[ncl-talk] Computing coefficient of determination with gridded datasets
Rashed Mahmood
rashidcomsis at gmail.com
Sat Jan 13 11:58:38 MST 2018
Hi Efren,
I think, the issue is that both variables have only 1 time step, So what
would be correlation if non of variables is changing in time?
Cheers,
Rashed
On Sat, Jan 13, 2018 at 10:13 AM, Efren Lopez Blanco via ncl-talk <
ncl-talk at ucar.edu> wrote:
> Hi Rick,
>
>
> Thanks for the answer, I thought also about the differences between
> missing values and fill values. I tested your suggestion, and the result
> was the same. Since I am dealing with doubles, I standardized both (missing
> values and fill values) to -9999. Quick and dirty, but in order to make
> sure both attributes are the same, I performed:
>
>
>
> cveg_HWSD_b_avg_3d at _FillValue = -9999
>
> cveg_HWSD_b_avg_3d at missing_value = -9999
>
> lpjml_hadgem2_es_avg_mask_3d at _FillValue = -9999
>
> lpjml_hadgem2_es_avg_mask_3d at missing_value = -9999
>
> cveg_HWSD_b_avg_3d at _FillValue = cveg_HWSD_b_avg_3d at missing_value
>
> lpjml_hadgem2_es_avg_mask_3d at _FillValue = lpjml_hadgem2_es_avg_mask_3d@
> missing_value
>
> delete(cveg_HWSD_b_avg_3d at missing_value) ; not necessary, just
> cleaning up
>
> delete(lpjml_hadgem2_es_avg_mask_3d at missing_value) ; not necessary,
> just cleaning up
>
>
>
>
>
> This resulted in:
>
>
>
> Variable: cveg_HWSD_b_avg_3d
>
> Type: double
>
> Total Size: 106560 bytes
>
> 13320 values
>
> Number of Dimensions: 3
>
> Dimensions and sizes: [time | 1] x [lat | 37] x [lon | 360]
>
> Coordinates:
>
> time: [1..1]
>
> lat: [79.5..43.5]
>
> lon: [-179.5..179.5]
>
> Number Of Attributes: 5
>
> average_op_ncl : dim_avg_n over dimension(s): time
>
> units : g C m-2
>
> long_name : C stored in Cveg
>
> percentile : 50
>
> _FillValue : -9999
>
> (0) C stored in Cveg: min=0.1382737806910972
> max=12.84650588106167
>
>
>
> Variable: lpjml_hadgem2_es_avg_mask_3d
>
> Type: double
>
> Total Size: 106560 bytes
>
> 13320 values
>
> Number of Dimensions: 3
>
> Dimensions and sizes: [time | 1] x [lat | 37] x [lon | 360]
>
> Coordinates:
>
> time: [1..1]
>
> lat: [79.5..43.5]
>
> lon: [-179.5..179.5]
>
> Number Of Attributes: 5
>
> units : kg m-2
>
> long_name : Carbon Mass in Vegetation
>
> standard_name : vegetation_carbon_content
>
> average_op_ncl : dim_avg_n over dimension(s): time
>
> _FillValue : -9999
>
> (0) Carbon Mass in Vegetation: min=3.493304393487051e-05
> max=15.10204410552979
>
>
>
> And then the escorc_n function [lpjml_r = escorc_n(cveg_HWSD_b_avg_3d,lpjml_hadgem2_es_avg_mask_3d,
> 0, 0)], which again has resulted in min=-9999 max=-9999. The issue is
> clearly in the escorc_n function.
>
>
>
> Best,
>
> Efren
>
>
>
> *From: *Rick Brownrigg <brownrig at ucar.edu>
> *Date: *Saturday, 13 January 2018 at 16.49
> *To: *Efren Lopez Blanco <elb at bios.au.dk>
> *Cc: *"ncl-talk at ucar.edu" <ncl-talk at ucar.edu>
> *Subject: *Re: [ncl-talk] Computing coefficient of determination with
> gridded datasets
>
>
>
> Pure speculation on my part, but I notice that both variables have
> different values for their missing_value vs. _FillValue attributes. I
> wonder which value actually occurs in the data? I *think* if both are
> present as attributes, NCL will honor the _FillValue value, and then values
> of missing_value get treated as data (?) Something to perhaps check out.
>
> Rick
>
>
>
> On Sat, Jan 13, 2018 at 3:21 AM, Efren Lopez Blanco via ncl-talk <
> ncl-talk at ucar.edu> wrote:
>
> Hi there,
>
>
>
> I would like to compute the coefficient of determination (r squared)
> between 2 gridded datasets of the form (time,lat,lon). In order to do so, I
> aim to apply the escorc_n function. The two datasets I am using look like:
>
>
>
> Variable: *cveg_HWSD_b_avg_3d*
>
> Type: double
>
> Total Size: 106560 bytes
>
> 13320 values
>
> Number of Dimensions: 3
>
> Dimensions and sizes: [time | 1] x [lat | 37] x [lon | 360]
>
> Coordinates:
>
> time: [1..1]
>
> lat: [79.5..43.5]
>
> lon: [-179.5..179.5]
>
> Number Of Attributes: 6
>
> average_op_ncl : dim_avg_n over dimension(s): time
>
> missing_value : -9999
>
> units : g C m-2
>
> long_name : C stored in Cveg
>
> percentile : 50
>
> _FillValue : 1.000000020040877e+20
>
> (0) C stored in Cveg: min=0.1382737806910972
> max=12.84650588106167
>
>
>
> Variable: *lpjml_hadgem2_es_avg_mask_3d*
>
> Type: double
>
> Total Size: 106560 bytes
>
> 13320 values
>
> Number of Dimensions: 3
>
> Dimensions and sizes: [time | 1] x [lat | 37] x [lon | 360]
>
> Coordinates:
>
> time: [1..1]
>
> lat: [79.5..43.5]
>
> lon: [-179.5..179.5]
>
> Number Of Attributes: 6
>
> units : kg m-2
>
> long_name : Carbon Mass in Vegetation
>
> standard_name : vegetation_carbon_content
>
> average_op_ncl : dim_avg_n over dimension(s): time
>
> missing_value : 1e+20
>
> _FillValue : 1.000000020040877e+20
>
> (0) Carbon Mass in Vegetation: min=3.493304393487051e-05
> max=15.10204410552979
>
>
>
> Then I apply the function:
>
>
>
> lpjml_r = escorc_n(cveg_HWSD_b_avg_3d,lpjml_hadgem2_es_avg_mask_3d, 0,
> 0) ; r(nlat,mlon)
>
>
>
> However, I’ve noticed the output only contain NAs:
>
>
>
> Variable: *lpjml_r*
>
> Type: double
>
> Total Size: 106560 bytes
>
> 13320 values
>
> Number of Dimensions: 2
>
> Dimensions and sizes: [lat | 37] x [lon | 360]
>
> Coordinates:
>
> lat: [79.5..43.5]
>
> lon: [-179.5..179.5]
>
> Number Of Attributes: 6
>
> missing_value : -9999
>
> average_op_ncl : dim_avg_n over dimension(s): time
>
> units : g C m-2
>
> long_name : C stored in Cveg
>
> percentile : 50
>
> _FillValue : 1.000000020040877e+20
>
> (0) C stored in Cveg: min=1.000000020040877e+20
> max=1.000000020040877e+20
>
>
>
> Am I doing something wrong? I’ve also tried to convert from double to
> float.
>
>
>
> Best,
>
> Efren
>
>
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