[ncl-talk] model ensemble EOF analysis

[Sri nandini]

Thank you.

The idea is that SSH variability is computed over the ensemble 
dimension, rather than the traditional time-dimension- i.e computing EOF 
across individual ensemble member at each time step with looping, in 
which case the below would not make sense .i.e averaging the ensemble 
dimension?

Perhaps i should perform the EOF on both the time and ensemble dimension 
as variations across time and ensemble are supposed to be the same after 
detrending, the more samples the more robust the EOF is?

Best

Sri


On 21.05.20 05:09, Dennis Shea wrote:
> At each time stelp,and lat/lon location all 100 ensembel members
>
>
> ssh_ens_mean = *dim_avg_n_Wrap* 
> <http://www.ncl.ucar.edu/Document/Functions/Contributed/dim_avg_n_Wrap.shtml>(ssh,1)  
> ; average
>  printVarSummary(ssh_ens_mean)
>  printMinMax(ssh_ens_mean,0)
>
> --
> Input  'ssh_ens_mean'  as you would any other variable   to the eof 
> function
>
> On Wed, May 20, 2020 at 7:29 AM Sri nandini via ncl-talk 
> <ncl-talk at ucar.edu <mailto:ncl-talk at ucar.edu>> wrote:
>
>     Hello dear fellow ncl users,
>
>     I have been analyzing and plotting the standard EOF with success.
>     Now i
>     wish to proceed onto model ensemble EOF but having problems
>     understanding this coding.
>
>     My original data is in this format: SSH=   [time | 240] x [ens |
>     100] x
>     [lat | 45] x [lon | 90]
>     How can i modify the standard EOF script on the NCL page to
>     perform it
>     on model ensemble of 100 members?e.g through looping it?
>
>     Would be grateful for an example.
>
>     best
>
>     sri
>
>
>
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