cfu r common diagnostics
DESCRIPTION
CFU R common diagnostics. CFU_load. CFU_season. CFU_trend. CFU_clim. CFU_ano. CFU_anocrossvalid. CFU_smoothing. CFU_plotclim. CFU_animvsltime. CFU_plotano. CFU R common diagnostics. CFU_load. Minimum set of arguments : - PowerPoint PPT PresentationTRANSCRIPT
Climate Forecasting Unit
CFU R common diagnostics
CFU_load
CFU_season
CFU_clim
CFU_ano CFU_anocrossvalid
CFU_plotclim CFU_plotano
CFU_smoothing
CFU_trend
CFU_animvsltime
Climate Forecasting Unit
CFU R common diagnostics
CFU_load
Minimum set of arguments :
1) var 2) exp 3) obs (can be obs=NULL) 4) sdates
You can request area-averages, longitudinal or latitudinal averages, 2d fields
You can define any region by sending masks
You can request a subset of leadtimes
You can work on a subdomain by providing lat/lon borders
Climate Forecasting Unit
CFU_season
CFU R common diagnostics
This function computes averages over extended season. It can be used to compute annual means for exemple.
Climate Forecasting Unit
CFU R common diagnostics
CFU_clim
This function computes per-pair climatologies, one climatology per member or one for all the members together.
If you have only one start date, your climatology should be computed as a simple annual cycle not with CFU_clim.
If you don’t have observations, you don’t need the per-pair method. Your clim is clim=CFU_mean1dim(exp, 3)
Climate Forecasting Unit
CFU R common diagnostics
CFU_anocrossvalid
This function computes anomalies using the cross-validation method, i.e. for each startdate, the climatology is computed using all the other startdates. It also uses the per-pair method.
Climate Forecasting Unit
CFU R common diagnostics
CFU_trend
This function provides not only the linear trend but also the linearly detrended data.
Climate Forecasting Unit
CFU R common diagnostics
CFU_load
CFU_clim
CFU_ano CFU_anocrossvalid
CFU_plotclim CFU_plotanoCFU_animvsltime
mod = array(dim=c(nexp, nmemb, nsdates, nltimes) to mod = array(dim=c(nexp, nmemb, nsdates, nltimes, nlat, nlon)
obs = array(dim=c(nobs, nmemb, nsdates, nltimes) to obs = array(dim=c(nobs, nmemb, nsdates, nltimes, nlat, nlon)
Those functions work only with the common diagnostic structure.
Climate Forecasting Unit
CFU R common diagnostics
CFU_season
CFU_smoothing
CFU_trend
For those functions, the input structure is free.
Input matrix can have any number of dimensions and the dimension along which the trend, smoothing or season has to be computed should be specified.
Default parameters : common diagnostic structure, leadtime dimensions for CFU_season/CFU_smoothing, nsdates for CFU_trend
You can use them on any time series
Climate Forecasting Unit
CFU R common diagnostics
CFU_load
CFU_season
CFU_clim
CFU_ano CFU_anocrossvalid
CFU_plotclim CFU_plotano
CFU_smoothing
CFU_trend
CFU_animvsltime
Climate Forecasting Unit
CFU R common diagnosticsCFU_ano CFU_anocrossvalid
CFU_spread
CFU_corr
CFU_RMS
CFU_trend
CFU_ratioRMS
CFU_ratioSDRMSCFU_RMSSS
CFU_consist_trend
CFU_animvsltimeCFU_plotvsltime CFU_plotequimap
Climate Forecasting Unit
CFU R common diagnostics
CFU_spread
CFU_corr
CFU_RMS
CFU_trend
CFU_ratioRMS
CFU_ratioSDRMSCFU_RMSSS
For those functions, the input structure is free.
Default : common diagnostic structure
Scores are computed for each experimental dataset versus each observational dataset in your input matrix.
Climate Forecasting Unit
CFU R common diagnosticsCFU_ano CFU_anocrossvalid
CFU_consist_trend
CFU_animvsltimeCFU_plotvsltime
Those functions expect the common diagnostic structure
Climate Forecasting Unit
CFU R common diagnostics
CFU_plotequimap
For this function, (lat,lon) expected and a second matrix of flags=T/F with the same dimensions is expected for significance level
It has many functionalities to make nice plots for publication. Color levels (square or smoothed), contours, dots …, continents can be filled in grey or show as black lines. Colorbar can be drawn or not….
It can be used in a multipanel after splitting the space with layout
Climate Forecasting Unit
CFU R common diagnostics
CFU_spread
CFU_corr
CFU_RMS
CFU_trend
CFU_ratioRMS
CFU_ratioSDRMSCFU_RMSSS
CFU_consist_trend
Confidence intervals or significance levels or both are systematically provided.
Climate Forecasting Unit
CFU R common diagnostics
CFU_corr
CFU_RMS CFU_ratioRMS
CFU_ratioSDRMSCFU_RMSSS
For those functions, there are issues about the temporal dependance of the data for confidence intervals/significance levels. For non-parametric tests, a window of dependence has to be defined, for parametric ones, a number of independant data has to be defined.
Those functions currently use parametric tests with a number of independant data defined following the classical formula from Von Storch and Zwiers (2001). This might change depending on the literature. Call to CFU_eno
Climate Forecasting Unit
CFU R common diagnostics
CFU_spread
CFU_corr
CFU_RMS
CFU_trend
CFU_ratioRMS
CFU_ratioSDRMSCFU_RMSSS
CFU_consist_trend
bootstrapone sided T-test Fisher transform
chi2
one-sided Fisher test
two-sided Fisher test
one-sided Fisher test
T- distribution
Climate Forecasting Unit
CFU R common diagnosticsCFU_ano CFU_anocrossvalid
CFU_spread
CFU_corr
CFU_RMS
CFU_trend
CFU_ratioRMS
CFU_ratioSDRMSCFU_RMSSS
CFU_consist_trend
CFU_animvsltimeCFU_plotvsltime CFU_plotequimap
Climate Forecasting Unit
CFU R common diagnostics
CFU_eno
CFU_mean1dim
CFU_meanlistdim
CFU_insertdim
CFU_colorbar
For those functions, the input structure is free.
This function makes a colorbar if you send the levels and colors. Useful for multipanels after calling layout
Climate Forecasting Unit
CFU R common diagnostics[vguemas@bor ~]$ R
source(‘/cfu/pub/scripts/R/common_diagnostics.txt’) [1] List of functions : [1] [1] CFU_load[1] CFU_season[1] CFU_clim[1] CFU_ano[1] CFU_ano_crossvalid[1] CFU_smoothing[1] CFU_plotano[1] CFU_plotclim[1] CFU_spread[1] CFU_plotvsltime[1] CFU_corr[1] CFU_RMS[1] CFU_RMSSS
Climate Forecasting Unit
CFU R common diagnostics[1] CFU_ratioRMS[1] CFU_ratioSDRMS[1] CFU_trend[1] CFU_consist_trend[1] CFU_plotequimap[1] CFU_colorbar[1] CFU_animvsltime[1] CFU_eno[1] CFU_enlarge[1] CFU_insertdim[1] CFU_mean1dim[1] CFU_meanlistdim[1] CFU_inilistdims[1] [1] For more information about any function, type info_cd('function name')
info_cd(‘CFU_load’)
Climate Forecasting Unit
CFU R common diagnostics[1] [1] Description [1] ~~~~~~~~~~~~~[1] [1] Load experimental data and corresponding observed ones in 2 matrix with similar structures[1] If loading EC-Earth experiments, PUT FIRST THE EXPERIMENT ID WITH THE LARGEST NUMBER[1] OF MEMBERS & if possible, THE LARGEST NUMBER OF LEADTIMES. If not possible, fill up the nleatime argument.[1] [1] Inputs[1] ~~~~~~~~[1] [1] - var= 'tas','prlr','tos','g500','g200','ta50','psl','hflsd','hfssd','rls','rss','rsds','uas','vas'
Climate Forecasting Unit
[1] - exp=c('ecmwf','ukmo','cerfacs','ifm','DePreSysAsimDec','DePreSysNoAsimDec','DePreSysAsimSeas','ECMWF_S3Seas','ECMWF_S4Seas','ECMWF_S4SeasQWeCI','hadcm3dec','miroc4dec','miroc5dec','mri-cgcm3dec','cancm4dec1','cancm4dec2','cnrm-cm5dec','knmidec','mpimdec','gfdldec','cmcc-cmdec','hadcm3his','miroc4his','miroc5his','mri-cgcm3his','cancm4his','cnrm-cm5his','knmihis','i00k','b013','b014','yve2' ...)[1] - obs=c('ERA40','NCEP','ERAint','GHCN','ERSST','HADISST','GPCP','GPCC','CRU','DS94','OAFlux','DFS4.3','NCDCglo','NCDCland','NCDCoc','GISSglo','GISSland','GISSoc','HadCRUT3glo','HadSST2oc','CRUTEM3land')[1] - sdates=c('YYYYMMDD','YYYYMMDD')[1] - lonmin, lonmax, latmin, latmax : domain border 0 <= lonmin,lonmax <= 360 [1] default : world [1] - nleadtime : optional argument needed only if the first exp does not have the largest number of leadtimes.[1] default : number of leadtimes of the first experiment.
CFU R common diagnostics
Climate Forecasting Unit
[1] - leadtimemin : output only the leadtimes from leadtimemin. default = 1[1] - leadtimemax : output only the leadtimes before leadtimemax. default = nleadtime[1] - output = 'areave' / 'lon' / 'lat' / 'lonlat' [1] 1) Time series of area-averaged variables over the specified domain[1] 2) Time series of meridional averages as a function of longitudes[1] 3) Time series of zonal averages as a function of latitudes[1] 4) Time series of 2d fields[1] default : 'areave' [1] - method = 'bilinear' / 'bicubic' / 'conservative' / 'distance-weighted'[1] Method of interpolation for 'lon' / 'lat' / 'lonlat' output options[1] default : 'conservative' [1] - grid = to choose the output grid [1] possible options : rNXxNY or tTRgrid, ex: r96x72, t106grid[1] default : model grid, argument need to be filled if various exp on various grids
CFU R common diagnostics
Climate Forecasting Unit
[1] - maskmod=list(mask[lon,lat]) = 1/0 : kept/removed grid cell over the entire model domains[1] Warning : list() compulsory even if 1 model !!![1] default : 1 everywhere [1] - maskobs=list(mask[lon,lat]) = 1/0 : kept/removed grid cell over the entire[1] observed domains, only necessary for 'areave' output option [1] Warning : list() compulsory even if 1 dataset !!![1] default : 1 everywhere [1]
CFU R common diagnostics
Climate Forecasting Unit
[1] Outputs[1] ~~~~~~~~~[1] [1] $mod = model outputs[1] $obs = observations[1] $lat = latitudes of the model grid[1] $lon = longitudes of the model grid[1] [1] 2 matrix with dimensions [1] c(nmod/nexp or nobs, nmemb/nparam, nsdates, nltime) if output = 'areave'[1] c(nmod/nexp or nobs, nmemb/nparam, nsdates, nltime, nlat ) if = 'lat'[1] c(nmod/nexp or nobs, nmemb/nparam, nsdates, nltime, nlon ) if = 'lon' [1] c(nmod/nexp or nobs, nmemb/nparam, nsdates, nltime, nlat, nlon) = 'lonlat'[1] [1] Author [1] ~~~~~~~~[1] [1] CFUers <[email protected]> March 2011
CFU R common diagnostics