From b40e548f7cfdc3fd1082ac9ad7562d98feda7f64 Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Mon, 27 Mar 2023 11:58:45 +0200 Subject: [PATCH] Correct links of urls and dois in documentation --- R/BEI_PDFBest.R | 10 +++--- R/BEI_Weights.R | 2 +- R/CST_BEI_Weighting.R | 8 ++--- R/CST_BiasCorrection.R | 4 +-- R/CST_Calibration.R | 14 ++++----- R/CST_DynBiasCorrection.R | 4 +-- R/CST_MultiMetric.R | 4 +-- R/CST_ProxiesAttractor.R | 4 +-- R/CST_RFWeights.R | 6 ++-- R/CST_RainFARM.R | 54 +++++++++++++++++---------------- R/CST_WeatherRegimes.R | 6 ++-- R/Predictability.R | 2 +- man/BEI_EMWeighting.Rd | 2 +- man/BEI_PDFBest.Rd | 6 ++-- man/BEI_ProbsWeighting.Rd | 2 +- man/BEI_TercilesWeighting.Rd | 2 +- man/BEI_Weights.Rd | 2 +- man/BiasCorrection.Rd | 2 +- man/CST_BEI_Weighting.Rd | 2 +- man/CST_BiasCorrection.Rd | 2 +- man/CST_Calibration.Rd | 8 ++--- man/CST_DynBiasCorrection.Rd | 2 +- man/CST_MultiMetric.Rd | 2 +- man/CST_ProxiesAttractor.Rd | 2 +- man/CST_RFWeights.Rd | 4 +-- man/CST_RainFARM.Rd | 23 +++++++------- man/CST_WeatherRegimes.Rd | 3 +- man/Calibration.Rd | 6 ++-- man/DynBiasCorrection.Rd | 2 +- man/MultiMetric.Rd | 2 +- man/PDFIndexHind.Rd | 2 +- man/Predictability.Rd | 2 +- man/ProxiesAttractor.Rd | 2 +- man/RF_Weights.Rd | 2 +- man/RainFARM.Rd | 23 +++++++------- man/WeatherRegimes.Rd | 3 +- vignettes/RainFARM_vignette.Rmd | 2 +- 37 files changed, 118 insertions(+), 110 deletions(-) diff --git a/R/BEI_PDFBest.R b/R/BEI_PDFBest.R index 61313008..d2d98af8 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 f5cc7f59..e550af1e 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 885e1d84..adc268a2 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 ae5b61f9..ba3f1422 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 9b3c33fa..79f51320 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 3f715f9e..ffbba9b8 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 2a7970e2..7f847a42 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 e490efca..e9967717 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 c14aafa8..3cc7998d 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 282298c3..d01d9a1b 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 7268e8ec..56783aeb 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 24160674..680666df 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 cd47ff01..72282ec1 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 5edaf07b..0f8da8e9 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 17c1d592..d14321b8 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 ab88af18..31d5f731 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 9f54ac6c..fdaacca7 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 233639af..fa087478 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 749d2f61..fa9cf10b 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 be2b2aed..4de92577 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 07226a1e..c8d1320f 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 0f3a1ab2..2197343a 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 3489fab3..7b4bc000 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 45f4753a..58f949af 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 54926f5f..887e81dc 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 942e584a..71cb54a1 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 220b0aca..bd4bad00 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 2248834b..b907326a 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 e82ca8c7..b329ac92 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 d99b073f..1fbba692 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 74c79911..79a6424d 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 3cc07a9a..04f7204e 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 998a1113..ffa1b36b 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 d82fbe11..83610636 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 333896dc..6a6e2739 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 fe52d152..8164d705 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 070b38a1..b2a49a5a 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) -- GitLab