diff --git a/R/BEI_PDFBest.R b/R/BEI_PDFBest.R index 61313008f103ee8ab7a5285d98e32a416f759be0..d2d98af8ccf2b47cc048c1d232c712d2099066f0 100644 --- a/R/BEI_PDFBest.R +++ b/R/BEI_PDFBest.R @@ -6,12 +6,12 @@ #'Probability Density Functions (PDFs) (e.g. NAO index) obtained to combining #'the Index PDFs for two Seasonal Forecast Systems (SFSs), the Best Index #'estimation (see Sanchez-Garcia, E. et al (2019), -#'\url{https://doi.org/10.5194/asr-16-165-2019} for more details about the +#'\doi{10.5194/asr-16-165-2019} for more details about the #'methodology applied to estimate the Best Index). #' #'@references Regionally improved seasonal forecast of precipitation through #'Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +#' Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} #' #'@param index_obs Index (e.g. NAO index) array from an observational database #' or reanalysis with at least a temporal dimension (by default 'time'), @@ -55,7 +55,7 @@ #' error or bias between observation and predictions to correct the predicted #' values), and "LMEV" (a bias correction scheme based on a linear model using #' ensemble variance of index as predictor). (see Sanchez-Garcia, E. et al -#' (2019), https://doi.org/10.5194/asr-16-165-2019 for more details). +#' (2019), \doi{10.5194/asr-16-165-2019} for more details). #'@param time_dim_name A character string indicating the name of the temporal #' dimension, by default 'time'. #'@param na.rm Logical (default = FALSE). Should missing values be removed? @@ -397,7 +397,7 @@ BEI_PDFBest <- function(index_obs, index_hind1, index_hind2, index_fcst1 = NULL, #' #'@references Regionally improved seasonal forecast of precipitation through Best #'estimation of winter NAO, Sanchez-Garcia, E. et al., -#'Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +#'Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} #' #'@param index_hind Index (e.g. NAO index) array from SFSs #' with at least two dimensions (time , member) or (time, statistic). @@ -615,7 +615,7 @@ PDFIndexHind <- function(index_hind, index_obs, method ='ME', #' #'@references Regionally improved seasonal forecast of precipitation through Best #' estimation of winter NAO, Sanchez-Garcia, E. et al., -#' Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +#' Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} #' #'@param index_hind Index (e.g. NAO index) array from SFSs #' with at least two dimensions (time , member) or (time, statistic). diff --git a/R/BEI_Weights.R b/R/BEI_Weights.R index f5cc7f59336d24f2091a0d76daba058b4507c9d9..e550af1eba5d70846b46528f4fe30cc42955ca4d 100644 --- a/R/BEI_Weights.R +++ b/R/BEI_Weights.R @@ -11,7 +11,7 @@ #' #'@references Regionally improved seasonal forecast of precipitation through #'Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#'Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +#'Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} #' #'@param index_weight Index (e.g. NAO index) array, from a dataset of SFSs #' for a period of years, with at least dimensions 'member'. diff --git a/R/CST_BEI_Weighting.R b/R/CST_BEI_Weighting.R index 885e1d84714dfe1ea1ed40f2c2659d2434ea72cf..adc268a2ad2e8b48b95579be87891d9819064616 100644 --- a/R/CST_BEI_Weighting.R +++ b/R/CST_BEI_Weighting.R @@ -9,7 +9,7 @@ #' #'@references Regionally improved seasonal forecast of precipitation through #'Best estimation of winter NAO, Sanchez-Garcia, E. et al., -#'Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +#'Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} #' #'@param var_exp An object of the class 's2dv_cube' containing the variable #' (e.g. precipitation, temperature, NAO index) array. @@ -106,7 +106,7 @@ CST_BEI_Weighting <- function(var_exp, aweights, terciles = NULL, #' #'@references Regionally improved seasonal forecast of precipitation through Best #'estimation of winter NAO, Sanchez-Garcia, E. et al., -#'Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#'Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} #' #'@param var_exp Variable (e.g. precipitation, temperature, NAO index) #' array from a SFS with at least dimensions (time, member) for a spatially @@ -234,7 +234,7 @@ BEI_EMWeighting <- function(var_exp, aweights, time_dim_name = 'time', #' #'@references Regionally improved seasonal forecast of precipitation through Best #'estimation of winter NAO, Sanchez-Garcia, E. et al., -#'Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +#'Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} #' #'@param var_exp Variable (e.g. precipitation, temperature, NAO index) #' array from a SFS with at least dimensions (time, member) for a spatially @@ -439,7 +439,7 @@ BEI_ProbsWeighting <- function(var_exp, aweights, terciles, #' #'@references Regionally improved seasonal forecast of precipitation through Best #'estimation of winter NAO, Sanchez-Garcia, E. et al., -#'Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +#'Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} #' #'@param var_exp Variable (e.g. precipitation, temperature, NAO index) #' array from a SFS with at least dimensions (time, member) for a spatially diff --git a/R/CST_BiasCorrection.R b/R/CST_BiasCorrection.R index ae5b61f98c2be98f1274875e640ecf00b30ba86d..ba3f1422a718b03b63ae069b4a2d108154c143e3 100644 --- a/R/CST_BiasCorrection.R +++ b/R/CST_BiasCorrection.R @@ -39,7 +39,7 @@ #'@references Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. #'Davis (2017). Seasonal climate prediction: a new source of information for #'the management of wind energy resources. Journal of Applied Meteorology and -#'Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, +#'Climatology, 56, 1231-1247, \doi{10.1175/JAMC-D-16-0204.1}. (CLIM4ENERGY, #'EUPORIAS, NEWA, RESILIENCE, SPECS) #' #'@examples @@ -131,7 +131,7 @@ CST_BiasCorrection <- function(exp, obs, exp_cor = NULL, na.rm = FALSE, #'@references Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. #'Davis (2017). Seasonal climate prediction: a new source of information for the #'management of wind energy resources. Journal of Applied Meteorology and -#'Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, +#'Climatology, 56, 1231-1247, \doi{10.1175/JAMC-D-16-0204.1}. (CLIM4ENERGY, #'EUPORIAS, NEWA, RESILIENCE, SPECS) #' #'@examples diff --git a/R/CST_Calibration.R b/R/CST_Calibration.R index 9b3c33fae831a5a457024305c7dc31d0aa1d7f70..79f51320f60fab62195235ee2b53969896cf7483 100644 --- a/R/CST_Calibration.R +++ b/R/CST_Calibration.R @@ -93,18 +93,18 @@ #' #'@references Doblas-Reyes F.J, Hagedorn R, Palmer T.N. The rationale behind the #'success of multi-model ensembles in seasonal forecasting-II calibration and -#'combination. Tellus A. 2005;57:234-252. doi:10.1111/j.1600-0870.2005.00104.x +#'combination. Tellus A. 2005;57:234-252. \doi{10.1111/j.1600-0870.2005.00104.x} #'@references Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., #'Hermanson, L., & Robinson, N. (2014). Do seasonal-to-decadal climate #'predictions underestimate the predictability of the read world? Geophysical -#'Research Letters, 41(15), 5620-5628. doi: 10.1002/2014GL061146 +#'Research Letters, 41(15), 5620-5628. \doi{10.1002/2014GL061146} #'@references Van Schaeybroeck, B., & Vannitsem, S. (2011). Post-processing #'through linear regression. Nonlinear Processes in Geophysics, 18(2), -#'147. doi:10.5194/npg-18-147-2011 +#'147. \doi{10.5194/npg-18-147-2011} #'@references Van Schaeybroeck, B., & Vannitsem, S. (2015). Ensemble #'post-processing using member-by-member approaches: theoretical aspects. #'Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818. -#'doi:10.1002/qj.2397 +#'\doi{10.1002/qj.2397} #' #'@seealso \code{\link{CST_Load}} #' @@ -286,14 +286,14 @@ CST_Calibration <- function(exp, obs, exp_cor = NULL, cal.method = "mse_min", #'@references Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., #'Hermanson, L., & Robinson, N. (2014). Do seasonal-to-decadal climate #'predictions underestimate the predictability of the read world? Geophysical -#'Research Letters, 41(15), 5620-5628. doi: 10.1002/2014GL061146 +#'Research Letters, 41(15), 5620-5628. \doi{10.1002/2014GL061146} #'@references Van Schaeybroeck, B., & Vannitsem, S. (2011). Post-processing #'through linear regression. Nonlinear Processes in Geophysics, 18(2), -#'147. doi:10.5194/npg-18-147-2011 +#'147. \doi{10.5194/npg-18-147-2011} #'@references Van Schaeybroeck, B., & Vannitsem, S. (2015). Ensemble #'post-processing using member-by-member approaches: theoretical aspects. #'Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818. -#'doi:10.1002/qj.2397 +#'\doi{10.1002/qj.2397} #' #'@seealso \code{\link{CST_Load}} #' diff --git a/R/CST_DynBiasCorrection.R b/R/CST_DynBiasCorrection.R index 3f715f9e1bd151ff5deaad0e576ff13c8ab40017..ffbba9b846f13ce20bf80fc5d2fc09aab1b5f342 100644 --- a/R/CST_DynBiasCorrection.R +++ b/R/CST_DynBiasCorrection.R @@ -20,7 +20,7 @@ #'@references Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., #'and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large #'scale atmospheric predictability.Nature Communications, 10(1), 1316. -#'DOI = \doi{10.1038/s41467-019-09305-8} " +#'\doi{10.1038/s41467-019-09305-8}" #'@references Faranda, D., Gabriele Messori and Pascal Yiou. (2017). #' Dynamical proxies of North Atlantic predictability and extremes. #' Scientific Reports, 7-41278, 2017. @@ -107,7 +107,7 @@ CST_DynBiasCorrection<- function(exp, obs, method = 'QUANT', wetday=FALSE, #'@references Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., #'and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large #'scale atmospheric predictability.Nature Communications, 10(1), 1316. -#'DOI = https://doi.org/10.1038/s41467-019-09305-8 " +#'\doi{10.1038/s41467-019-09305-8}" #'@references Faranda, D., Gabriele Messori and Pascal Yiou. (2017). #' Dynamical proxies of North Atlantic predictability and extremes. #' Scientific Reports, 7-41278, 2017. diff --git a/R/CST_MultiMetric.R b/R/CST_MultiMetric.R index 2a7970e21a3726410c577667013309cbe230b320..7f847a4298107c211bec991a1065bf86ac0d239c 100644 --- a/R/CST_MultiMetric.R +++ b/R/CST_MultiMetric.R @@ -35,7 +35,7 @@ #'\code{\link[s2dv]{RMSSS}} and \code{\link{CST_Load}} #'@references Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill #'Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, -#'29-31.\url{https://link.springer.com/article/10.1007/s00382-018-4404-z} +#'29-31. \doi{10.1007/s00382-018-4404-z} #' #'@importFrom s2dv MeanDims Reorder Corr RMS RMSSS InsertDim #'@import abind @@ -105,7 +105,7 @@ CST_MultiMetric <- function(exp, obs, metric = "correlation", multimodel = TRUE, #'\code{\link[s2dv]{RMSSS}} and \code{\link{CST_Load}} #'@references Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill #'Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, -#'29-31.\url{https://link.springer.com/article/10.1007/s00382-018-4404-z} +#'29-31. \doi{10.1007/s00382-018-4404-z} #' #'@importFrom s2dv MeanDims Reorder Corr RMS RMSSS InsertDim #'@import abind diff --git a/R/CST_ProxiesAttractor.R b/R/CST_ProxiesAttractor.R index e490efca1b38fd69811acaa79dbbc9e1705c89d5..e99677176490c53be8b881e190f1168ab532f39e 100644 --- a/R/CST_ProxiesAttractor.R +++ b/R/CST_ProxiesAttractor.R @@ -16,7 +16,7 @@ #'@references Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., #'and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large #'scale atmospheric predictability. Nature Communications, 10(1), 1316. -#'DOI = \url{https://doi.org/10.1038/s41467-019-09305-8} " +#'\doi{10.1038/s41467-019-09305-8}" #'@references Faranda, D., Gabriele Messori and Pascal Yiou. (2017). #'Dynamical proxies of North Atlantic predictability and extremes. #'Scientific Reports, 7-41278, 2017. @@ -72,7 +72,7 @@ CST_ProxiesAttractor <- function(data, quanti, ncores = NULL) { #'@references Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., and #'Yiou, P. (2019). The hammam effect or how a warm ocean enhances large scale #'atmospheric predictability. Nature Communications, 10(1), 1316. -#'DOI = \url{https://doi.org/10.1038/s41467-019-09305-8} " +#'\doi{10.1038/s41467-019-09305-8}" #'@references Faranda, D., Gabriele Messori and Pascal Yiou. (2017). #' Dynamical proxies of North Atlantic predictability and extremes. #' Scientific Reports, 7-41278, 2017. diff --git a/R/CST_RFWeights.R b/R/CST_RFWeights.R index c14aafa87b1eaa8ad1c8e2a6184cf5b4748d907a..3cc7998d1e9efb2460ca5d79f5d6042a976d6d6b 100644 --- a/R/CST_RFWeights.R +++ b/R/CST_RFWeights.R @@ -8,7 +8,7 @@ #'Stochastic downscaling of precipitation in complex orography: #'A simple method to reproduce a realistic fine-scale climatology. #'Natural Hazards and Earth System Sciences, 18(11), -#'2825-2840. \url{http://doi.org/10.5194/nhess-18-2825-2018} . +#'2825-2840. \doi{10.5194/nhess-18-2825-2018}. #'@param climfile Filename of a fine-scale precipitation climatology. The file #' is expected to be in NetCDF format and should contain at least one #' precipitation field. If several fields at different times are provided, @@ -17,7 +17,7 @@ #' regional climate model (see e.g. Terzago et al. 2018), a local #' high-resolution gridded climatology from observations, or a reconstruction #' such as those which can be downloaded from the WORLDCLIM -#' (\url{http://www.worldclim.org}) or CHELSA (\url{http://chelsa-climate.org}) +#' (\url{https://www.worldclim.org}) or CHELSA (\url{https://chelsa-climate.org}) #' websites. The latter data will need to be converted to NetCDF format before #' being used (see for example the GDAL tools (\url{https://www.gdal.org}). It #' could also be an 's2dv_cube' object. @@ -141,7 +141,7 @@ CST_RFWeights <- function(climfile, nf, lon, lat, varname = NULL, fsmooth = TRUE #'Stochastic downscaling of precipitation in complex orography: #'A simple method to reproduce a realistic fine-scale climatology. #'Natural Hazards and Earth System Sciences, 18(11), -#'2825-2840. \url{http://doi.org/10.5194/nhess-18-2825-2018}. +#'2825-2840. \doi{10.5194/nhess-18-2825-2018}. #'@param zclim A multi-dimensional array with named dimension containing at #' least one precipiation field with spatial dimensions. #'@param lonin A vector indicating the longitudinal coordinates corresponding to diff --git a/R/CST_RainFARM.R b/R/CST_RainFARM.R index 282298c33b63b8b4951ad51f8184f05286072fa7..d01d9a1bc9584723737f3412b27125c8e3573a70 100644 --- a/R/CST_RainFARM.R +++ b/R/CST_RainFARM.R @@ -9,7 +9,7 @@ #'Adapted for climate downscaling and including orographic correction #'as described in Terzago et al. 2018. #'@references Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. -#'\url{http://doi.org/10.5194/nhess-18-2825-2018} ; +#'\doi{10.5194/nhess-18-2825-2018}; #'D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. #'(2006), JHM 7, 724. #'@param data An object of the class 's2dv_cube' as returned by `CST_Load`, @@ -22,16 +22,17 @@ #' expected to be even and the same. If not the function will perform a #' subsetting to ensure this condition. #'@param weights Matrix with climatological weights which can be obtained using -#' the \code{CST_RFWeights} function. If \code{weights=1.} (default) no weights -#' are used. The names of these dimensions must be at least 'lon' and 'lat'. +#' the \code{CST_RFWeights} function. If \code{weights = 1.} (default) no +#' weights are used. The names of these dimensions must be at least 'lon' and +#' 'lat'. #'@param nf Refinement factor for downscaling (the output resolution is #' increased by this factor). -#'@param slope Prescribed spectral slope. The default is \code{slope=0.} +#'@param slope Prescribed spectral slope. The default is \code{slope = 0.} #' meaning that the slope is determined automatically over the dimensions #' specified by \code{time_dim}. A 1D array with named dimension can be #' provided (see details and examples). -#'@param kmin First wavenumber for spectral slope (default: \code{kmin=1}). -#'@param nens Number of ensemble members to produce (default: \code{nens=1}). +#'@param kmin First wavenumber for spectral slope (default: \code{kmin = 1}). +#'@param nens Number of ensemble members to produce (default: \code{nens = 1}). #'@param fglob Logical to conserve global precipitation over the domain #' (default: FALSE). #'@param fsmooth Logical to conserve precipitation with a smoothing kernel @@ -47,20 +48,20 @@ #' ensemble dimension, needed for saving data through function CST_SaveData #' (default: FALSE) with the following behaviour if set to TRUE: #' \enumerate{ -#' \item{if \code{nens==1}: the dimension is dropped;} -#' \item{if \code{nens>1} and a "member" dimension exists: the "realization" +#' \item{if \code{nens == 1}: the dimension is dropped;} +#' \item{if \code{nens > 1} and a "member" dimension exists: the "realization" #' and "member" dimensions are compacted (multiplied) and the resulting #' dimension is named "member";} -#' \item{if \code{nens>1} and a "member" dimension does not exist: the +#' \item{if \code{nens > 1} and a "member" dimension does not exist: the #' "realization" dimension is renamed to "member".} #' } #'@param nprocs The number of parallel processes to spawn for the use for #' parallel computation in multiple cores. (default: 1) #' #'@return CST_RainFARM() returns a downscaled CSTools object (i.e., of the -#'class 's2dv_cube'). If \code{nens>1} an additional dimension named +#'class 's2dv_cube'). If \code{nens > 1} an additional dimension named #'"realization" is added to the \code{$data} array after the "member" dimension -#'(unless \code{drop_realization_dim=TRUE} is specified). The ordering of the +#'(unless \code{drop_realization_dim = TRUE} is specified). The ordering of the #'remaining dimensions in the \code{$data} element of the input object is #'maintained. #'@details Wether parameter 'slope' and 'weights' presents seasonality @@ -142,8 +143,9 @@ CST_RainFARM <- function(data, weights = 1., slope = 0, nf, kmin = 1, #'over which to average automatically determined spectral slopes. #'Adapted for climate downscaling and including orographic correction. #'References: -#'Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018, -#'D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. (2006), JHM 7, 724. +#'Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. \doi{10.5194/nhess-18-2825-2018}, +#'D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. +#'(2006), JHM 7, 724. #'@param data Precipitation 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) @@ -154,17 +156,17 @@ CST_RainFARM <- function(data, weights = 1., slope = 0, nf, kmin = 1, #'@param lon Vector or array of longitudes. #'@param lat Vector or array of latitudes. #'@param weights Multi-dimensional array with climatological weights which can -#' be obtained using the \code{CST_RFWeights} function. If \code{weights=1.} +#' be obtained using the \code{CST_RFWeights} function. If \code{weights = 1.} #' (default) no weights are used. The names of these dimensions must be at #' least the same longitudinal and latitudinal dimension names as data. #'@param nf Refinement factor for downscaling (the output resolution is #' increased by this factor). -#'@param slope Prescribed spectral slope. The default is \code{slope=0.} +#'@param slope Prescribed spectral slope. The default is \code{slope = 0.} #' meaning that the slope is determined automatically over the dimensions #' specified by \code{time_dim}. A 1D array with named dimension can be #' provided (see details and examples). -#'@param kmin First wavenumber for spectral slope (default: \code{kmin=1}). -#'@param nens Number of ensemble members to produce (default: \code{nens=1}). +#'@param kmin First wavenumber for spectral slope (default: \code{kmin = 1}). +#'@param nens Number of ensemble members to produce (default: \code{nens = 1}). #'@param fglob Logical to conseve global precipitation over the domain #' (default: FALSE). #'@param fsmooth Logical to conserve precipitation with a smoothing kernel @@ -182,20 +184,20 @@ CST_RainFARM <- function(data, weights = 1., slope = 0, nf, kmin = 1, #' ensemble dimension (default: FALSE) with the following behaviour if set to #' TRUE: #' \enumerate{ -#' \item{if \code{nens==1}: the dimension is dropped;} -#' \item{if \code{nens>1} and a "member" dimension exists: the "realization" +#' \item{if \code{nens == 1}: the dimension is dropped;} +#' \item{if \code{nens > 1} and a "member" dimension exists: the "realization" #' and "member" dimensions are compacted (multiplied) and the resulting #' dimension is named "member";} -#' \item{if \code{nens>1} and a "member" dimension does not exist: the +#' \item{if \code{nens > 1} and a "member" dimension does not exist: the #' "realization" dimension is renamed to "member".} #' } #'@param nprocs The number of parallel processes to spawn for the use for #' parallel computation in multiple cores. (default: 1) #'@return RainFARM() Returns a list containing the fine-scale longitudes, #' latitudes and the sequence of \code{nens} downscaled fields. If -#' \code{nens>1} an additional dimension named "realization" is added to the +#' \code{nens > 1} an additional dimension named "realization" is added to the #' output array after the "member" dimension (if it exists and unless -#' \code{drop_realization_dim=TRUE} is specified). The ordering of the +#' \code{drop_realization_dim = TRUE} is specified). The ordering of the #' remaining dimensions in the \code{exp} element of the input object is #' maintained. #'@details Wether parameter 'slope' and 'weights' presents seasonality @@ -384,14 +386,14 @@ RainFARM <- function(data, lon, lat, nf, weights = 1., nens = 1, slope = 0, #'Atomic RainFARM #'@param pr Precipitation array to downscale with dimensions (lon, lat, time). #'@param weights Matrix with climatological weights which can be obtained using -#' the \code{CST_RFWeights} function (default: \code{weights=1.} i.e. no +#' the \code{CST_RFWeights} function (default: \code{weights = 1.} i.e. no #' weights). -#'@param slope Prescribed spectral slope (default: \code{slope=0.} +#'@param slope Prescribed spectral slope (default: \code{slope = 0.} #'@param nf Refinement factor for downscaling (the output resolution is #' increased by this factor). Meaning that the slope is determined #' automatically over the dimensions specified by \code{time_dim}. -#'@param kmin First wavenumber for spectral slope (default: \code{kmin=1}). -#'@param nens Number of ensemble members to produce (default: \code{nens=1}). +#'@param kmin First wavenumber for spectral slope (default: \code{kmin = 1}). +#'@param nens Number of ensemble members to produce (default: \code{nens = 1}). #'@param fglob Logical to conseve global precipitation over the domain #' (default: FALSE). #'@param fsmooth Logical to conserve precipitation with a smoothing kernel diff --git a/R/CST_WeatherRegimes.R b/R/CST_WeatherRegimes.R index 7268e8ec51ef879962954f3c2f8a848875bd9a0f..56783aebb44f8aaac9b282c403b7a52441e1af12 100644 --- a/R/CST_WeatherRegimes.R +++ b/R/CST_WeatherRegimes.R @@ -12,7 +12,8 @@ #' #'@references Cortesi, N., V., Torralba, N., González-Reviriego, A., Soret, and #'F.J., Doblas-Reyes (2019). Characterization of European wind speed variability -#'using weather regimes. Climate Dynamics,53, 4961–4976, doi:10.1007/s00382-019-04839-5. +#'using weather regimes. Climate Dynamics,53, 4961–4976, +#'\doi{10.1007/s00382-019-04839-5}. #'@references Torralba, V. (2019) Seasonal climate prediction for the wind #'energy sector: methods and tools for the development of a climate service. #'Thesis. Available online: \url{https://eprints.ucm.es/56841/}. @@ -115,7 +116,8 @@ CST_WeatherRegimes <- function(data, ncenters = NULL, #' #'@references Cortesi, N., V., Torralba, N., González-Reviriego, A., Soret, and #'F.J., Doblas-Reyes (2019). Characterization of European wind speed variability -#'using weather regimes. Climate Dynamics,53, 4961–4976, doi:10.1007/s00382-019-04839-5. +#'using weather regimes. Climate Dynamics,53, 4961–4976, +#'\doi{10.1007/s00382-019-04839-5}. #'@references Torralba, V. (2019) Seasonal climate prediction for the wind #'energy sector: methods and tools for the development of a climate service. #'Thesis. Available online: \url{https://eprints.ucm.es/56841/} diff --git a/R/Predictability.R b/R/Predictability.R index 2416067431b72758c520c5598ff5ca01438dcd62..680666df92b5c779e79c7488f5d6906672f88eb3 100644 --- a/R/Predictability.R +++ b/R/Predictability.R @@ -16,7 +16,7 @@ #'@references Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., #'and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large #'scale atmospheric predictability.Nature Communications, 10(1), 1316. -#'DOI = \url{https://doi.org/10.1038/s41467-019-09305-8} " +#'\doi{10.1038/s41467-019-09305-8}" #'@references Faranda, D., Gabriele Messori and Pascal Yiou. (2017). #'Dynamical proxies of North Atlantic predictability and extremes. #'Scientific Reports, 7-41278, 2017. diff --git a/man/BEI_EMWeighting.Rd b/man/BEI_EMWeighting.Rd index cd47ff0142b15116edadbfc98806743148608f05..72282ec1464b2c42c8173d6d10b110b3e43b7186 100644 --- a/man/BEI_EMWeighting.Rd +++ b/man/BEI_EMWeighting.Rd @@ -51,7 +51,7 @@ res <- BEI_EMWeighting(var_exp, aweights) \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} diff --git a/man/BEI_PDFBest.Rd b/man/BEI_PDFBest.Rd index 5edaf07b4c759e65f1dddb24056f0165d176a15d..0f8da8e9a9cfa2dd74afae1351f0a2c1d34d091e 100644 --- a/man/BEI_PDFBest.Rd +++ b/man/BEI_PDFBest.Rd @@ -63,7 +63,7 @@ methods developped are "ME" (a bias correction scheme based on the mean error or bias between observation and predictions to correct the predicted values), and "LMEV" (a bias correction scheme based on a linear model using ensemble variance of index as predictor). (see Sanchez-Garcia, E. et al -(2019), https://doi.org/10.5194/asr-16-165-2019 for more details).} +(2019), \doi{10.5194/asr-16-165-2019} for more details).} \item{time_dim_name}{A character string indicating the name of the temporal dimension, by default 'time'.} @@ -85,7 +85,7 @@ This function implements the computation to obtain the index Probability Density Functions (PDFs) (e.g. NAO index) obtained to combining the Index PDFs for two Seasonal Forecast Systems (SFSs), the Best Index estimation (see Sanchez-Garcia, E. et al (2019), -\url{https://doi.org/10.5194/asr-16-165-2019} for more details about the +\doi{10.5194/asr-16-165-2019} for more details about the methodology applied to estimate the Best Index). } \examples{ @@ -121,7 +121,7 @@ res <- BEI_PDFBest(index_obs, index_hind1, index_hind2, index_fcst1, \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} diff --git a/man/BEI_ProbsWeighting.Rd b/man/BEI_ProbsWeighting.Rd index 17c1d592d7968281396375fdafc056983c84931d..d14321b897cd4c661f87384d90b4865b938ec8cc 100644 --- a/man/BEI_ProbsWeighting.Rd +++ b/man/BEI_ProbsWeighting.Rd @@ -66,7 +66,7 @@ res <- BEI_ProbsWeighting(var_exp, aweights, terciles) \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} diff --git a/man/BEI_TercilesWeighting.Rd b/man/BEI_TercilesWeighting.Rd index ab88af1845d9533e8250e338ef84487312e569d8..31d5f7319303eeb21d2bf4fb3f38f665d6835a44 100644 --- a/man/BEI_TercilesWeighting.Rd +++ b/man/BEI_TercilesWeighting.Rd @@ -56,7 +56,7 @@ res <- BEI_TercilesWeighting(var_exp, aweights) \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, https://doi.org/10.5194/asr-16-165-2019 +Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} diff --git a/man/BEI_Weights.Rd b/man/BEI_Weights.Rd index 9f54ac6c3e1b7c99dc18fe24b0b14a2bdb523958..fdaacca762105bccc8abb16dfc86559e7d54470b 100644 --- a/man/BEI_Weights.Rd +++ b/man/BEI_Weights.Rd @@ -47,7 +47,7 @@ dim(res) \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} diff --git a/man/BiasCorrection.Rd b/man/BiasCorrection.Rd index 233639afc593a7fcbadde0986adf48389585beec..fa087478548e9b9f6bff5ba7394f8bd00b0ca472 100644 --- a/man/BiasCorrection.Rd +++ b/man/BiasCorrection.Rd @@ -69,7 +69,7 @@ a <- BiasCorrection(exp = mod1, obs = obs1) Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and -Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, +Climatology, 56, 1231-1247, \doi{10.1175/JAMC-D-16-0204.1}. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS) } \author{ diff --git a/man/CST_BEI_Weighting.Rd b/man/CST_BEI_Weighting.Rd index 749d2f612de72d29fefd01b05e1e39d8b91eb2e5..fa9cf10bef068829f4c79162ae12c6a81fcc01ff 100644 --- a/man/CST_BEI_Weighting.Rd +++ b/man/CST_BEI_Weighting.Rd @@ -77,7 +77,7 @@ res_CST <- CST_BEI_Weighting(var_exp, aweights) \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} diff --git a/man/CST_BiasCorrection.Rd b/man/CST_BiasCorrection.Rd index be2b2aed73d48af400ef2c175c02d84ac2278cb1..4de9257784afa9affd1f9ca9f45b5f2425fe421b 100644 --- a/man/CST_BiasCorrection.Rd +++ b/man/CST_BiasCorrection.Rd @@ -79,7 +79,7 @@ a <- CST_BiasCorrection(exp = exp, obs = obs) Torralba, V., F.J. Doblas-Reyes, D. MacLeod, I. Christel and M. Davis (2017). Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and -Climatology, 56, 1231-1247, doi:10.1175/JAMC-D-16-0204.1. (CLIM4ENERGY, +Climatology, 56, 1231-1247, \doi{10.1175/JAMC-D-16-0204.1}. (CLIM4ENERGY, EUPORIAS, NEWA, RESILIENCE, SPECS) } \author{ diff --git a/man/CST_Calibration.Rd b/man/CST_Calibration.Rd index 07226a1e3fbe56520e06f3c0982bdc677bde9023..c8d1320f8e674f6c5d89ef9353c7a487010e94d8 100644 --- a/man/CST_Calibration.Rd +++ b/man/CST_Calibration.Rd @@ -164,21 +164,21 @@ a <- CST_Calibration(exp = exp, obs = obs, exp_cor = exp_cor, cal.method = "evmo \references{ Doblas-Reyes F.J, Hagedorn R, Palmer T.N. The rationale behind the success of multi-model ensembles in seasonal forecasting-II calibration and -combination. Tellus A. 2005;57:234-252. doi:10.1111/j.1600-0870.2005.00104.x +combination. Tellus A. 2005;57:234-252. \doi{10.1111/j.1600-0870.2005.00104.x} Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., Hermanson, L., & Robinson, N. (2014). Do seasonal-to-decadal climate predictions underestimate the predictability of the read world? Geophysical -Research Letters, 41(15), 5620-5628. doi: 10.1002/2014GL061146 +Research Letters, 41(15), 5620-5628. \doi{10.1002/2014GL061146} Van Schaeybroeck, B., & Vannitsem, S. (2011). Post-processing through linear regression. Nonlinear Processes in Geophysics, 18(2), -147. doi:10.5194/npg-18-147-2011 +147. \doi{10.5194/npg-18-147-2011} Van Schaeybroeck, B., & Vannitsem, S. (2015). Ensemble post-processing using member-by-member approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818. -doi:10.1002/qj.2397 +\doi{10.1002/qj.2397} } \seealso{ \code{\link{CST_Load}} diff --git a/man/CST_DynBiasCorrection.Rd b/man/CST_DynBiasCorrection.Rd index 0f3a1ab28683708fdea1a6978c3d5fc529082b20..2197343a469d260183883f471b4daa740743b9b3 100644 --- a/man/CST_DynBiasCorrection.Rd +++ b/man/CST_DynBiasCorrection.Rd @@ -81,7 +81,7 @@ dynbias2 <- CST_DynBiasCorrection(exp = expL, obs = obsL, proxy= "dim", Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large scale atmospheric predictability.Nature Communications, 10(1), 1316. -DOI = \doi{10.1038/s41467-019-09305-8} " +\doi{10.1038/s41467-019-09305-8}" Faranda, D., Gabriele Messori and Pascal Yiou. (2017). Dynamical proxies of North Atlantic predictability and extremes. diff --git a/man/CST_MultiMetric.Rd b/man/CST_MultiMetric.Rd index 3489fab391c36503aeafc003b0078988425836f0..7b4bc000e0b90af84ee636d6507ed78cd18115e6 100644 --- a/man/CST_MultiMetric.Rd +++ b/man/CST_MultiMetric.Rd @@ -71,7 +71,7 @@ a <- CST_MultiMetric(exp = exp, obs = obs) \references{ Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, -29-31.\url{https://link.springer.com/article/10.1007/s00382-018-4404-z} +29-31. \doi{10.1007/s00382-018-4404-z} } \seealso{ \code{\link[s2dv]{Corr}}, \code{\link[s2dv]{RMS}}, diff --git a/man/CST_ProxiesAttractor.Rd b/man/CST_ProxiesAttractor.Rd index 45f4753a7f4504a8d6b6d05f0780baf6177dacf5..58f949af388ee6c572421514b885d1e487e43f46 100644 --- a/man/CST_ProxiesAttractor.Rd +++ b/man/CST_ProxiesAttractor.Rd @@ -42,7 +42,7 @@ attractor <- CST_ProxiesAttractor(data = data, quanti = 0.6) Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large scale atmospheric predictability. Nature Communications, 10(1), 1316. -DOI = \url{https://doi.org/10.1038/s41467-019-09305-8} " +\doi{10.1038/s41467-019-09305-8}" Faranda, D., Gabriele Messori and Pascal Yiou. (2017). Dynamical proxies of North Atlantic predictability and extremes. diff --git a/man/CST_RFWeights.Rd b/man/CST_RFWeights.Rd index 54926f5f06fe437e3711bd9703761967f9d70df1..887e81dc0e7110676896e9e55a91d341a05a3918 100644 --- a/man/CST_RFWeights.Rd +++ b/man/CST_RFWeights.Rd @@ -25,7 +25,7 @@ be for example a fine-scale precipitation climatology from a high-resolution regional climate model (see e.g. Terzago et al. 2018), a local high-resolution gridded climatology from observations, or a reconstruction such as those which can be downloaded from the WORLDCLIM -(\url{http://www.worldclim.org}) or CHELSA (\url{http://chelsa-climate.org}) +(\url{https://www.worldclim.org}) or CHELSA (\url{https://chelsa-climate.org}) websites. The latter data will need to be converted to NetCDF format before being used (see for example the GDAL tools (\url{https://www.gdal.org}). It could also be an 's2dv_cube' object.} @@ -79,7 +79,7 @@ Terzago, S., Palazzi, E., & von Hardenberg, J. (2018). Stochastic downscaling of precipitation in complex orography: A simple method to reproduce a realistic fine-scale climatology. Natural Hazards and Earth System Sciences, 18(11), -2825-2840. \url{http://doi.org/10.5194/nhess-18-2825-2018} . +2825-2840. \doi{10.5194/nhess-18-2825-2018}. } \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} diff --git a/man/CST_RainFARM.Rd b/man/CST_RainFARM.Rd index 942e584ab05308602054acef61ca71a96a60e837..71cb54a155ef6270e75fd910f358e96deed24299 100644 --- a/man/CST_RainFARM.Rd +++ b/man/CST_RainFARM.Rd @@ -31,10 +31,11 @@ expected to be even and the same. If not the function will perform a subsetting to ensure this condition.} \item{weights}{Matrix with climatological weights which can be obtained using -the \code{CST_RFWeights} function. If \code{weights=1.} (default) no weights -are used. The names of these dimensions must be at least 'lon' and 'lat'.} +the \code{CST_RFWeights} function. If \code{weights = 1.} (default) no +weights are used. The names of these dimensions must be at least 'lon' and +'lat'.} -\item{slope}{Prescribed spectral slope. The default is \code{slope=0.} +\item{slope}{Prescribed spectral slope. The default is \code{slope = 0.} meaning that the slope is determined automatically over the dimensions specified by \code{time_dim}. A 1D array with named dimension can be provided (see details and examples).} @@ -42,9 +43,9 @@ provided (see details and examples).} \item{nf}{Refinement factor for downscaling (the output resolution is increased by this factor).} -\item{kmin}{First wavenumber for spectral slope (default: \code{kmin=1}).} +\item{kmin}{First wavenumber for spectral slope (default: \code{kmin = 1}).} -\item{nens}{Number of ensemble members to produce (default: \code{nens=1}).} +\item{nens}{Number of ensemble members to produce (default: \code{nens = 1}).} \item{fglob}{Logical to conserve global precipitation over the domain (default: FALSE).} @@ -68,19 +69,19 @@ the first one with more than one element is chosen.} ensemble dimension, needed for saving data through function CST_SaveData (default: FALSE) with the following behaviour if set to TRUE: \enumerate{ - \item{if \code{nens==1}: the dimension is dropped;} - \item{if \code{nens>1} and a "member" dimension exists: the "realization" + \item{if \code{nens == 1}: the dimension is dropped;} + \item{if \code{nens > 1} and a "member" dimension exists: the "realization" and "member" dimensions are compacted (multiplied) and the resulting dimension is named "member";} - \item{if \code{nens>1} and a "member" dimension does not exist: the + \item{if \code{nens > 1} and a "member" dimension does not exist: the "realization" dimension is renamed to "member".} }} } \value{ CST_RainFARM() returns a downscaled CSTools object (i.e., of the -class 's2dv_cube'). If \code{nens>1} an additional dimension named +class 's2dv_cube'). If \code{nens > 1} an additional dimension named "realization" is added to the \code{$data} array after the "member" dimension -(unless \code{drop_realization_dim=TRUE} is specified). The ordering of the +(unless \code{drop_realization_dim = TRUE} is specified). The ordering of the remaining dimensions in the \code{$data} element of the input object is maintained. } @@ -113,7 +114,7 @@ res <- CST_RainFARM(data, nf = nf, weights = ww, nens = 3, time_dim = 'ftime') } \references{ Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. -\url{http://doi.org/10.5194/nhess-18-2825-2018} ; +\doi{10.5194/nhess-18-2825-2018}; D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. (2006), JHM 7, 724. } diff --git a/man/CST_WeatherRegimes.Rd b/man/CST_WeatherRegimes.Rd index 220b0aca368710bcc31069b8f01af97c05e0a6a1..bd4bad00ffee3b5bafdd8e7053ce55e67f82fa6b 100644 --- a/man/CST_WeatherRegimes.Rd +++ b/man/CST_WeatherRegimes.Rd @@ -83,7 +83,8 @@ res2 <- CST_WeatherRegimes(data = obs, EOFs = TRUE, ncenters = 3) \references{ Cortesi, N., V., Torralba, N., González-Reviriego, A., Soret, and F.J., Doblas-Reyes (2019). Characterization of European wind speed variability -using weather regimes. Climate Dynamics,53, 4961–4976, doi:10.1007/s00382-019-04839-5. +using weather regimes. Climate Dynamics,53, 4961–4976, +\doi{10.1007/s00382-019-04839-5}. Torralba, V. (2019) Seasonal climate prediction for the wind energy sector: methods and tools for the development of a climate service. diff --git a/man/Calibration.Rd b/man/Calibration.Rd index 2248834bfe5f12a8fe904506172f6bdc5c99cd4e..b907326aafb8c44a9e773096afb6c5434997d0d4 100644 --- a/man/Calibration.Rd +++ b/man/Calibration.Rd @@ -139,16 +139,16 @@ combination. Tellus A. 2005;57:234-252. doi:10.1111/j.1600-0870.2005.00104.x Eade, R., Smith, D., Scaife, A., Wallace, E., Dunstone, N., Hermanson, L., & Robinson, N. (2014). Do seasonal-to-decadal climate predictions underestimate the predictability of the read world? Geophysical -Research Letters, 41(15), 5620-5628. doi: 10.1002/2014GL061146 +Research Letters, 41(15), 5620-5628. \doi{10.1002/2014GL061146} Van Schaeybroeck, B., & Vannitsem, S. (2011). Post-processing through linear regression. Nonlinear Processes in Geophysics, 18(2), -147. doi:10.5194/npg-18-147-2011 +147. \doi{10.5194/npg-18-147-2011} Van Schaeybroeck, B., & Vannitsem, S. (2015). Ensemble post-processing using member-by-member approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society, 141(688), 807-818. -doi:10.1002/qj.2397 +\doi{10.1002/qj.2397} } \seealso{ \code{\link{CST_Load}} diff --git a/man/DynBiasCorrection.Rd b/man/DynBiasCorrection.Rd index e82ca8c7215b8d60ff546a367d048ab378f4ee35..b329ac920d4f86c2e01da4fd16f639ef8e942df5 100644 --- a/man/DynBiasCorrection.Rd +++ b/man/DynBiasCorrection.Rd @@ -66,7 +66,7 @@ dynbias <- DynBiasCorrection(exp = expL, obs = obsL, method='QUANT', Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large scale atmospheric predictability.Nature Communications, 10(1), 1316. -DOI = https://doi.org/10.1038/s41467-019-09305-8 " +\doi{10.1038/s41467-019-09305-8}" Faranda, D., Gabriele Messori and Pascal Yiou. (2017). Dynamical proxies of North Atlantic predictability and extremes. diff --git a/man/MultiMetric.Rd b/man/MultiMetric.Rd index d99b073f4e3fa47ad77e176621342999a4c50e15..1fbba692f6140a5e0285933d864187d92f844293 100644 --- a/man/MultiMetric.Rd +++ b/man/MultiMetric.Rd @@ -61,7 +61,7 @@ res <- MultiMetric(exp = exp, obs = obs) \references{ Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, -29-31.\url{https://link.springer.com/article/10.1007/s00382-018-4404-z} +29-31. \doi{10.1007/s00382-018-4404-z} } \seealso{ \code{\link[s2dv]{Corr}}, \code{\link[s2dv]{RMS}}, diff --git a/man/PDFIndexHind.Rd b/man/PDFIndexHind.Rd index 74c79911ceaa021120d12ff7b4fb19cfded13199..79a6424d02a70b631c86dcae2ca35af48aa6e94b 100644 --- a/man/PDFIndexHind.Rd +++ b/man/PDFIndexHind.Rd @@ -67,7 +67,7 @@ res <- PDFIndexHind(index_hind, index_obs) \references{ Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO, Sanchez-Garcia, E. et al., -Adv. Sci. Res., 16, 165174, 2019, \url{https://doi.org/10.5194/asr-16-165-2019} +Adv. Sci. Res., 16, 165174, 2019, \doi{10.5194/asr-16-165-2019} } \author{ Eroteida Sanchez-Garcia - AEMET, \email{esanchezg@aemet.es} diff --git a/man/Predictability.Rd b/man/Predictability.Rd index 3cc07a9af96f099cb5ead2d59d06e8891a83a33f..04f7204eea53f35f0dad6e1ffcb75a0b86232ffe 100644 --- a/man/Predictability.Rd +++ b/man/Predictability.Rd @@ -60,7 +60,7 @@ predyn <- Predictability(dim = attractor$dim, theta = attractor$theta) Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large scale atmospheric predictability.Nature Communications, 10(1), 1316. -DOI = \url{https://doi.org/10.1038/s41467-019-09305-8} " +\doi{10.1038/s41467-019-09305-8}" Faranda, D., Gabriele Messori and Pascal Yiou. (2017). Dynamical proxies of North Atlantic predictability and extremes. diff --git a/man/ProxiesAttractor.Rd b/man/ProxiesAttractor.Rd index 998a1113ed9336ed6d3ba2d543ad83b915b58d56..ffa1b36b18654fd83119cbfec3079abd6f0dde7d 100644 --- a/man/ProxiesAttractor.Rd +++ b/man/ProxiesAttractor.Rd @@ -42,7 +42,7 @@ plot(time, Attractor$dim, xlab = 'time', ylab = 'd', Faranda, D., Alvarez-Castro, M.C., Messori, G., Rodriguez, D., and Yiou, P. (2019). The hammam effect or how a warm ocean enhances large scale atmospheric predictability. Nature Communications, 10(1), 1316. -DOI = \url{https://doi.org/10.1038/s41467-019-09305-8} " +\doi{10.1038/s41467-019-09305-8}" Faranda, D., Gabriele Messori and Pascal Yiou. (2017). Dynamical proxies of North Atlantic predictability and extremes. diff --git a/man/RF_Weights.Rd b/man/RF_Weights.Rd index d82fbe111b7c114f8b03f35b5d85391364707a6c..836106369843db7d917a3ca4426444524c1fb7f7 100644 --- a/man/RF_Weights.Rd +++ b/man/RF_Weights.Rd @@ -66,7 +66,7 @@ Terzago, S., Palazzi, E., & von Hardenberg, J. (2018). Stochastic downscaling of precipitation in complex orography: A simple method to reproduce a realistic fine-scale climatology. Natural Hazards and Earth System Sciences, 18(11), -2825-2840. \url{http://doi.org/10.5194/nhess-18-2825-2018}. +2825-2840. \doi{10.5194/nhess-18-2825-2018}. } \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} diff --git a/man/RainFARM.Rd b/man/RainFARM.Rd index 333896dccba2d46e6261f6a0762cfc647033d359..6a6e2739f1fb0d781686880d90f2206e68bd2c1b 100644 --- a/man/RainFARM.Rd +++ b/man/RainFARM.Rd @@ -40,18 +40,18 @@ the function will perform a subsetting to ensure this condition.} increased by this factor).} \item{weights}{Multi-dimensional array with climatological weights which can -be obtained using the \code{CST_RFWeights} function. If \code{weights=1.} +be obtained using the \code{CST_RFWeights} function. If \code{weights = 1.} (default) no weights are used. The names of these dimensions must be at least the same longitudinal and latitudinal dimension names as data.} -\item{nens}{Number of ensemble members to produce (default: \code{nens=1}).} +\item{nens}{Number of ensemble members to produce (default: \code{nens = 1}).} -\item{slope}{Prescribed spectral slope. The default is \code{slope=0.} +\item{slope}{Prescribed spectral slope. The default is \code{slope = 0.} meaning that the slope is determined automatically over the dimensions specified by \code{time_dim}. A 1D array with named dimension can be provided (see details and examples).} -\item{kmin}{First wavenumber for spectral slope (default: \code{kmin=1}).} +\item{kmin}{First wavenumber for spectral slope (default: \code{kmin = 1}).} \item{fglob}{Logical to conseve global precipitation over the domain (default: FALSE).} @@ -77,11 +77,11 @@ with more than one element is chosen.} ensemble dimension (default: FALSE) with the following behaviour if set to TRUE: \enumerate{ - \item{if \code{nens==1}: the dimension is dropped;} - \item{if \code{nens>1} and a "member" dimension exists: the "realization" + \item{if \code{nens == 1}: the dimension is dropped;} + \item{if \code{nens > 1} and a "member" dimension exists: the "realization" and "member" dimensions are compacted (multiplied) and the resulting dimension is named "member";} - \item{if \code{nens>1} and a "member" dimension does not exist: the + \item{if \code{nens > 1} and a "member" dimension does not exist: the "realization" dimension is renamed to "member".} }} @@ -90,9 +90,9 @@ TRUE: \value{ RainFARM() Returns a list containing the fine-scale longitudes, latitudes and the sequence of \code{nens} downscaled fields. If - \code{nens>1} an additional dimension named "realization" is added to the + \code{nens > 1} an additional dimension named "realization" is added to the output array after the "member" dimension (if it exists and unless - \code{drop_realization_dim=TRUE} is specified). The ordering of the + \code{drop_realization_dim = TRUE} is specified). The ordering of the remaining dimensions in the \code{exp} element of the input object is maintained. } @@ -103,8 +103,9 @@ and one or more dimension (such as "ftime", "sdate" or "time") over which to average automatically determined spectral slopes. Adapted for climate downscaling and including orographic correction. References: -Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. http://doi.org/10.5194/nhess-18-2825-2018, -D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. (2006), JHM 7, 724. +Terzago, S. et al. (2018). NHESS 18(11), 2825-2840. \doi{10.5194/nhess-18-2825-2018}, +D'Onofrio et al. (2014), J of Hydrometeorology 15, 830-843; Rebora et. al. +(2006), JHM 7, 724. } \details{ Wether parameter 'slope' and 'weights' presents seasonality diff --git a/man/WeatherRegimes.Rd b/man/WeatherRegimes.Rd index fe52d152b85cb6cd18cba54382002b45fc5a1ad4..8164d705140c309a347bc41917eea696dc08105e 100644 --- a/man/WeatherRegimes.Rd +++ b/man/WeatherRegimes.Rd @@ -87,7 +87,8 @@ res <- WeatherRegime(data = data, lat = lat, \references{ Cortesi, N., V., Torralba, N., González-Reviriego, A., Soret, and F.J., Doblas-Reyes (2019). Characterization of European wind speed variability -using weather regimes. Climate Dynamics,53, 4961–4976, doi:10.1007/s00382-019-04839-5. +using weather regimes. Climate Dynamics,53, 4961–4976, +\doi{10.1007/s00382-019-04839-5}. Torralba, V. (2019) Seasonal climate prediction for the wind energy sector: methods and tools for the development of a climate service. diff --git a/vignettes/RainFARM_vignette.Rmd b/vignettes/RainFARM_vignette.Rmd index 070b38a18b7efcec3f721d293224dd4e4f577235..b2a49a5a4236fbd02e928a911a7b413f389ba735 100644 --- a/vignettes/RainFARM_vignette.Rmd +++ b/vignettes/RainFARM_vignette.Rmd @@ -118,7 +118,7 @@ RainFARM has downscaled the original field with a realistic fine-scale correlati The area of interest in our example presents a complex orography, but the basic RainFARM algorithm used does not consider topographic elevation in deciding how to distribute fine-scale precipitation. A long term climatology of the downscaled fields would have a resolution comparable to that of the original coarse fields and would not resemble the fine-scale structure of an observed climatology. If an external fine-scale climatology of precipitation is available, we can use the method discussed in Terzago et al. (2018) to change the distribution of precipitation by RainFARM for each timestep, so that the long-term average is close to this reference climatology in terms of precipitation distribution (while the total precipitation amount of the original fields to downscale is preserved). -Suitable climatology files could be for example a fine-scale precipitation climatology from a high-resolution regional climate model (see e.g. Terzago et al. 2018), a local high-resolution gridded climatology from observations, or a reconstruction such as those which can be downloaded from the WORLDCLIM (https://www.worldclim.org) or CHELSA (chelsa-climate.org) websites. The latter data will need to be converted to NetCDF format before being used (see for example the GDAL tools (https://gdal.org). +Suitable climatology files could be for example a fine-scale precipitation climatology from a high-resolution regional climate model (see e.g. Terzago et al. 2018), a local high-resolution gridded climatology from observations, or a reconstruction such as those which can be downloaded from the WORLDCLIM (https://www.worldclim.org) or CHELSA (https://chelsa-climate.org/) websites. The latter data will need to be converted to NetCDF format before being used (see for example the GDAL tools (https://gdal.org). We will assume that a copy of the WORLDCLIM precipitation climatology at 30 arcseconds (about 1km resolution) is available in the local file `medscope.nc`. From this file we can derive suitable weights to be used with RainFARM using the `CST_RFWeights` functions as follows: ```{r} ww <- CST_RFWeights("./worldclim.nc", nf = 20, lon = exp$coords$lon, lat = exp$coords$lat)