[gradsusr] Cluster Ensemble members

[Thomas Davies]

Thank you for those links, still trying to wrap my head around it !

I was wondering whether it would work to find each members distance from
the ens mean and deal with these values externally in Python, in which
clustering modules can be imported?

On Thu, Feb 18, 2016 at 1:17 PM, Bill Bua - NOAA Affiliate <
bill.bua at noaa.gov> wrote:

> Thomas --
>
> Correlation of each member to the others is part of the first step, and
> represents the distance of the members from each other. Another part is
> choosing the forecast variable most relevant for your clustering .... 500
> hPa heights (this is a common one), sea level pressure, or some other
> variable. While a global domain can be used, you may even want to choose a
> smaller domain over which to cluster, say Europe or North America.
>
> Once those decisions are made, things get a little fuzzy for me, but I can
> recommend you read Tracton and Kalnay 1993 (Tracton, M. S., and E. Kalnay,
> 1993: Operational ensemble prediction at the National Meteorological
> Center: Practical aspects.* Wea. Forecasting*, *8*, 379–398) or.Toth et
> al. (Toth, Z., E. Kalnay, S. M. Tracton, R. Wobus, and J. Irwin, 1997: A
> synoptic evaluation of the NCEP ensemble. Wea. Forecasting,12, 140–153
> <http://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-11-00016.1>.) for
> details of using *anomaly* correlation as a distance metric in a
> clustering algorithm.
>
> Good luck!
>
> Dr. William R. (Bill) Bua
> UCAR/COMET/NCEP/EMC
> 5830 University Research Court #2784
> College Park, MD 20740
>
>
> On Wed, Feb 17, 2016 at 5:32 PM, Thomas Davies <tsd.davies at gmail.com>
> wrote:
>
>> Thanks for your reply.
>>
>> I looped over each GEFS ensemble member, calculating the correlation with
>> every other member and stored these values like this:
>>
>> *p1p2 0.625844*
>> *p1p3 0.778631*
>> *p1p4 0.718804*
>> *p1p5 0.597726*
>> *p1p6 0.73876*
>> *p1p7 0.744276*
>> *p1p8 0.683437*
>> *p1p9 0.548653*
>> *p1p10 0.479272*
>> *p1p11 0.668351*
>> *p1p12 0.600638*
>> *p1p13 0.698219*
>> *p1p14 0.606607*
>> *p1p15 0.536851*
>> *p1p16 0.736465*
>> *p1p17 0.564005*
>> *p1p18 0.557531*
>> *p1p19 0.641763*
>> *p1p20 0.5872*
>> *p1p21 0.57592*
>> *...*
>> p21p1 0.57592
>> p21p2 0.623274
>> p21p3 0.630096
>> p21p4 0.551386
>> p21p5 0.561231
>> p21p6 0.60267
>> p21p7 0.638492
>> p21p8 0.646961
>> p21p9 0.5132
>> p21p10 0.621424
>> p21p11 0.684271
>> p21p12 0.541928
>> p21p13 0.502403
>> p21p14 0.671402
>> p21p15 0.637574
>> p21p16 0.76546
>> p21p17 0.564914
>> p21p18 0.580842
>> p21p19 0.644114
>> p21p20 0.599859
>>
>> However I am not sure how I should progress. I have read through the link
>> you provided but couldn't find anything useful, all I know is that I need
>> to firstly correlate between members
>>
>> On Wed, Feb 17, 2016 at 6:02 PM, Stephen McMillan <
>> smcmillan at planalytics.com> wrote:
>>
>>> Thomas,
>>>
>>> In the GrADS Documentation Index (
>>> http://cola.gmu.edu/grads/gadoc/gadocindex.html), under the letter "E"
>>> you can find several relevant topics, such as "ensemble dimension" and
>>> "eloop."  One or more may answer your questions.
>>>
>>> Stephen McMillan
>>>
>>> On Wed, Feb 17, 2016 at 12:27 PM, Thomas Davies <tsd.davies at gmail.com>
>>> wrote:
>>>
>>>> Hi,
>>>>
>>>> Does anybody know how to cluster ensemble members showing similar
>>>> solutions and display? Is there a quick command to do this
>>>>
>>>> Thanks in advance
>>>>
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>>>>
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>>
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>
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