CFU R common diagnostics

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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

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 Presentation

Transcript of CFU R common diagnostics

Page 1: CFU R common diagnostics

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CFU R common diagnostics

CFU_load

CFU_season

CFU_clim

CFU_ano CFU_anocrossvalid

CFU_plotclim CFU_plotano

CFU_smoothing

CFU_trend

CFU_animvsltime

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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

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CFU_season

CFU R common diagnostics

This function computes averages over extended season. It can be used to compute annual means for exemple.

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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)

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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.

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CFU R common diagnostics

CFU_trend

This function provides not only the linear trend but also the linearly detrended data.

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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.

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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

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CFU R common diagnostics

CFU_load

CFU_season

CFU_clim

CFU_ano CFU_anocrossvalid

CFU_plotclim CFU_plotano

CFU_smoothing

CFU_trend

CFU_animvsltime

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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

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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.

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CFU R common diagnosticsCFU_ano CFU_anocrossvalid

CFU_consist_trend

CFU_animvsltimeCFU_plotvsltime

Those functions expect the common diagnostic structure

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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

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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.

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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

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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

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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

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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

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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

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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’)

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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'

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[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

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[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

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[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

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[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