% Generated by roxygen2: do not edit by hand % Please edit documentation in R/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, ncores = ncores) } \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: # 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, # excludeTime=NULL 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") 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(time = 10,lat = 4, lon = 5) 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,time_expL = "01-01-1994") 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 (AnalogsInfo # =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,time_expL = "01-01-2003", AnalogsInfo=TRUE, excludeTime= "01-01-2003") 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 (AnalogsInfo =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, time_expL = "01-10-2003", AnalogsInfo=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, time_expL = "01-10-2000", nAnalogs = 10, AnalogsInfo=TRUE) 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. AnalogsInfo=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, 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) 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, time_expL = "01-10-2000", nAnalogs = 10, AnalogsInfo = 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. AnalogsInfo=FALSE. two options # for nAnalogs 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), time_expL = "01-10-2000", latVar=seq(30,35,1.5),nAnalogs=8,region=region, AnalogsInfo = FALSE) str(Local_scalecor) # 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) Local_scalecor$AnalogsInfo expSLP <- rnorm(1:20) dim(expSLP) <- c(lat = 4, lon = 5) obsSLP <- c(rnorm(1:180),expSLP*2) dim(obsSLP) <- c(lat = 4, lon = 5, time = 10) time_obsSLP <- paste(rep("01", 10), rep("01", 10), 1994 : 2003, sep = "-") # analogs of large scale using criteria 1 Large_scale <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP, criteria="Large_dist", time_expL = "01-10-2000", nAnalogs = 7, AnalogsInfo = TRUE) str(Large_scale) Large_scale$AnalogsInfo # 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, 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) str(Local_scale) Local_scale$AnalogsInfo # analogs of local scale using criteria 3 Local_scalecor <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP, obsVar=obsSLP,expVar=expSLP, time_expL = "01-10-2000", criteria="Local_cor",lonVar=seq(-1,5,1.5), latVar=seq(30,35,1.5),nAnalogs=7,region=region, AnalogsInfo = TRUE) str(Local_scalecor) Local_scalecor$AnalogsInfo # 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) Large_scale <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP, criteria="Large_dist",time_expL = "01-10-2000", nAnalogs = 7, AnalogsInfo=TRUE) str(Large_scale) Large_scale$AnalogsInfo # # analogs of local scale using criteria 2 lonmin=-1 lonmax=2 latmin=30 latmax=33 region=c(lonmin,lonmax,latmin,latmax) Local_scale <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP, obsVar=obs.pr,time_expL = "01-10-2000", criteria="Local_dist",lonVar=seq(-1,5,1.5), latVar=seq(30,35,1.5),nAnalogs=7,region=region, AnalogsInfo = TRUE) str(Local_scale) Local_scale$AnalogsInfo # # analogs of local scale using criteria 3 Local_scalecor <- Analogs(expL=expSLP, obsL=obsSLP, time_obsL=time_obsSLP, obsVar=obs.pr,expVar=exp.pr, 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) str(Local_scalecor) Local_scalecor$AnalogsInfo } \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} }