These functions detrend observed (CFU_DTO) and forecast (CFU_FTO) anomalies of, e.g., North Pacific (PDO) SSTs,
then compute the observed EOF mode (CFU_EOF) and project the anomalies onto the observed EOF pattern CFU_MODE().

Find an example below.

Detrend obs anomalies:
tmp2 <- CFU_DTO(lons,lats,XY1_ano_obs)

EOF of detrended obs anomalies::
tmp3 <- CFU_EOF(lons,lats,tmp2$ano.dt)

Detrend forecast anomalies:
tmp4 <- CFU_DTF(lons,lats,XY1_ano_exp,exp_gmt_an)

Obs PCs:
tmp11 <- CFU_MODE(lons,lats,XY1_ano_obs,tmp3,mode=1)
Forecast PCs:
tmp12 <- CFU_MODE(lons,lats,XY1_ano_exp,tmp3,mode=1)

New options : ORCA025t_v3.2 and EcEarth3; All the corresponding weigths have been computed and stored at /cfu/pub/scripts/interpolation/weigths

CFU_plotmapmostliktercile() produces a probabilistic forecast map of most likely tercile of a forecast
using the probabilistic bins computed in parallel by CFU_PB().
Similar to http://eurobrisa.cptec.inpe.br.

The data_transfer.sh is serving the purpose for transferring model output from all available HPCs...

Bug fix for loading atmospheric 2d fields introduced when adding the ocean diagnostics options;  Options nleadtime, leadtimemin and leadtimemax now available for oceanic diagnostics; GFDL tos data can be loaded

Distinction between the ORCA1 NEMOv2.2 and NEMO v3.2 grids in the input options. interp uses the weights Isabel and I computed respectively for v2.2 and v3.2

'siaN','sieN','sivN','siaS','sieS','sivS','moc_40N55N_1-2km','moc_30N40N_1-2km'
'max_moc_38N50N_500m-2km','max_moc_40N','heatc','0-315_heatc','373-657_heatc','800-5350_heatc'
'mxl_heatc','NAtl_10N65N_heatc','NAtl_10N65N_0-315_heatc','NAtl_10N65N_373-657_heatc'
'NAtl_10N65N_800-5350_heatc','mxl_NAtl_10N65N_heatc','TAtl_30S30N_heatc','TAtl_30S30N_0-315_heatc'
'TAtl_30S30N_373-657_heatc','TAtl_30S30N_800-5350_heatc','mxl_TAtl_30S30N_heatc','NPac_10N70N_heatc'
'NPac_10N70N_0-315_heatc','NPac_10N70N_373-657_heatc','NPac_10N70N_800-5350_heatc','mxl_NPac_10N70N_heatc'
'TPac_30S30N_heatc','TPac_30S30N_0-315_heatc','TPac_30S30N_373-657_heatc','TPac_30S30N_800-5350_heatc'
'mxl_TPac_30S30N_heatc','Arc_65N90N_heatc','Arc_65N90N_0-315_heatc','Arc_65N90N_373-657_heatc'
'Arc_65N90N_800-5350_heatc','mxl_Arc_65N90N_heatc','Ant_90S60S_heatc','Ant_90S60S_0-315_heatc'
'Ant_90S60S_373-657_heatc','Ant_90S60S_800-5350_heatc','mxl_Ant_90S60S_heatc','TInd_30S30N_heatc'
'TInd_30S30N_0-315_heatc','TInd_30S30N_373-657_heatc','TInd_30S30N_800-5350_heatc','mxl_TInd_30S30N_heatc'

CFU_load        : new behaviour, only the missing experiment files are printed to screen instead of systematic print of the path
CFU_plotclim    : option to remove the legend
CFU_plotequimap : option to plot missing value in any color

- TOA net longwave flux
- TOA net shortwave flux
- TOA net clear sky longwave flux
- TOA net clear sky shortwave flux

- /cfu/pub/scripts/postp_ecearth/header/table_of_variable (has also been modified)

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new function CFU_colorbar

- "oce" (for ocean) and "atm" (for atmosphere) got introduced for nccf scripts to distinguish easily...

- it is working fine with "ithaca" and "mn"
- use of the script is very simple, fill a few user defined variables and issue the following command from cfu desktops:
 nohup ./data_transfer.sh >& expid.log &

and sea ice integrated diagnostics

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functions and CFU_load handles all the CMIP5 data stored in /cfunas.
Furthermore, as no definite conclusion has yet been taken about the
assessment of the number of independent data in a time series, I put
back in v20, the CFU_eno function of v18 which relies on the VonStorch
and Zwiers formula and which I bounded the output by the actual number
of data. This avoids your playing with the different versions.
    I also put some default values to a number of parameters in all the
functions to make easier your use of the functions and to avoid any
mistake. If you strictly follows the common diagnostic structure for
using all the functions, send the ensemble means to CFU_corr, CFU_RMS,
CFU_trend and CFU_consist_trend and select only 1 experiment and 1
observational dataset for CFU_ratioRMS, the default parameters should
be correct. Of course, when you know well the function, you can play
with the parameters.

1) CFU_load : The options to load the new CMIP5 experiments on /cfunas are
:
'cancm4dec1','cancm4dec2','cnrm-cm5dec','knmidec','cancm4his','cnrm-cm5his','knmihis'

2) CFU_ratioSDRMS : The input should be var_exp and var_obs with
dimensions : c(nexp/nobs, nmemb/nparam, nsdates, nltime) to c(nmod/nexp,
nmemb/nparam, nsdates, nltime, nlevel, nlat, nlon). The output is the
ratio of the spread of the members around the ensemble-mean over the RMSE
of the ensemble-mean together with the p-value (Ho : SD/RMSE = 1)

3) CFU_ano_crossvalid : The input should be var_exp and var_obs with
dimensions : c(nexp/nobs, nmemb/nparam, nsdates, nltime) to c(nmod/nexp,
nmemb/nparam, nsdates, nltime, nlevel, nlat, nlon). The output are ano_exp
and ano_obs which are the anomalies obtained by subtracting the
climatologies computed in a cross-validation mode and with the per-pair
method. ano_exp/ano_obs have the same dimensions as var_exp/var_obs.

--These lines, and those below, will be ignored--

M    trunk/R/common_diagnostics.txt
A    tags/R/v20
AM   tags/R/v20/common_diagnostics.txt

CFU_eno has been modified to apply a more reliable and more stable formula to provide the number of independant data of a time series. The result is still not perfect but it is much better than the previous version which provided crazy results. Your confidence intervals and significance levels for RMSE and correlation should then be modified with the version 19 of the common diagnostics toward more accurate estimations. Furthermore, an option has been introduced in CFU_plotequimap to be able to remove the colorbar. Finally, the ERSST & HadISST dataset are now available also when loading tas in CFU_load (previously, they were only available when loading tos). 

I have prepared a new function called CFU_ratioRMS to compute the ratio of RMSE for two experiments and provide the p-value of the Fisher test with Ho: RMSE1/RMSE2 = 1. Furthermore, a trouble has been detected in the computation of the number of independant data in a time series. The computation of the confidence interval and significance level in CFU_corr and CFU_RMS may have been affected by this trouble as they rely on parametric tests. I advise you to check whether the significance level and confidence intervals of your correlation and RMSE is modified with version 18. Some more details are described below :

1) CFU_ratioRMS : You have to provide three matrix:  two for the 2 experiments you wish to compare and 1 observational dataset. All three input matrix should have the same dimensions. The output matrix contains a dimension of length 2 at the position of the input dimension along which the RMSE has been requested : the ratio of the two RMSE1 + the p-value of the Fisher test.

2) The computation of the number of independant data in a time series rely on a formula provided in the VonStorch & Zwiers and which seems not to be accurate enough. The number of independant data can be larger than the true number of data in the original formulation. I have made a temporary fix which consists in bounding the number of independant data by the true number of data. This may not be an optimal solution but it seems to provide good results on the few examples I have tested. 

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