Re: drawing Taylor diagram ...

Hi,

According to your suggestion, i interpolated the all data into CRU
observation grid to calculate cross corelations. The dataset that i used
to plot Taylor diagram, is basically output of an regional climate model
(RegCM3) and the domain is not big (eastern part of Europe). Is it
necessary to use the weights to plot taylor diagram? How can i generate
it? Is it based on latitude?

Thanks,

--ufuk

> On 05/17/2010 12:30 PM, u.utku.turuncoglu@be.itu.edu.tr wrote:
>> Hi,
>>
>> I try to plot a Taylor diagram to compare the different model
>> simulations
>> with observations. So, i have some basic question about the procedure;
> Hi Ufuk,
>
> [1] No.
>
> [2] See Example 7 at
>
> http://www.ncl.ucar.edu/Applications/taylor.shtml
>
>
>>
>> 1 - I am using monthly mean precipitation rate field (mm/day) and
>> surface
>> temperature that is averaged over specified region (i will plot them in
>> different Taylor diagrams). Is it necessary to interpolate all model
>> results and observation into a common grid before calculating variances
>> and cross correlation?
>
> It is easy to compute the variances on the original grids. However, the
> correlations requires the same grid. Hence, it is best to interpolate
> to a common grid. This grid is commonly the reference grid configuration
> but that is up to you.
>
>>
>> 2 - As you already know that the Taylor diagram NCL function basically
>> expects the ratio of the standardized variances and cross correlation
>> between model and observation. To calculate the variance, i am using
>> "variance" function but i think i have to standardize the fields before
>> applying variance function. Is it correct?
>
> vcntl => reference or control variable
> vtest => model variable
>
> If the region being tested is 'large' then a weight variable [wgt]
> should be created. If your region is 'small', you can ignore the
> weight [wgt=1]. Given that you are looking at a very small area, you
> can ignore any weighting. Also you have masked out all points outside
> your area of interest.
>
> var_cntl = variance(vcntl)
> var_test = variance(vtest)
>
> ratio = var_test/var_cntl
>
> By definition, the correlation is already standardized [-1 to +1]
> So, if var_test and var_cntl are on the same grid and you are not
> applying weighting then the pattern correlation is
>
>
> cc = escorc( ndtooned(var_cntl), ndtooned(var_test) )
>
> ===
> For a 'large' area, you will have to apply weighting [same grids]
>
> wgt = conform(vcntl, wt , rank-2)
> ;;wgt = mask(wgt, lsflag.eq.0, False) ; if desired
> ;;wgt = mask(wgt, lsflag.eq.1, False)
>
> ; temporary variables
> sumw = sum(wgt)
> sumwc = sum(wgt*vcntl)
> sumwt = sum(wgt*vtest)
> ; wgted areal mean
> wmean_cntl = sumwc/sumw
> wmean_test = sumwt/sumw
> ; wgted areal variance
> wvar_cntl = sum(wgt*(vcntl-wmean_cntl)^2)/sumw
> wvar_test = sum(wgt*(vtest-wmean_test)^2)/sumw
>
> ; wgted correlation coef
> wcc = (sum(wgt*vcntl*vtest) - sumwc*sumwt/sumw )/ \
> ((sum(wgt*vcntl^2) - sumwc^2/sumw) * \
> (sum(wgt*vtest^2) -sumwt^2/sumw))^0.5
>
>
> =======
>
> Good luck
> D
>
> If yes, can i use the
>> "dim_standardize" to do that? The current structure of my scripts seems
>> like,
>>
>> model1_variance = variance(model1)
>> obs_variance = variance(obs)
>>
>> cc = escorc(obs, model1)
>> ratio = model1_variance/obs_variance
>>
>> but i think that it must be,
>>
>> model1_variance = variance(dim_standardize (model1))
>> obs_variance = variance(dim_standardize (obs))
>>
>> cc = escorc(obs, model1)
>> ratio = model1_variance/obs_variance
>>
>> PS: you can find my taylor NCL scripts as attachment.
>>
>> Any suggestions can be helpful.
>> Thanks,
>>
>> --ufuk
>>
>>
>>
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>
>
> --
> ======================================================
> Dennis J. Shea tel: 303-497-1361 |
> P.O. Box 3000 fax: 303-497-1333 |
> Climate Analysis Section |
> Climate & Global Dynamics Div. |
> National Center for Atmospheric Research |
> Boulder, CO 80307 |
> USA email: shea 'at' ucar.edu |
> ======================================================
>
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