Newer
Older
#'@rdname CST_Analogs
#'@title Downscaling using Analogs based on large scale fields.
#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it}
#'@author Veronica Torralba, \email{veronica.torralba@cmcc.it}
#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
#'@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 to a 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 Minimum Euclidean distance in the large scale pattern
#'(i.e.SLP).
#'
#'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
#'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. For an advanced search of
#'Analogs (multiple Analogs, different criterias, further information from the
#'metrics and date of the selected Analogs) use the'Analog'
#'function within 'CSTools' package.
#'@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}
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
#'@param expL An 's2dv_cube' object 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.
#'@param obsL An 's2dv_cube' object 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.
#'@param expVar An 's2dv_cube' object 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'.
#'@param obsVar An 's2dv_cube' containing the field of the same variable as the
#' passed in parameter 'expVar' for the same region.
#'@param region A vector of length four indicating the minimum longitude, the
#' maximum longitude, the minimum latitude and the maximum latitude.
#'@param 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.}
#' Criteria 'Large_dist' is recommended for CST_Analogs, for an advanced use of
#' the criterias 'Local_dist' and 'Local_cor' use 'Analogs' function.
#'@param excludeTime An array of N named dimensions (coinciding with time
#' dimensions in expL)of character string(s) indicating the date(s) of the
#' observations in the format "dd/mm/yyyy" to be excluded during the search of
#' analogs. It can be NULL but if expL is not a forecast (time_expL contained in
#' time_obsL), by default time_expL will be removed during the search of analogs.
#'@param time_expL A character string indicating the date of the experiment
#' in the same format than time_obsL (i.e. "yyyy-mm-dd"). By default it is NULL
#' and dates are taken from element \code{$attrs$Dates} from expL.
#'@param time_obsL A character string indicating the date of the observations
#' in the date format (i.e. "yyyy-mm-dd"). By default it is NULL and dates are
#'@param region A vector of length four indicating the minimum longitude,
#' the maximum longitude, the minimum latitude and the maximum latitude.
#'@param 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.
#'@param AnalogsInfo A logical value. 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.
#'@param ncores The number of cores to use in parallel computation
#'@seealso \code{\link{CST_Load}}, \code{\link[s2dv]{Load}} and
#'\code{\link[s2dv]{CDORemap}}
#'@return An 's2dv_cube' object containing an array with the dowscaled values of
#'the best analogs in element 'data'. If 'AnalogsInfo' is TRUE, 'data' is a list
#'with an array of the downscaled fields and the analogs information in
#'elements 'analogs', 'metric' and 'dates'.
#'dim(expL) <- c(member = 10, lat = 4, lon = 5)
#'set.seed(2)
#'obsL <- c(rnorm(1:180), expL[1,,]*1.2)
#'dim(obsL) <- c(time = 10, lat = 4, lon = 5)
#'time_obsL <- as.POSIXct(paste(rep("01", 10), rep("01", 10), 1994:2003, sep = "-"),
#' format = "%d-%m-%y")
#'dim(time_obsL) <- c(time = 10)
#'lon <- seq(-1,5,1.5)
#'coords <- list(lon = seq(-1,5,1.5), lat = seq(30,35,1.5))
#'expL <- s2dv_cube(data = expL, coords = coords,
#' Dates = time_expL)
#'obsL <- s2dv_cube(data = obsL, coords = list(lon = lon, lat = lat),
#' Dates = time_obsL)
#'region <- c(min(lon), max(lon), min(lat), max(lat))
#'downscaled_field <- CST_Analogs(expL = expL, obsL = obsL, region = region)
#'
#'@import multiApply
#'@import abind
#'@importFrom ClimProjDiags SelBox Subset
#'@export
CST_Analogs <- function(expL, obsL, expVar = NULL, obsVar = NULL, region = NULL,
criteria = 'Large_dist', excludeTime = NULL,
time_expL = NULL, time_obsL = NULL,
nAnalogs = NULL, AnalogsInfo = FALSE,
if (!inherits(expL, "s2dv_cube") || !inherits(obsL, "s2dv_cube")) {
stop("Parameter 'expL' and 'obsL' must be of the class 's2dv_cube', ",
"as output by CSTools::CST_Load.")
}
if (!is.null(expVar) && !inherits(expVar, "s2dv_cube")) {
stop("Parameter 'expVar' must be of the class 's2dv_cube', ",
"as output by CSTools::CST_Load.")
}
if (!is.null(obsVar) && !inherits(obsVar, "s2dv_cube")) {
stop("Parameter 'obsVar' must be of the class 's2dv_cube', ",
"as output by CSTools::CST_Load.")
}
if (any(names(dim(obsL$data)) %in% 'sdate')) {
if (any(names(dim(obsL$data)) %in% 'ftime')) {
obsL <- CST_MergeDims(obsL, c('ftime', 'sdate'), rename_dim = 'time')
} else if (any(names(dim(obsL$data)) %in% 'time')) {
obsL <- CST_MergeDims(obsL, c('time', 'sdate'), rename_dim = 'time')
}
if (!is.null(obsVar)) {
if (any(names(dim(obsVar$data)) %in% 'sdate')) {
if (any(names(dim(obsVar$data)) %in% 'ftime')) {
obsVar <- CST_MergeDims(obsVar, c('ftime', 'sdate'),rename_dim = 'time')
} else if (any(names(dim(obsVar$data)) %in% 'time')) {
obsVar <- CST_MergeDims(obsVar, c('time', 'sdate'), rename_dim = 'time')
}
}
time_expL <- expL$attrs$Dates
time_obsL <- obsL$attrs$Dates
lon_expL_name <- names(expL$coords)[[which(names(expL$coords) %in% .KnownLonNames())]]
lat_expL_name <- names(expL$coords)[[which(names(expL$coords) %in% .KnownLatNames())]]
lon_obsL_name <- names(obsL$coords)[[which(names(obsL$coords) %in% .KnownLonNames())]]
lat_obsL_name <- names(obsL$coords)[[which(names(obsL$coords) %in% .KnownLatNames())]]
lonVar <- obsVar$coords[[which(names(obsVar$coords) %in% .KnownLonNames())]]
latVar <- obsVar$coords[[which(names(obsVar$coords) %in% .KnownLatNames())]]
res <- Analogs(expL$data, obsL$data, time_obsL = time_obsL,
time_expL = time_expL, lonL = expL$coords[[lon_expL_name]],
latL = expL$coords[[lat_expL_name]], expVar = expVar$data,
obsVar = obsVar$data, criteria = criteria,
excludeTime = excludeTime, region = region,
lonVar = as.vector(lonVar), latVar = as.vector(latVar),
nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
ncores = ncores)
if (AnalogsInfo) {
if (is.numeric(res$dates)) {
res$dates <- as.POSIXct(res$dates, origin = '1970-01-01', tz = 'UTC')
}
expL$coords[[lon_expL_name]] <- obsL$coords[[lon_obsL_name]]
expL$coords[[lat_expL_name]] <- obsL$coords[[lat_obsL_name]]
expL$coords[[lon_expL_name]] <- SelBox(obsL$data,
lon = as.vector(obsL$coords[[lon_obsL_name]]),
lat = as.vector(obsL$coords[[lat_obsL_name]]),
region = region)$lon
expL$coords[[lat_expL_name]] <- SelBox(obsL$data,
lon = as.vector(obsL$coords[[lon_obsL_name]]),
lat = as.vector(obsL$coords[[lat_obsL_name]]),
region = region)$lat
#'@rdname Analogs
#'@title Analogs based on large scale fields.
#'
#'@author M. Carmen Alvarez-Castro, \email{carmen.alvarez-castro@cmcc.it}
#'@author Maria M. Chaves-Montero, \email{mariadm.chaves@cmcc.it }
#'@author Veronica Torralba, \email{veronica.torralba@cmcc.it}
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
#'@author Nuria Perez-Zanon \email{nuria.perez@bsc.es}
#'
#'@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.
#'
#'@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}
#'
#'@param 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.
#'@param 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
#' single temporal dimension with the maximum number of available observations.
#'@param time_obsL A character string indicating the date of the observations
#' in the format "dd-mm-yyyy". Reference time to search for analogs.
#'@param time_expL An array of N named dimensions (coinciding with time
#' dimensions in expL) of character string(s) indicating the date(s) of the
#' experiment in the format "dd-mm-yyyy". Time(s) to find the analogs.
#'@param lonL A vector containing the longitude of parameter 'expL'.
#'@param latL A vector containing the latitude of parameter 'expL'.
#'@param excludeTime An array of N named dimensions (coinciding with time
#' dimensions in expL) of character string(s) indicating the date(s) of the
#' observations in the format "dd/mm/yyyy" to be excluded during the search of
#' analogs. It can be NULL but if expL is not a forecast (time_expL contained
#' in time_obsL), by default time_expL will be removed during the search of
#' analogs.
#'@param 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'.
#'@param obsVar An array of N named dimensions containing the field of the
#' same variable as the passed in parameter 'expVar' for the same region.
#'@param AnalogsInfo A logical value. If it is TRUE it returns 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.
#'@param 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.}
#'@param lonVar A vector containing the longitude of parameter 'expVar'.
#'@param latVar A vector containing the latitude of parameter 'expVar'.
#'@param region A vector of length four indicating the minimum longitude,
#' the maximum longitude, the minimum latitude and the maximum latitude.
#'@param 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.
#'@param ncores The number of cores to use in parallel computation.
#'@return An array with the dowscaled values of the best analogs for the criteria
#'selected. If 'AnalogsInfo' is set to TRUE it returns a list with an array
#'of the dowsncaled field and the analogs information in elements 'analogs',
#''metric' and 'dates'.
#'# 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
#'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:
#'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",
#' lonL = seq(-1, 5, 1.5),latL = 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", lonL = seq(-1, 5, 1.5),
#' latL = 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", lonL = seq(-1, 5, 1.5),
#' latL = seq(30, 35, 1.5), region = region,
#' time_expL = "01-10-2000",
#'
#'# 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,
#' criteria = "Local_cor", lonL = seq(-1, 5, 1.5),
#' time_expL = "01-10-2000", latL = seq(30, 35, 1.5),
#' lonVar = seq(-1, 5, 1.5), 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,
#' lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5),
#' criteria = "Local_cor", lonL = seq(-1,5,1.5),
#' time_expL = "01-10-2000", latL = seq(30, 35, 1.5),
#'#'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",
#'Local_scale <- Analogs(expL = expSLP, obsL = obsSLP, time_obsL = time_obsSLP,
#' time_expL = "01-10-2000", criteria = "Local_dist",
#' lonL = seq(-1, 5, 1.5), latL = 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",
#' lonL = seq(-1, 5, 1.5), latL = seq(30, 35, 1.5),
#' lonVar = seq(-1, 5, 1.5), latVar = seq(30, 35, 1.5),
#' nAnalogs = 7, region = region,
#'#Example 10: Downscaling using criteria 'Large_dist' and a single variable,
#'# more than 1 sdate:
#'expSLP <- rnorm(1:40)
#'dim(expSLP) <- c(sdate = 2, lat = 4, lon = 5)
#'obsSLP <- c(rnorm(1:180), expSLP * 1.2)
#'dim(obsSLP) <- c(time = 11, lat = 4, lon = 5)
#'time_obsSLP <- paste(rep("01", 11), rep("01", 11), 1993 : 2003, sep = "-")
#'time_expSLP <- paste(rep("01", 2), rep("01", 2), 1994 : 1995, sep = "-")
#'excludeTime <- c("01-01-2003", "01-01-2003")
#'dim(excludeTime) <- c(sdate = 2)
#'downscale_field_exclude <- Analogs(expL = expSLP, obsL = obsSLP,
#' time_obsL = time_obsSLP, time_expL = time_expSLP,
#' excludeTime = excludeTime, AnalogsInfo = TRUE)
#'@import multiApply
#'@import abind
#'@importFrom ClimProjDiags SelBox Subset
Analogs <- function(expL, obsL, time_obsL, time_expL = NULL,
lonL = NULL, latL = NULL, expVar = NULL,
obsVar = NULL, criteria = "Large_dist",
excludeTime = NULL, lonVar = NULL, latVar = NULL,
region = NULL, nAnalogs = NULL,
AnalogsInfo = FALSE, ncores = NULL) {
# Check inputs
# expL, obsL
if (!is.array(expL) || !is.numeric(expL)) {
stop("Parameter 'expL' must be a numeric array.")
if (!is.array(obsL) || !is.numeric(obsL)) {
stop("Parameter 'obsL' must be a numeric array.")
obsdims <- names(dim(obsL))
expdims <- names(dim(expL))
if (is.null(expdims)) {
stop("Parameter 'expL' must have dimension names.")
}
if (is.null(obsdims)) {
stop("Parameter 'obsL' must have dimension names.")
warning("Parameter 'expL' contains NA values.")
}
if (any(is.na(obsL))) {
warning("Parameter 'obsL' contains NA values.")
if (!any(.KnownLonNames() %in% obsdims) | !any(.KnownLonNames() %in% expdims)) {
stop("Parameter 'expL' and 'obsL' must have longitudinal dimension.")
if (!any(.KnownLatNames() %in% obsdims) | !any(.KnownLatNames() %in% expdims)) {
stop("Parameter 'expL' and 'obsL' must have latitudinal dimension.")
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
# criteria
if (!criteria %in% c('Large_dist', 'Local_dist', 'Local_cor')) {
stop("Parameter 'criteria' can only be: 'Large_dist', 'Local_dist' or 'Local_cor'.")
}
if (length(criteria) > 1) {
warning("Only first element of 'criteria' parameter will be used.")
criteria <- criteria[1]
}
# lonL, latL, lonVar, latVar
if (criteria == "Local_dist" | criteria == "Local_cor") {
if (is.null(lonL) | is.null(latL)) {
stop("Parameters 'lonL' and 'latL' cannot be NULL.")
}
if (!is.numeric(lonL) | !is.numeric(latL)) {
stop("Parameters 'lonL' and 'latL' must be numeric.")
}
if (!is.null(dim(lonL)) | !is.null(dim(latL))) {
if (length(dim(lonL)) == 1 & length(dim(latL)) == 1) {
lonL <- as.vector(lonL)
latL <- as.vector(latL)
} else {
stop("Parameters 'lonL' and 'latL' need to be a vector.")
}
}
} else if (criteria == "Local_cor") {
if (is.null(lonVar) | is.null(latVar)) {
stop("Parameters 'lonVar' and 'latVar' cannot be NULL.")
}
if (!is.numeric(lonVar) | !is.numeric(latVar)) {
stop("Parameters 'lonVar' and 'latVar' must be numeric.")
}
if (!is.null(dim(lonVar)) | !is.null(dim(latVar))) {
if (length(dim(lonVar)) == 1 & length(dim(latVar)) == 1) {
lonVar <- as.vector(lonVar)
latVar <- as.vector(latVar)
} else {
stop("Parameters 'lonVar' and 'latVar' need to be a vector.")
}
}
}
# expVar and obsVar
if (!is.null(expVar) & is.null(obsVar)) {
expVar <- NULL
warning("Parameter 'expVar' is set to NULL as parameter 'obsVar',
large scale field will be returned.")
}
if (is.null(expVar) & is.null(obsVar)) {
warning("Parameter 'expVar' and 'obsVar' are NULLs, downscaling/listing
same variable as obsL and expL'.")
if (!is.null(obsVar) & is.null(expVar) & criteria == "Local_cor") {
stop("Parameter 'expVar' cannot be NULL.")
if (is.null(nAnalogs) & criteria != "Large_dist") {
nAnalogs = length(time_obsL)
warning("Parameter 'nAnalogs' is NULL and is set to the same length of",
"'time_obsL' by default")
if (is.null(nAnalogs) & criteria == "Large_dist") {
nAnalogs <- 1
# time_obsL, time_expL
if (is.null(time_obsL)) {
stop("Parameter 'time_obsL' cannot be NULL.")
}
stop("Parameter 'time_expL' cannot be NULL.")
if (!inherits(time_obsL, "character")) {
Carmen Alvarez-Castro
committed
warning('imposing time_obsL to be a character')
time_obsL <- format(as.Date(time_obsL), '%d-%m-%Y')
Carmen Alvarez-Castro
committed
}
if (!inherits(time_expL, "character")) {
Carmen Alvarez-Castro
committed
warning('imposing time_expL to be a character')
time_expL <- format(as.Date(time_expL), '%d-%m-%Y')
Carmen Alvarez-Castro
committed
}
if (!is.null(excludeTime)) {
if (!inherits(excludeTime, "character")) {
Carmen Alvarez-Castro
committed
warning('imposing excludeTime to be a character')
excludeTime <- format(as.Date(excludeTime),'%d-%m-%Y')
Carmen Alvarez-Castro
committed
}
}
# time_obsL
if (is.null(time_obsL)) {
stop("Parameter 'time_obsL' cannot be NULL")
}
if (any(names(dim(obsL)) %in% 'ftime')) {
if (any(names(dim(obsL)) %in% 'time')) {
stop("Multiple temporal dimensions ('ftime' and 'time') found",
"in parameter 'obsL'.")
} else {
time_pos_obsL <- which(names(dim(obsL)) == 'ftime')
names(dim(obsL))[time_pos_obsL] <- 'time'
if (any(names(dim(expL)) %in% 'ftime')) {
time_pos_expL <- which(names(dim(expL)) == 'ftime')
names(dim(expL))[time_pos_expL] <- 'time'
}
}
}
if (any(names(dim(obsVar)) %in% 'ftime')) {
if (any(names(dim(obsVar)) %in% 'time')) {
stop("Multiple temporal dimensions ('ftime' and 'time') found",
"in parameter 'obsVar'.")
} else {
time_pos_obsVar <- which(names(dim(obsVar)) == 'ftime')
names(dim(obsVar))[time_pos_obsVar] <- 'time'
if (any(names(dim(expVar)) %in% 'ftime')) {
time_pos_expVar <- which(names(dim(expVar)) == 'ftime')
names(dim(expVar))[time_pos_expVar] <- 'time'
}
}
}
}
if ((any(names(dim(obsL)) %in% 'sdate')) &&
(any(names(dim(obsL)) %in% 'time'))) {
dims_obsL <- dim(obsL)
pos_sdate <- which(names(dim(obsL)) == 'sdate')
pos_time <- which(names(dim(obsL)) == 'time')
pos <- 1 : length(dim(obsL))
pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
obsL <- aperm(obsL, pos)
dim(obsL) <- c(time = prod(dims_obsL[c(pos_time, pos_sdate)]),
dims_obsL[-c(pos_time, pos_sdate)])
} else {
if (any(names(dim(obsL)) %in% 'sdate')) {
dims_obsL <- dim(obsL)
pos_sdate <- which(names(dim(obsL)) == 'sdate')
pos <- 1 : length(dim(obsL))
pos <- c( pos_sdate, pos[-c(pos_sdate)])
obsL <- aperm(obsL, pos)
dim(obsL) <- c(time = prod(dims_obsL[c(pos_sdate)]),
dims_obsL[-c( pos_sdate)])
} else {
if (any(names(dim(obsL)) %in% 'time')) {
dims_obsL <- dim(obsL)
pos_time <- which(names(dim(obsL)) == 'time')
if(length(time_obsL) != dim(obsL)[pos_time]) {
Eva Rifà
committed
stop("'time_obsL' and 'obsL' must have same length in the temporal
dimension.")
}
pos <- 1 : length(dim(obsL))
pos <- c(pos_time, pos[-c(pos_time)])
obsL <- aperm(obsL, pos)
dim(obsL) <- c(time = prod(dims_obsL[pos_time]),
dims_obsL[-c(pos_time)])
} else {
stop("Parameter 'obsL' must have a temporal dimension named 'time'.")
}
if (any(names(dim(obsVar)) %in% 'sdate')) {
if (any(names(dim(obsVar)) %in% 'time')) {
dims_obsVar <- dim(obsVar)
pos_sdate <- which(names(dim(obsVar)) == 'sdate')
pos_time <- which(names(dim(obsVar)) == 'time')
pos <- 1 : length(dim(obsVar))
pos <- c(pos_time, pos_sdate, pos[-c(pos_sdate,pos_time)])
obsVar <- aperm(obsVar, pos)
dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time, pos_sdate)]),
dims_obsVar[-c(pos_time, pos_sdate)])
} else {
dims_obsVar <- dim(obsVar)
pos_sdate <- which(names(dim(obsVar)) == 'sdate')
pos <- 1 : length(dim(obsVar))
pos <- c(pos_sdate, pos[-c(pos_sdate)])
obsVar <- aperm(obsVar, pos)
dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_sdate)]),
dims_obsVar[-c(pos_sdate)])
}
} else {
if (any(names(dim(obsVar)) %in% 'time')) {
dims_obsVar <- dim(obsVar)
pos_time <- which(names(dim(obsVar)) == 'time')
if (length(time_obsL) != dim(obsVar)[pos_time]) {
stop(" 'time_obsL' and 'obsVar' must have same length in the temporal
dimension.")}
pos <- 1 : length(dim(obsVar))
pos <- c(pos_time, pos[-c(pos_time)])
obsVar <- aperm(obsVar, pos)
dim(obsVar) <- c(time = prod(dims_obsVar[c(pos_time)]),
dims_obsVar[-c(pos_time)])
} else {
stop("Parameter 'obsVar' must have a temporal dimension named 'time'.")
if (is.null(region) && criteria != 'Large_dist') {
if (!is.null(lonVar) & !is.null(latVar)) {
region <- c(min(lonVar), max(lonVar), min(latVar), max(latVar))
stop("Parameters 'lonVar' and 'latVar' must be given in criteria
'Local_dist' and 'Local_cor'")
}
}
if (any(names(dim(expL)) %in% c('ftime', 'leadtime', 'ltime'))) {
if (length(which(names(dim(expL)) %in%
c('ftime', 'leadtime', 'ltime') == TRUE)) > 1) {
stop("Parameter 'expL' cannot have multiple forecast time dimensions")
} else {
names(dim(expL))[which(names(dim(expL)) %in% c('ftime','leadtime','ltime'))] <- 'time'
}
}
# remove dimension length 1 to simplify outputs:
if (any(dim(obsL) == 1)) {
obsL <- adrop(obsL, which(dim(obsL) == 1))
}
if (any(dim(expL) == 1)) {
expL <- adrop(expL, which(dim(expL) == 1))
}
if (!is.null(obsVar)) {
if (any(dim(obsVar) == 1)) {
obsVar <- adrop(obsVar, which(dim(obsVar) == 1))
}
}
if (!is.null(expVar)) {
if (any(dim(expVar) == 1)) {
expVar <- adrop(expVar, which(dim(expVar) == 1))
}
}
names(dim(expL)) <- replace_repeat_dimnames(names(dim(expL)),
names(dim(obsL)))
if (!is.null(expVar)) {
names(dim(expVar)) <- replace_repeat_dimnames(names(dim(expVar)),
if (is.null(excludeTime)) {
Eva Rifà
committed
excludeTime <- vector(mode = "character", length = length(time_expL))
if (length(time_expL) == length(excludeTime)) {
if (any(names(dim(expL)) %in% c('sdate_exp'))) {
dim(time_expL) <- c(dim(expL)['sdate_exp'], dim(expL)['time_exp'])
} else if (any(names(dim(expL)) %in% c('sdate'))) {
if (any(names(dim(expL)) %in% c('time_exp'))) {
dim(time_expL) <- c(dim(expL)['sdate'], dim(expL)['time_exp'])
dim(excludeTime) <- c(dim(expL)['sdate'], dim(expL)['time_exp'])
} else if (any(names(dim(expL)) %in% c('time'))) {
dim(time_expL) <- c(dim(expL)['sdate'], dim(expL)['time'])
dim(excludeTime) <- c(dim(expL)['sdate'], dim(expL)['time'])
} else {
dim(time_expL) <- c(dim(expL)['sdate'])
dim(excludeTime) <- c(dim(expL)['sdate'])
}
} else if (any(names(dim(expL)) %in% c('time'))) {
dim(time_expL) <- c(dim(expL)['time'])
dim(excludeTime) <- c(dim(expL)['time'])
} else if (any(names(dim(expL)) %in% c('time_exp'))) {
dim(time_expL) <- c(dim(expL)['time_exp'])
dim(excludeTime) <- c(dim(expL)['time_exp'])
}
if (!AnalogsInfo) {
if (is.null(obsVar)) {
Carmen Alvarez-Castro
committed
res <- Apply(list(expL, obsL),
target_dims = list(c('lat', 'lon'), c('time','lat','lon')),
fun = .analogs, time_obsL, expVar = expVar,
time_expL = time_expL, excludeTime = excludeTime,
obsVar = obsVar, criteria = criteria,
lonL = lonL, latL = latL,
lonVar = lonVar, latVar = latVar, region = region,
nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
output_dims = c('nAnalogs', 'lat', 'lon'),
ncores = ncores)$output1
} else if (!is.null(obsVar) && is.null(expVar)) {
Carmen Alvarez-Castro
committed
res <- Apply(list(expL, obsL, obsVar),
target_dims = list(c('lat', 'lon'), c('time','lat','lon'),
c('time', 'lat', 'lon')),
fun = .analogs, time_obsL,
time_expL = time_expL, excludeTime = excludeTime,
expVar = expVar, criteria = criteria,
lonVar = lonVar, latVar = latVar, region = region,
nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
output_dims = c('nAnalogs', 'lat', 'lon'),
ncores = ncores)$output1
} else if (!is.null(obsVar) && !is.null(expVar)) {
Carmen Alvarez-Castro
committed
res <- Apply(list(expL, obsL, obsVar, expVar),
target_dims = list(c('lat', 'lon'), c('time','lat','lon'),
c('time','lat','lon'), c('lat','lon')),
criteria = criteria, time_obsL,
time_expL = time_expL, excludeTime = excludeTime,
lonVar = lonVar, latVar = latVar, region = region,
nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
output_dims = c('nAnalogs', 'lat', 'lon'),
ncores = ncores)$output1
Carmen Alvarez-Castro
committed
res <- Apply(list(expL, obsL),
target_dims = list(c('lat', 'lon'), c('time','lat','lon')),
fun = .analogs, time_obsL, expVar = expVar,
time_expL = time_expL, excludeTime = excludeTime,
obsVar = obsVar, criteria = criteria,
lonVar = lonVar, latVar = latVar, region = region,
nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
analogs = c('nAnalogs'),
metric = c('nAnalogs', 'metric'),
dates = c('nAnalogs')),
ncores = ncores)
} else if (!is.null(obsVar) && is.null(expVar)) {
Carmen Alvarez-Castro
committed
res <- Apply(list(expL, obsL, obsVar),
target_dims = list(c('lat', 'lon'), c('time','lat','lon'),
c('time', 'lat', 'lon')),
fun = .analogs, time_obsL,
time_expL = time_expL, excludeTime = excludeTime,
expVar = expVar, criteria = criteria,
lonVar = lonVar, latVar = latVar, region = region,
nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
analogs = c('nAnalogs'),
metric = c('nAnalogs', 'metric'),
dates = c('nAnalogs')),
ncores = ncores)
} else if (!is.null(obsVar) && !is.null(expVar)) {
Carmen Alvarez-Castro
committed
res <- Apply(list(expL, obsL, obsVar, expVar),
target_dims = list(c('lat', 'lon'), c('time', 'lat', 'lon'),
c('time', 'lat', 'lon'), c('lat', 'lon')),
time_expL = time_expL, excludeTime = excludeTime,
lonVar = lonVar, latVar = latVar, region = region,
nAnalogs = nAnalogs, AnalogsInfo = AnalogsInfo,
output_dims = list(fields = c('nAnalogs', 'lat', 'lon'),
analogs = c('nAnalogs'),
metric = c('nAnalogs', 'metric'),
dates = c('nAnalogs')),
ncores = ncores)
}
}
return(res)
}
.analogs <- function (expL, obsL, time_expL, excludeTime = NULL,
obsVar = NULL, expVar = NULL,
time_obsL, criteria = "Large_dist",
lonL = NULL, latL = NULL,
lonVar = NULL, latVar = NULL, region = NULL,
nAnalogs = NULL, AnalogsInfo = FALSE) {
if (all(excludeTime == "")) {
excludeTime = NULL
}
if (!is.null(obsL)) {
#obsL <- replace_time_dimnames(obsL)
if (any(time_expL %in% time_obsL)) {
excludeTime <- time_expL
warning("Parameter 'excludeTime' is NULL, time_obsL contains
time_expL, so, by default, the date of
time_expL will be excluded in the search of analogs")
if(any(time_expL %!in% excludeTime)) {
excludeTime <- c(excludeTime, time_expL)
warning("Parameter 'excludeTime' is not NULL, time_obsL contains
time_expL, so, by default, the date of
time_expL will be excluded in the search of analogs")
}
}
time_ref <- time_obsL[-c(which(time_obsL %in% excludeTime))]
posdim <- which(names(dim(obsL)) == 'time')
posref <- which(time_obsL %in% time_ref)
obsT <- Subset(obsL, along = posdim, indices = posref)
if (!is.null(obsVar)) {
obsTVar <- Subset(obsVar, along = posdim, indices = posref)
}
time_obsL <- time_ref
obsL <- obsT
if (!is.null(obsVar)) {
obsVar <- obsTVar
}
} else {
if (is.null(excludeTime)) {
if (!is.null(obsVar)) {
warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
time_expL, obsVar not NULL")
} else {
warning("Parameter 'excludeTime' is NULL, time_obsL does not contain
}
} else {
time_ref <- time_obsL[-c(which(time_obsL %in% excludeTime))]
posdim <- which(names(dim(obsL)) == 'time')
posref <- which(time_obsL %in% time_ref)
obsT <- Subset(obsL, along = posdim, indices = posref)
if (!is.null(obsVar)) {
obsTVar <- Subset(obsVar, along = posdim, indices = posref)
}
time_obsL <- time_ref
obsL <- obsT
if (!is.null(obsVar)) {
obsVar <- obsTVar
}
if (!is.null(obsVar)) {
warning("Parameter 'excludeTime' has a value and time_obsL does not
contain time_expL, obsVar not NULL")
} else {
warning("Parameter 'excludeTime' has a value and time_obsL does not
}
}
}
} else {
stop("parameter 'obsL' cannot be NULL")
Eva Rifà
committed
if (length(time_obsL) == 0) {
stop("Parameter 'time_obsL' can not be length 0")
}
Analog_result <- FindAnalog(expL = expL, obsL = obsL, time_obsL = time_obsL,
expVar = expVar, obsVar = obsVar,
criteria = criteria,
AnalogsInfo = AnalogsInfo,
nAnalogs = nAnalogs,
lonL = lonL, latL = latL, lonVar = lonVar,
if (AnalogsInfo == TRUE) {
return(list(AnalogsFields = Analog_result$AnalogsFields,
AnalogsInfo = Analog_result$Analog,
AnalogsMetric = Analog_result$metric,
AnalogsDates = Analog_result$dates))
} else {
return(AnalogsFields = Analog_result$AnalogsFields)
FindAnalog <- function(expL, obsL, time_obsL, expVar, obsVar, criteria,
lonL, latL, lonVar,
latVar, region, nAnalogs = nAnalogs,
AnalogsInfo = AnalogsInfo) {
position <- Select(expL = expL, obsL = obsL, expVar = expVar,
obsVar = obsVar, criteria = criteria,
lonL = lonL, latL = latL, lonVar = lonVar,
metrics <- Select(expL = expL, obsL = obsL, expVar = expVar,
obsVar = obsVar, criteria = criteria, lonL = lonL,
latL = latL, lonVar = lonVar,
latVar = latVar, region = region)$metric.original
best <- Apply(list(position), target_dims = c('time', 'pos'),
fun = BestAnalog, criteria = criteria,
AnalogsInfo = AnalogsInfo, nAnalogs = nAnalogs)$output1
Analogs_dates <- time_obsL[best]
dim(Analogs_dates) <- dim(best)
if (all(!is.null(region), !is.null(lonVar), !is.null(latVar))) {
if (is.null(obsVar)) {
obsVar <- SelBox(obsL, lon = lonL, lat = latL, region = region)$data
expVar <- SelBox(expL, lon = lonL, lat = latL, region = region)$data
Analogs_fields <- Subset(obsVar,
along = which(names(dim(obsVar)) == 'time'),
indices = best)
warning("Parameter 'obsVar' is NULL and the returned field ",
"will be computed from 'obsL' (same variable).")
} else {
obslocal <- SelBox(obsVar, lon = lonVar, lat = latVar,
region = region)$data
Analogs_fields <- Subset(obslocal,
along = which(names(dim(obslocal)) == 'time'),
indices = best)
}
} else {
warning("One or more of the parameter 'region', 'lonVar' and 'latVar'",
" are NULL and the large scale field will be returned.")
if (is.null(obsVar)) {
Analogs_fields <- Subset(obsL, along = which(names(dim(obsL)) == 'time'),
indices = best)
} else {
Analogs_fields <- Subset(obsVar,
along = which(names(dim(obsVar)) == 'time'),
indices = best)
}
}
lon_dim <- which(names(dim(Analogs_fields)) == 'lon')
lat_dim <- which(names(dim(Analogs_fields)) == 'lat')
Analogs_metrics <- Subset(metrics,
along = which(names(dim(metrics)) == 'time'),
indices = best)
analog_number <- as.numeric(1:nrow(Analogs_metrics))
dim(analog_number) <- c(nAnalog = length(analog_number))
dim(Analogs_dates) <- c(nAnalog = length(Analogs_dates))
return(list(AnalogsFields = Analogs_fields,
Analog = analog_number,
metric = Analogs_metrics,
dates = Analogs_dates))
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
}
BestAnalog <- function(position, nAnalogs = nAnalogs, AnalogsInfo = FALSE,
criteria = 'Large_dist') {
pos_dim <- which(names(dim(position)) == 'pos')
if (dim(position)[pos_dim] == 1) {
pos1 <- Subset(position, along = pos_dim, indices = 1)
if (criteria != 'Large_dist') {
warning("Dimension 'pos' in parameter 'position' has length 1,",
" criteria 'Large_dist' will be used.")
criteria <- 'Large_dist'
}
} else if (dim(position)[pos_dim] == 2) {
pos1 <- Subset(position, along = pos_dim, indices = 1)
pos2 <- Subset(position, along = pos_dim, indices = 2)
if (criteria == 'Local_cor') {
warning("Dimension 'pos' in parameter 'position' has length 2,",
" criteria 'Local_dist' will be used.")
criteria <- 'Local_dist'
}
} else if (dim(position)[pos_dim] == 3) {
pos1 <- Subset(position, along = pos_dim, indices = 1)
pos2 <- Subset(position, along = pos_dim, indices = 2)
pos3 <- Subset(position, along = pos_dim, indices = 3)
if (criteria != 'Local_cor') {
warning("Parameter 'criteria' is set to", criteria, ".")
}
} else {
stop("Parameter 'position' has dimension 'pos' of different ",
"length than expected (from 1 to 3).")
}
if (criteria == 'Large_dist') {
if (AnalogsInfo == FALSE) {
pos <- pos1[1]
} else {
pos <- pos1[1 : nAnalogs]
}
Eva Rifà
committed
} else if (criteria == 'Local_dist') {
pos1 <- pos1[1 : nAnalogs]
pos2 <- pos2[1 : nAnalogs]
best <- match(pos1, pos2)
Eva Rifà
committed
if (length(best) == 1) {