim} parameters)}

\item{lon}{Vector or array of longitudes.}

\item{lat}{Vector or array of latitudes.}

\item{oro}{Array containing fine-scale orography (in m)
The destination downscaling area must be contained in the orography field.}

\item{lonoro}{Vector or array of longitudes corresponding to the fine orography.}

\item{latoro}{Vector or array of latitudes corresponding to the fine orography.}

\item{xlim}{vector with longitude bounds for downscaling;
the full input field is downscaled if `xlim` and `ylim` are not specified.}

\item{ylim}{vector with latitude bounds for downscaling}

\item{lapse}{float with environmental lapse rate}

\item{lon_dim}{string with name of longitude dimension}

\item{lat_dim}{string with name of latitude dimension}

\item{time_dim}{a vector of character string indicating the name of temporal dimension. By default, it is set to NULL and it considers "ftime", "sdate" and "time" as temporal dimensions.}

\item{nolapse}{logical, if true `oro` is interpreted as a
fine-scale climatology and used directly for bias correction}

\item{verbose}{logical if to print diagnostic output}

\item{compute_delta}{logical if true returns only a delta to be used for
out-of-sample forecasts.}

\item{method}{string indicating the method used for interpolation:
"nearest" (nearest neighbours followed by smoothing with a circular
uniform weights kernel), "bilinear" (bilinear interpolation)
The two methods provide similar results, but nearest is slightly better
provided that the fine-scale grid is correctly centered as a subdivision
of the large-scale grid}

\item{delta}{matrix containing a delta to be applied to the downscaled
input data. The grid of this matrix is supposed to be same as that of
the required output field}
}
\value{
CST_RFTemp() returns a downscaled CSTools object

RFTemp() returns a list containing the fine-scale
longitudes, latitudes and the downscaled fields.
}
\description{
This function implements a simple lapse rate correction of a
temperature field (a multidimensional array) as input.
The input lon grid must be increasing (but can be modulo 360).
The input lat grid can be irregularly spaced (e.g. a Gaussian grid)
The output grid can be irregularly spaced in lon and/or lat.
}
\examples{
# Generate simple synthetic data and downscale by factor 4
t <- rnorm(7 * 6 * 4 * 3) * 10 + 273.15 + 10
dim(t) <- c(sdate = 3, ftime = 4, lat = 6, lon = 7)
lon <- seq(3, 9, 1)
lat <- seq(42, 47, 1)
o <- runif(29 * 29) * 3000
dim(o) <- c(lat = 29, lon = 29)
lono <- seq(3, 10, 0.25)
lato <- seq(41, 48, 0.25)
res <- RFTemp(t, lon, lat, o, lono, lato, xlim = c(4, 8), ylim = c(43, 46),
              lapse = 6.5)
}
\references{
Method described in ERA4CS MEDSCOPE milestone M3.2: 
High-quality climate prediction data available to WP4
[https://www.medscope-project.eu/the-project/deliverables-reports/]([https://www.medscope-project.eu/the-project/deliverables-reports/)
and in H2020 ECOPOTENTIAL Deliverable No. 8.1:
High resolution (1-10 km) climate, land use and ocean change scenarios
[https://www.ecopotential-project.eu/images/ecopotential/documents/D8.1.pdf](https://www.ecopotential-project.eu/images/ecopotential/documents/D8.1.pdf)
}
\author{
Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it}
}