% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CST_Analogs.R
\name{Analogs}
\alias{Analogs}
\title{Analogs based on large scale fields.}
\usage{
Analogs(
  expL,
  obsL,
  time_obsL,
  time_expL = NULL,
  expVar = NULL,
  obsVar = NULL,
  criteria = "Large_dist",
  excludeTime = NULL,
  lonVar = NULL,
  latVar = NULL,
  region = NULL,
  nAnalogs = NULL,
  AnalogsInfo = FALSE
)
}
\arguments{
\item{expL}{an array of N named dimensions containing the experimental field
on the large scale for which the analog is aimed. This field is used to in 
all the criterias. If parameter 'expVar' is not provided, the function will
return the expL analog. The element 'data' in the 's2dv_cube' object must 
have, at least, latitudinal and longitudinal dimensions. The object is expect
to be already subset for the desired large scale region.}

\item{obsL}{an array of N named dimensions containing the observational field 
on the large scale. The element 'data' in the 's2dv_cube' object must have 
the same latitudinal and longitudinal dimensions as parameter 'expL' and a
temporal dimension with the maximum number of available observations.}

\item{time_obsL}{a character string indicating the date of the observations 
in the format "dd/mm/yyyy". Reference time to search for analogs.}

\item{time_expL}{a character string indicating the date of the experiment 
in the format "dd/mm/yyyy". Time to find the analogs.}

\item{expVar}{an array of N named dimensions containing the experimental 
field on the local scale, usually a different variable to the parameter 
'expL'. If it is not NULL (by default, NULL), the returned field by this 
function will be the analog of parameter 'expVar'.}

\item{obsVar}{an array of N named dimensions containing the field of the 
same variable as the passed in parameter 'expVar' for the same region.}

\item{criteria}{a character string indicating the criteria to be used for the
selection of analogs:
\itemize{
\item{Large_dist} minimum Euclidean distance in the large scale pattern; 
\item{Local_dist} minimum Euclidean distance in the large scale pattern 
and minimum Euclidean distance in the local scale pattern; and
\item{Local_cor} minimum Euclidean distance in the large scale pattern, 
minimum Euclidean distance in the local scale pattern and highest 
correlation in the local variable to downscale.}}

\item{excludeTime}{a character string indicating the date of the observations 
in the format "dd/mm/yyyy" to be excluded during the search of analogs, in a 
forecast might be NULL, if is not a forecast can not be NULL.}

\item{lonVar}{a vector containing the longitude of parameter 'expVar'.}

\item{latVar}{a vector containing the latitude of parameter 'expVar'.}

\item{region}{a vector of length four indicating the minimum longitude, 
the maximum longitude, the minimum latitude and the maximum latitude.}

\item{nAnalogs}{number of Analogs to be selected to apply the criterias 
'Local_dist' or 'Local_cor'. This is not the necessary the number of analogs 
that the user can get, but the number of events with minimum distance in 
which perform the search of the best Analog. The default value for the 
'Large_dist' criteria is 1, for 'Local_dist' and 'Local_cor' criterias must
be greater than 1 in order to match with the first criteria, if nAnalogs is
NULL for 'Local_dist' and 'Local_cor' the default value will be set at the 
length of 'time_obsL'. If AnalogsInfo is FALSE the function returns just 
the best analog.}

\item{AnalogsInfo}{TRUE to get a list with two elements: 1) the downscaled 
field and 2) the AnalogsInfo which contains: a) the number of the best 
analogs, b) the corresponding value of the metric used in the selected 
criteria (distance values for Large_dist and Local_dist,correlation values 
for Local_cor), c)dates of the analogs). The analogs are listed in decreasing
order, the first one is the best analog (i.e if the selected criteria is 
Local_cor the best analog will be the one with highest correlation, while for
Large_dist criteria the best analog will be the day with minimum Euclidean 
distance). Set to FALSE to get a single analog, the best analog, for instance
for downscaling.}
}
\value{
AnalogsFields, dowscaled values of the best analogs for the criteria 
selected. If AnalogsInfo is set to TRUE the function also returns a 
list with the dowsncaled field and the Analogs Information.
}
\description{
This function perform a downscaling using Analogs. To compute 
the analogs, the function search for days with similar large scale conditions
to downscaled fields in the local scale. The large scale and the local scale 
regions are defined by the user. The large scale is usually given by 
atmospheric circulation as sea level pressure or geopotential height (Yiou 
et al, 2013) but the function gives the possibility to use another field. The
local scale will be usually given by precipitation or temperature fields, but
might be another variable. 
The analogs function will find the best analogs based in three criterias:
(1) Minimum Euclidean distance in the large scale pattern (i.e. SLP)
(2) Minimum Euclidean distance in the large scale pattern (i.e. SLP) 
and minimum Euclidean distance in the local scale pattern (i.e. SLP). 
(3) Minimum Euclidean distance in the large scale pattern (i.e. SLP), 
minimum distance in the local scale pattern (i.e. SLP) and highest 
correlation in the local variable to downscale (i.e Precipitation).
The search of analogs must be done in the longest dataset posible. This is 
important since it is necessary to have a good representation of the 
possible states of the field in the past, and therefore, to get better 
analogs. Once the search of the analogs is complete, and in order to used the 
three criterias the user can select a number of analogs , using parameter 
'nAnalogs' to restrict 
the selection of the best analogs in a short number of posibilities, the best
ones. This function has not constrains of specific regions, variables to 
downscale, or data to be used (seasonal forecast data, climate projections 
data, reanalyses data). The regrid into a finner scale is done interpolating 
with CST_Load. Then, this interpolation is corrected selecting the analogs in
the large and local scale in based of the observations. The function is an 
adapted version of the method of Yiou et al 2013.
}
\examples{
# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
obsSLP <- c(rnorm(1:180), expSLP * 1.2)
dim(obsSLP) <- c(time = 10, lat = 4, lon = 5)
time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                          time_expL = "01-01-1994")

# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
obs.pr <- c(rnorm(1:200) * 0.001)
dim(obs.pr) <- dim(obsSLP)
downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, obsVar = obs.pr,
                          time_obsL = time_obsSLP, time_expL = "01-01-1994")

# Example 3:List of best Analogs using criteria 'Large_dist' and a single 
obsSLP <- c(rnorm(1:1980), expSLP * 1.5)
dim(obsSLP) <- c(lat = 4, lon = 5, time = 100)
time_obsSLP <- paste(rep("01", 100), rep("01", 100), 1920 : 2019, sep = "-")
downscale_field<- Analogs(expL = expSLP, obsL = obsSLP, time_obsSLP,
                         nAnalogs = 5, time_expL = "01-01-2003", 
                         AnalogsInfo = TRUE, excludeTime = "01-01-2003")

# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
obsSLP <- c(rnorm(1:180), expSLP * 2)
dim(obsSLP) <- c(lat = 4, lon = 5, time = 10)
time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-")
downscale_field <- Analogs(expL = expSLP, obsL = obsSLP, obsVar = obs.pr,
                          time_obsL = time_obsSLP,nAnalogs=5,
                          time_expL = "01-10-2003", AnalogsInfo = TRUE)

# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
# analogs of local scale using criteria 2
region=c(lonmin = -1 ,lonmax = 2, latmin = 30, latmax = 33)
Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                      obsVar = obs.pr, criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
                      latVar = seq(30, 35, 1.5), region = region,
                      time_expL = "01-10-2000", nAnalogs = 10, AnalogsInfo = TRUE)

# Example 6: list of best analogs using criteria 'Local_dist' and 2 
Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                      criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
                      latVar = seq(30, 35, 1.5), region = region,
                      time_expL = "01-10-2000", nAnalogs = 5, AnalogsInfo = TRUE)

# Example 7: Downscaling using Local_dist criteria
Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                      criteria = "Local_dist", lonVar = seq(-1, 5, 1.5),
                      latVar = seq(30, 35, 1.5), region = region, time_expL = "01-10-2000",
                      nAnalogs = 10, AnalogsInfo = FALSE)

# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
exp.pr <- c(rnorm(1:20) * 0.001)
dim(exp.pr) <- dim(expSLP)
Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                         obsVar = obs.pr, expVar = exp.pr,
                         criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
                         time_expL = "01-10-2000", latVar = seq(30, 35, 1.5),
                         nAnalogs = 8, region = region, AnalogsInfo = FALSE)
# same but without imposing nAnalogs,so nAnalogs will be set by default as 10 
Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                         obsVar = obs.pr, expVar = exp.pr,
                         criteria = "Local_cor", lonVar = seq(-1,5,1.5),
                         time_expL = "01-10-2000", latVar=seq(30, 35, 1.5), 
                         region = region, AnalogsInfo = TRUE)

#'Example 9: List of best analogs in the three criterias Large_dist, 
Large_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                      criteria = "Large_dist", time_expL = "01-10-2000",
                      nAnalogs = 7, AnalogsInfo = TRUE)
Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                      time_expL = "01-10-2000", criteria = "Local_dist",
                      lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5), 
                      nAnalogs = 7,region = region, AnalogsInfo = TRUE)
Local_scalecor <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
                         obsVar = obsSLP, expVar = expSLP, time_expL = "01-10-2000",
                         criteria = "Local_cor", lonVar = seq(-1, 5, 1.5),
                         latVar = seq(30, 35, 1.5), nAnalogs = 7,region = region, 
                         AnalogsInfo = TRUE)
}
\references{
Yiou, P., T. Salameh, P. Drobinski, L. Menut, R. Vautard,
and M. Vrac, 2013 : Ensemble reconstruction of the atmospheric column 
from surface pressure using analogues.  Clim. Dyn., 41, 1419-1437. 
\email{pascal.yiou@lsce.ipsl.fr}
}
\author{
M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}

Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }

Nuria Perez-Zanon \email{nuria.perez@bsc.es}
}