Re: how to identify the control in the tigge ensemble grib2 data.

Dave,
Thank you so much for your perfect answers to my last two questions. I got it.
My first question is about the definition of another set of data in addition to the ensemble ones in the tigges data.
For example, assuming f is connected to one ecmwf tigge data, "vars=getfilevarnames(f)", the output of "print(vars)" is :
^^^^
ncl 27> print(vars)

Variable: vars
Type: string
Total Size: 128 bytes
            16 values
Number of Dimensions: 1
Dimensions and sizes: [16]
Coordinates:
(0) lv_ISBL0
(1) lon_0
(2) lat_0
(3) lv_ISBL1
(4) ensemble0
(5) ensemble0_info
(6) gh_P1_L100_GGA0
(7) v_P1_L100_GGA0
(8) u_P1_L100_GGA0
(9) q_P1_L100_GGA0
(10) t_P1_L100_GGA0
(11) gh_P0_L100_GGA0
(12) v_P0_L100_GGA0
(13) u_P0_L100_GGA0
(14) q_P0_L100_GGA0
(15) t_P0_L100_GGA0
VVV

For variables of ?_P1_L100_GGA0 , they are the ensemble fields of 4 dimension.
However, what are those 3d variables of form-?_p0_L100_GGA0, like q_p0_l100_GGA0?

Your further help is appreciated.
Best,
Ting Lei

> Subject: Re: how to identify the control in the tigge ensemble grib2 data.
> From: dbrown@ucar.edu
> Date: Thu, 8 Nov 2012 12:56:15 -0700
> CC: ncl-talk@ucar.edu; schuster@ucar.edu
> To: leiting2002@hotmail.com
>
> Hi Ting,
> Sorry for the delay in answering. I am not quite sure what you mean in your first question. As for number 2, when NCL reads a GRIB file with ensemble data (including tigge data) it creates a virtual string variable that includes information from the GRIB records about what type of ensemble member it is. In the file dump on the web that you pointed to, this variable is listed as:
>
> string ensemble0_info ( ensemble0 )
> long_name : ensemble elements description
>
> Here is what it looks like in a similar file (this file contains 3 different ensembles, with the 3rd one having 50 elements plus a control run):
>
> ncl_filedump z_tigge_c_ecmf_20061028120000_0060_pl_glob_prod.grib2 -v ensemble2_info
>
> ....
> Variable: ensemble2_info (file variable)
> Type: string
> Total Size: 408 bytes
> 51 values
> Number of Dimensions: 1
> Dimensions and sizes: [ensemble2 | 51]
> Coordinates:
> ensemble2: [0..50]
> Number Of Attributes: 1
> long_name : ensemble elements description
> unperturbed low-resolution control forecast
> positively perturbed forecast # 1
> positively perturbed forecast # 2
> positively perturbed forecast # 3
> positively perturbed forecast # 4
> positively perturbed forecast # 5
> positively perturbed forecast # 6
> positively perturbed forecast # 7
> positively perturbed forecast # 8
> positively perturbed forecast # 9
> positively perturbed forecast # 10
> positively perturbed forecast # 11
> positively perturbed forecast # 12
> positively perturbed forecast # 13
> positively perturbed forecast # 14
> positively perturbed forecast # 15
> positively perturbed forecast # 16
> positively perturbed forecast # 17
> positively perturbed forecast # 18
> positively perturbed forecast # 19
> positively perturbed forecast # 20
> positively perturbed forecast # 21
> positively perturbed forecast # 22
> positively perturbed forecast # 23
> positively perturbed forecast # 24
> positively perturbed forecast # 25
> positively perturbed forecast # 26
> positively perturbed forecast # 27
> positively perturbed forecast # 28
> positively perturbed forecast # 29
> positively perturbed forecast # 30
> positively perturbed forecast # 31
> positively perturbed forecast # 32
> positively perturbed forecast # 33
> positively perturbed forecast # 34
> positively perturbed forecast # 35
> positively perturbed forecast # 36
> positively perturbed forecast # 37
> positively perturbed forecast # 38
> positively perturbed forecast # 39
> positively perturbed forecast # 40
> positively perturbed forecast # 41
> positively perturbed forecast # 42
> positively perturbed forecast # 43
> positively perturbed forecast # 44
> positively perturbed forecast # 45
> positively perturbed forecast # 46
> positively perturbed forecast # 47
> positively perturbed forecast # 48
> positively perturbed forecast # 49
> positively perturbed forecast # 50
>
> You can always print out this variable to discover which member is the control run, but I believe that in general the control run is always the first element of the ensemble dimension.
>
> As far as question 3 goes, are you actually encountering single tigge GRIB files that exceed 2GB in size? Even recent versions of NCL cannot deal with GRIB2 files that are bigger than 2GB (it can handle greater than 2GB for GRIB1 files). If you need to split large GRIB2 files into a smaller size, you can use wgrib2. You will need to look at their documentation to see what flags to set.
> -dave
>
>
> On Nov 7, 2012, at 2:51 PM, À×öª wrote:
>
> > Dear NCL users,
> > Now, I 'm using the tigge grib2 data as described at http://www.ncl.ucar.edu/Applications/Prints/tigge_ncl_filedump.txt.
> >
> > I have 3 questions:
> > 1). what is ,say, q_P0_L100_GGA0 , when q_P1_L100_GGA0 is for the 51 member.
> >
> > 2), in the 51 member fields( like q_p1_l100_gGA0), there is one control run and 50 perturbed run.
> > How can I know which member is corresponding to the control run.
> > 3) for on the system I 'm working on is the older ncl with 2GB barrier,
> > that will be great if there are any ready-to-use script to split that kind of data.
> > Your help is appreciated.
> > Best,
> > Ting
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Received on Thu Nov 8 13:23:15 2012