[NARCCAP-discuss] narccap-discuss Digest, Vol 26, Issue 1

[Alejandro Riano]

Kelli,

I don't know if this helps, but there are some papers by Doiminguez et al
and Giorgio et al. that discuss the Reliability Ensemble Average. This
method helps the user qualify each NARCCAP model pairing in regards to the
different climatological variables. It evaluates all the models based upon
their skill in modeling present day climate and their convergence in
predicting future day climate. Dominguez has modified the method and
included both temperature and precipitation for evaluating each model
pairing. Remember, these models are rather coarse both spatially and
temporally and replication of local climate is limited to the physics
calculations of the size of the model. I have found that frontal weather
events are better represented for a region that I have studied. If you have
any questions, feel free to contact me.

Best Regrads,

Alejandro Riano


On Wed, Sep 11, 2013 at 2:00 PM,
<narccap-discuss-request at mailman.ucar.edu>wrote:

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>    1. Re: High observed vs. modeled errors for precipitation
>       (Kelli Walters)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 09 Sep 2013 15:55:49 -0700
> From: Kelli Walters <waltersk at onid.orst.edu>
> Subject: Re: [NARCCAP-discuss] High observed vs. modeled errors for
>         precipitation
> To: Discussion of NARCCAP data - uses and questions
>         <narccap-discuss at mailman.ucar.edu>
> Message-ID: <20130909155549.16646u7fa2cy1o0s at webmail.oregonstate.edu>
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>
> Obey,
>
> Thank you for your input. I am looking into some bias correction methods
> now.
>
> Thanks,
> Kelli
>
> --
> KELLI WALTERS, EIT
> M.S. Candidate
> Civil Engineering | Water Resources
> Oregon State University
> waltersk at onid.orst.edu
>
>
>
> Quoting "Obeysekera, Jayantha" <jobey at sfwmd.gov>:
>
> > Hi Kelli,
> > I am not surprised.
> > We have had similar findings in Florida.  I believe your only choice
> > may be to "bias-correct" the NARCCAP data before they are used in
> > hydrological models.  I personally do not think that is the right
> > way to use RCM data but there appears to be no other choice.
> > Obey
> > ________________________________________
> > From: narccap-discuss-bounces at mailman.ucar.edu
> > [narccap-discuss-bounces at mailman.ucar.edu] on behalf of Kelli
> > Walters [waltersk at onid.orst.edu]
> > Sent: Tuesday, August 27, 2013 8:17 PM
> > To: narccap-discuss at mailman.ucar.edu
> > Subject: [NARCCAP-discuss] High observed vs. modeled errors for
> precipitation
> >
> > Hello,
> >
> > I am evaluating which NARCCAP model runs best fit my observed data to
> > determine which ones I should use as climate projections in a
> > hydrologic model. I am comparing the past data set (1970-2000) from
> > the models to observed data for temperature (max and min) and
> > precipitation. For temperature, the models seem to accurately reflect
> > the observations, with Nash-Sutcliffe efficiencies around 0.5 (r2 of
> > 0.5-0.6) for daily comparison and in the range of 0.6-0.9 (r2 of about
> > 0.85) for monthly averages.
> >
> > However, for precipitation, my errors have been much larger. I am
> > getting Nash-Sutcliffe efficiencies of around -0.4 to -1 (r2 of 0.02
> > and smaller) for both daily values and monthly averages. What I am
> > wondering is: are these error values normal or in the same range as
> > other people are getting for precipitation?
> >
> > Do you have any papers or references that give these error metrics for
> > precipitation when comparing the modeled data to observations? I have
> > been looking for literature to back up what I am finding, but I can't
> > seem to find what values are considered "acceptable" for precipitation
> > error. Any help or sources you can provide on this would be much
> > appreciated.
> >
> > Thank you,
> > Kelli
> >
> >
> > --
> > KELLI WALTERS, EIT
> > M.S. Candidate
> > Civil Engineering | Water Resources
> > Oregon State University
> > waltersk at onid.orst.edu
> >
> >
> >
> >
> >
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> End of narccap-discuss Digest, Vol 26, Issue 1
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