grid-cell specific percentiles: sorting without missing values to find percentile

From: Marc Pace Marcella <marcpace_at_nyahnyahspammersnyahnyah>
Date: Wed Mar 20 2013 - 19:43:13 MDT

Hi all,

I am trying to find different percentiles of occurrence (90th, 75th, 25th, etc) of a certain RCM output variable in a time, lat, lon netcdf file. I realize that the options qsort and dim_pqsort_n do not support ignoring missing values.

For a 1D array I am able to use the inz=ind(.not.ismissing(AOD)) command and then the sort_AOD_nz=AOD_nz(inz) to remove all values which are of fill_value, but I am getting tripped up with the added time and 2D grid. Below is the command I am using to mask any AOD events that are under 0.1 and replacing with fill_value:

ncl> AOD_ALL_nz=where(AOD_all_sum .gt. 0.1, AOD_all_sum, AOD_all_sum@_FillValue) ;AOD_ALL_nz(time, lat, lon)

ncl> AOD_sorted_values=dim_pqsort_n(AOD_ALL_nz_sorted,2,0) ; yields the sorted array increasing in time but with missing_values at the beginning of file for each grid-cell---id like to remove these so they dont count to "skewing" the percentiles to be lower than they actually should be

>From here I am unable to figure out how to remove those values which are fill_value when sorting to then find the corresponding percentiles. Essentially I would like to follow a similar approach of the 1D or find the grid-cell specific percentiles some other way.

I tried searching the list serve but to no avail. Does anyone have an idea of how to figure out these percentiles (i.e. neglecting events below a certain value) using the sort function or another work-around?

Thanks for any help!


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Received on Wed Mar 20 19:43:24 2013

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