% 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, expVar = NULL, obsVar = NULL,
  criteria = "Large_dist", lonVar = NULL, latVar = NULL,
  region = NULL, nAnalogs = 1, return_list = 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"}

\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} minimal distance in the large scale pattern; 
\item{Local_dist} minimal distance in the large scale pattern and minimal 
distance in the local scale pattern; and
\item{Local_cor} minimal distance in the large scale pattern, minimal 
distance in the local scale pattern and maxima correlation in the 
local variable to downscale.}}

\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 (this
is not the necessary the number of analogs that the user can get, but the number
of events with minimal distance in which perform the search of the best Analog.
The default value for the Large_dist criteria is 1, the default value for 
the Local_dist criteria is 10 and same for Local_cor. If return_list is 
False you will get just the first one for downscaling purposes. If return_list
is True you will get the list of the best analogs that were searched in nAnalogs
under the selected criterias.}

\item{return_list}{TRUE if you want to get a list with the best analogs FALSE
if not. For Downscaling return_list must be FALSE.}
}
\value{
DatesAnalogs, A character string with the date of the best analogs (time, distance)

AnalogsFields, dowscaled values of the best analogs for the criteria selected.
}
\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) Minimal distance in the large scale pattern (i.e. SLP)
(2) Minimal distance in the large scale pattern (i.e. SLP) and minimal 
distance in the local scale pattern (i.e. SLP). 
(3) Minimal distance in the large scale pattern (i.e. SLP), minimal 
distance in the local scale pattern (i.e. SLP) and maxima 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 analogsi, 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{
require(zeallot)

# Example 1:Downscaling using criteria 'Large_dist' and a single variable:
# The best analog is found using a single variable (i.e. Sea level pressure, SLP)
# The downscaling will be done in the same variable used to study the minimal distance 
# (i.e. SLP). obsVar and expVar NULLS or equal to obsL and expL respectively
# region, lonVar and latVar not necessary here. return_list=FALSE 

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
obsSLP <- c(rnorm(1:180),expSLP*1.2)
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, time_obsL=time_obsSLP)
str(downscale_field)

# Example 2: Downscaling using criteria 'Large_dist' and 2 variables:
# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
# SLP and precipitation, pr): one variable (i.e. sea level pressure, expL) 
# to find the best analog day (defined in criteria 'Large_dist' as the day, in obsL,
# with the minimum Euclidean distance to the day of interest in expL) 
# The second variable will be the variable to donwscale (i.e. precipitation, obsVar)
# Parameter obsVar must be different to obsL.The downscaled precipitation
# will be the precipitation that belongs to the best analog day in SLP. 
# Region not needed here since will be the same for both variables.

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
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 = "-")
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)
str(downscale_field)

# Example 3:List of best Analogs using criteria 'Large_dist' and a single variable:
# same as Example 1 but getting a list of best analogs (return_list =TRUE) with the 
# corresponding downscaled values, instead of only 1 single donwscaled value
# instead of 1 single downscaled value. Imposing nAnalogs (number of analogs to do the 
# search of the best Analogs).obsVar and expVar NULL or equal to obsL and expL,respectively.

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
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,return_list = TRUE)
str(downscale_field)

# Example 4:List of best Analogs using criteria 'Large_dist' and 2 variables:
# same as example 2 but getting a list of best analogs (return_list =TRUE) with the values
# instead of only a single downscaled value. Imposing nAnalogs (number of analogs to do the 
# search of the best Analogs). obsVar and expVar must be different to obsL and expL.

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
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 = "-")
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,nAnalogs=5,return_list = TRUE)
str(downscale_field)

# Example 5: Downscaling using criteria 'Local_dist' and 2 variables:
# The best analog is found using 2 variables (i.e. Sea Level Pressure, 
# SLP and precipitation, pr). Parameter obsVar must be different to obsL.The 
# downscaled precipitation will be the precipitation that belongs to the best 
# analog day in SLP. lonVar, latVar and Region must be given here to select the local scale

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
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 = "-")
obs.pr <- c(rnorm(1:200)*0.001)
dim(obs.pr)=dim(obsSLP)
# analogs of local scale using criteria 2
lonmin=-1
lonmax=2
latmin=30
latmax=33
region=c(lonmin,lonmax,latmin,latmax)
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, 
                      nAnalogs = 10, return_list = FALSE)
str(Local_scale)

# Example 6: list of best analogs using criteria 'Local_dist' and 2 variables:
# The best analog is found using 2 variables. Parameter obsVar must be different to obsL in this case.The 
# downscaled precipitation will be the precipitation that belongs to the best 
# analog day in SLP. lonVar, latVar and Region needed. return_list=TRUE

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
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 = "-")
obs.pr <- c(rnorm(1:200)*0.001)
dim(obs.pr)=dim(obsSLP)
# analogs of local scale using criteria 2
lonmin=-1
lonmax=2
latmin=30
latmax=33
region=c(lonmin,lonmax,latmin,latmax)
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, 
                      nAnalogs = 10, return_list = TRUE)
str(Local_scale)

# Example 7: Downscaling using Local_dist criteria
# without parameters obsVar and expVar. return list =FALSE 

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
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 = "-")
# analogs of local scale using criteria 2
lonmin=-1
lonmax=2
latmin=30
latmax=33
region=c(lonmin,lonmax,latmin,latmax)
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, 
                      nAnalogs = 10, return_list = TRUE)
str(Local_scale)

# Example 8: Downscaling using criteria 'Local_cor' and 2 variables:
# The best analog is found using 2 variables. Parameter obsVar and expVar are necessary and must be different 
# to obsL and expL, respectively.The downscaled precipitation will be the 
# precipitation that belongs to the best analog day in SLP large and local scales,
# and to the day with the higher correlation in precipitation. return_list=FALSE

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
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 = "-")
exp.pr <- c(rnorm(1:20)*0.001)
dim(exp.pr)=dim(expSLP)
obs.pr <- c(rnorm(1:200)*0.001)
dim(obs.pr)=dim(obsSLP)
# analogs of local scale using criteria 2
lonmin=-1
lonmax=2
latmin=30
latmax=33
region=c(lonmin,lonmax,latmin,latmax)
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),
                         latVar=seq(30,35,1.5),nAnalogs=15,region=region, 
                         return_list = FALSE)
str(Local_scalecor)

# Example 9: List of best analogs in the three criterias Large_dist, Local_dist, and Local_cor 
# return list TRUE same variable

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
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 = "-")
# analogs of large scale using criteria 1
Large_scale <- Analogs(expL=expL,
                      obsL=obsL, time_obsL=time_obsL,
                      criteria="Large_dist", 
                      nAnalogs = 15, return_list = TRUE)
str(Large_scale)
# analogs of local scale using criteria 2
lonmin=-1
lonmax=2
latmin=30
latmax=33
region=c(lonmin,lonmax,latmin,latmax)
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),nAnalogs=15,region=region, 
                      return_list = TRUE)
str(Local_scale)
# analogs of local scale using criteria 3
Local_scalecor <- Analogs(expL=expSLP,
                         obsL=obsSLP, time_obsL=time_obsSLP,
                         obsVar=obsSLP,expVar=expSLP,
                         criteria="Local_cor",lonVar=seq(-1,5,1.5),
                         latVar=seq(30,35,1.5),nAnalogs=15,region=region, 
                         return_list = TRUE)
str(Local_scalecor)

# Example 10: Downscaling in the three criteria Large_dist, Local_dist, and Local_cor 
return list FALSE, different variable

expSLP <- rnorm(1:20)
dim(expSLP) <- c(lat = 4, lon = 5)
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 = "-")
exp.pr <- c(rnorm(1:20)*0.001)
dim(exp.pr)=dim(expSLP)
obs.pr <- c(rnorm(1:200)*0.001)
dim(obs.pr)=dim(obsSLP)
# analogs of large scale using criteria 1
Large_scale <- Analogs(expL=expL,
                      obsL=obsL, time_obsL=time_obsL,
                      criteria="Large_dist", 
                      nAnalogs = 15, return_list = FALSE)
str(Large_scale)
# analogs of local scale using criteria 2
lonmin=-1
lonmax=2
latmin=30
latmax=33
region=c(lonmin,lonmax,latmin,latmax)
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),nAnalogs=15,region=region, 
                      return_list = FALSE)
str(Local_scale)
# analogs of local scale using criteria 3
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),
                         latVar=seq(30,35,1.5),nAnalogs=15,region=region, 
                         return_list = FALSE)
str(Local_scalecor)

}
\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{
Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}

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