diff --git a/.Rbuildignore b/.Rbuildignore index b2d8e5fcebca62bff5e5380a881580283874cd54..fa596e707601da63df8c53cf4f087a70a953dbea 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -5,4 +5,4 @@ ./.nc$ .*^(?!data)\.RData$ .*\.gitlab-ci.yml$ -#^tests$ +^tests$ diff --git a/R/CST_MultiMetric.R b/R/CST_MultiMetric.R index d25ed5e49d888071e13755bcad69c93a6d5c2843..2390b490847bf9ed596fec25f15aa6b9cfe83df4 100644 --- a/R/CST_MultiMetric.R +++ b/R/CST_MultiMetric.R @@ -15,7 +15,7 @@ #'@return an object of class \code{s2dv_cube} containing the statistics of the selected metric in the element \code{$data} which is a list of arrays: for the metric requested and others for statistics about its signeificance. The arrays have two dataset dimensions equal to the 'dataset' dimension in the \code{exp$data} and \code{obs$data} inputs. If \code{multimodel} is TRUE, the first position in the first 'nexp' dimension correspons to the Multi-Model Mean. #'@seealso \code{\link[s2dv]{Corr}}, \code{\link[s2dv]{RMS}}, \code{\link[s2dv]{RMSSS}} and \code{\link{CST_Load}} #'@references -#'Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.\url{http://link.springer.com/10.1007/s00382-018-4404-z} +#'Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.\url{https://link.springer.com/10.1007/s00382-018-4404-z} #' #'@importFrom s2dv MeanDims Reorder Corr RMS RMSSS InsertDim #'@import abind @@ -75,7 +75,7 @@ CST_MultiMetric <- function(exp, obs, metric = "correlation", multimodel = TRUE, #'@return a list of arrays containing the statistics of the selected metric in the element \code{$data} which is a list of arrays: for the metric requested and others for statistics about its signeificance. The arrays have two dataset dimensions equal to the 'dataset' dimension in the \code{exp$data} and \code{obs$data} inputs. If \code{multimodel} is TRUE, the greatest position in the first dimension correspons to the Multi-Model Mean. #'@seealso \code{\link[s2dv]{Corr}}, \code{\link[s2dv]{RMS}}, \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{http://link.springer.com/10.1007/s00382-018-4404-z} +#'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/10.1007/s00382-018-4404-z} #' #'@importFrom s2dv MeanDims Reorder Corr RMS RMSSS InsertDim #'@import abind diff --git a/man/CST_MultiMetric.Rd b/man/CST_MultiMetric.Rd index dc2f756623acb51ff90e108d1bade669a6a98201..72ec383254b749d478689bb64449aa80e434c383 100644 --- a/man/CST_MultiMetric.Rd +++ b/man/CST_MultiMetric.Rd @@ -60,7 +60,7 @@ a <- CST_MultiMetric(exp, obs, metric = 'rmsss') } } \references{ -Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.\url{http://link.springer.com/10.1007/s00382-018-4404-z} +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/10.1007/s00382-018-4404-z} } \seealso{ \code{\link[s2dv]{Corr}}, \code{\link[s2dv]{RMS}}, \code{\link[s2dv]{RMSSS}} and \code{\link{CST_Load}} diff --git a/man/MultiMetric.Rd b/man/MultiMetric.Rd index f688f0a31050bb7bf3be5735454b3b1e41a263af..10a4c33f1a0852dc28ca79684610f8129c135cf9 100644 --- a/man/MultiMetric.Rd +++ b/man/MultiMetric.Rd @@ -39,7 +39,7 @@ This function calculates correlation (Anomaly Correlation Coefficient; ACC), roo res <- MultiMetric(lonlat_data$exp$data, lonlat_data$obs$data) } \references{ -Mishra, N., Prodhomme, C., & Guemas, V. (n.d.). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31.\url{http://link.springer.com/10.1007/s00382-018-4404-z} +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/10.1007/s00382-018-4404-z} } \seealso{ \code{\link[s2dv]{Corr}}, \code{\link[s2dv]{RMS}}, \code{\link[s2dv]{RMSSS}} and \code{\link{CST_Load}} diff --git a/vignettes/RainFARM_vignette.Rmd b/vignettes/RainFARM_vignette.Rmd index 2b0274e04dac002213d47b6eba33b2c60b717839..dbcb48a47bee31c92b80c4b879d55d989132843b 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 (http://www.worldclim.org) or CHELSA (http://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://www.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$lon, lat = exp$lat) diff --git a/vignettes/WeatherRegimes_vignette.Rmd b/vignettes/WeatherRegimes_vignette.Rmd index 62e4883db6b55406f5100f7d6cb4e9187859f3d2..d9272678f27f4cbfaaa131aaaddc479f32fa21d2 100644 --- a/vignettes/WeatherRegimes_vignette.Rmd +++ b/vignettes/WeatherRegimes_vignette.Rmd @@ -30,7 +30,7 @@ library(zeallot) The data employed in this example are described below. - Sea level pressure (psl): this has been selected as the circulation variable, however other variables such as geopotential at 500 hPa can be also used. - Region: Euro-Atlantic domain [85.5ºW-45ºE; 27-81ºN]. -- Datasets: seasonal predictions from ECMWF System 4 ([**Molteni et al. 2011**] (https://www.ecmwf.int/sites/default/files/elibrary/2011/11209-new-ecmwf-seasonal-forecast-system-system-4.pdf)) and ERA-Interim reanalysis ([**Dee et al. 2011**] (http://onlinelibrary.wiley.com/doi/10.1002/qj.828/pdf)) as a reference dataset. +- Datasets: seasonal predictions from ECMWF System 4 ([**Molteni et al. 2011**] (https://www.ecmwf.int/sites/default/files/elibrary/2011/11209-new-ecmwf-seasonal-forecast-system-system-4.pdf)) and ERA-Interim reanalysis ([**Dee et al. 2011**] (https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.828)) as a reference dataset. - Period: 1991-2010. Only 20 years have been selected for illustrative purposes, but the full hindcast period could be used for the analysis.