diff --git a/DESCRIPTION b/DESCRIPTION index 53fdebdea9459beaafec17f7034dce74e931ff77..1700491ba8fcc927dfd059484765ebb3c939211b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -57,7 +57,7 @@ Description: Exploits dynamical seasonal forecasts in order to provide Van Schaeybroeck et al. (2019) . Yiou et al. (2013) . Depends: - R (>= 3.4.0), + R (>= 3.5.0), maps, qmap, easyVerification diff --git a/R/CST_RFTemp.R b/R/CST_RFTemp.R index c0879c63960009549ddc3494484a040612089b38..1bf7ecdeedab5eed1078f395e120d17d47b31997 100644 --- a/R/CST_RFTemp.R +++ b/R/CST_RFTemp.R @@ -13,7 +13,7 @@ #'\url{https://www.medscope-project.eu/the-project/deliverables-reports/} #'and in H2020 ECOPOTENTIAL Deliverable No. 8.1: #'High resolution (1-10 km) climate, land use and ocean change scenarios available -#'here: \url{https://www.ecopotential-project.eu/images/ecopotential/documents/D8.1.pdf} +#'here: \url{https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5b6cd2324&appId=PPGMS} #'@param data An object of the class 's2dv_cube' as returned by `CST_Load`, #' containing the temperature fields to downscale. The data object is expected #' to have an element named \code{$data} with at least two spatial dimensions @@ -147,7 +147,7 @@ CST_RFTemp <- function(data, oro, xlim = NULL, ylim = NULL, lapse = 6.5, #'\ url{https://www.medscope-project.eu/the-project/deliverables-reports/} #'and in H2020 ECOPOTENTIAL Deliverable No. 8.1: #'High resolution (1-10 km) climate, land use and ocean change scenarios here: -#'\url{https://www.ecopotential-project.eu/images/ecopotential/documents/D8.1.pdf}. +#'\url{https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5b6cd2324&appId=PPGMS}. #'@param data Temperature 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). diff --git a/R/CST_RFWeights.R b/R/CST_RFWeights.R index 990dda67d18532eb0694d58738af2fda0c5460eb..e93931d7f96659b19ae6b02a3f055dd544382804 100644 --- a/R/CST_RFWeights.R +++ b/R/CST_RFWeights.R @@ -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{https://www.worldclim.org}) or CHELSA (\url{https://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://gdal.org/}). It #' could also be an 's2dv_cube' object. diff --git a/man/CST_RFTemp.Rd b/man/CST_RFTemp.Rd index 4f5d535419798c281fc5ab309358c737df46e863..ce2bd29e8a6e5c2fc612aaed545e91c63b11b15e 100644 --- a/man/CST_RFTemp.Rd +++ b/man/CST_RFTemp.Rd @@ -105,7 +105,7 @@ High-quality climate prediction data available to WP4 here: \url{https://www.medscope-project.eu/the-project/deliverables-reports/} and in H2020 ECOPOTENTIAL Deliverable No. 8.1: High resolution (1-10 km) climate, land use and ocean change scenarios available -here: \url{https://www.ecopotential-project.eu/images/ecopotential/documents/D8.1.pdf} +here: \url{https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5b6cd2324&appId=PPGMS} } \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} diff --git a/man/CST_RFWeights.Rd b/man/CST_RFWeights.Rd index 9592f18c0707dc9a61e8162ab70eeae4b151e164..3afde99133ee9ceea6be7d0ec47be800c09747cf 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{https://www.worldclim.org}) or CHELSA (\url{https://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://gdal.org/}). It could also be an 's2dv_cube' object.} diff --git a/man/RFTemp.Rd b/man/RFTemp.Rd index 1e519d8800b3e43f873acfa94cd572b118498b18..957ccc918d42b5bbdc160b21d7b2cd3f86ad8b8f 100644 --- a/man/RFTemp.Rd +++ b/man/RFTemp.Rd @@ -107,7 +107,7 @@ High-quality climate prediction data available to WP4 here: \ url{https://www.medscope-project.eu/the-project/deliverables-reports/} and in H2020 ECOPOTENTIAL Deliverable No. 8.1: High resolution (1-10 km) climate, land use and ocean change scenarios here: -\url{https://www.ecopotential-project.eu/images/ecopotential/documents/D8.1.pdf}. +\url{https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5b6cd2324&appId=PPGMS}. } \author{ Jost von Hardenberg - ISAC-CNR, \email{j.vonhardenberg@isac.cnr.it} diff --git a/vignettes/MultiModelSkill_vignette.Rmd b/vignettes/MultiModelSkill_vignette.Rmd index c32bfa2facc05dcfe4770e43dc0cd4f9f86920bc..5d9d123924276abd8a7df512fcdb6228345b3084 100644 --- a/vignettes/MultiModelSkill_vignette.Rmd +++ b/vignettes/MultiModelSkill_vignette.Rmd @@ -13,7 +13,7 @@ vignette: > Multi-model Skill Assessment ----------------------------------------- -**reference**: Mishra, N., Prodhomme, C., & Guemas, V. (2018). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31. +**reference**: Mishra, N., Prodhomme, C., & Guemas, V. (2018). Multi-Model Skill Assessment of Seasonal Temperature and Precipitation Forecasts over Europe, 29-31. The R package s2dv should be loaded by running: diff --git a/vignettes/RainFARM_vignette.Rmd b/vignettes/RainFARM_vignette.Rmd index b2a49a5a4236fbd02e928a911a7b413f389ba735..28ab753ded112a1d670fb8a00abc6dc9d0f8e0b8 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 (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). +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)