Computing filter increments using a complex model

The innovations to the model state are easy to derive. Use the NCO Operator ncdiff to difference the two DART diagnostic netCDF files to create the innovations. Be sure to check the CopyMetaData variable to figure out what copy is of interest. Then, use ncview to explore the innovations or the inflation values or …

If the assimilation used state-space inflation, the inflation fields will be added as additional ‘copies’. A sure sign of trouble is if the inflation fields grow without bound. As the observation network changes, expect the inflation values to change.

The only other thing I look for in state-space is that the increments are ‘reasonable’. As the assimilation ‘burns in’, the increments are generally larger than increments from an assimilation that has been cycling for a long time. If the increments keep getting bigger, the ensemble is continually drifting away from the observation. Not good. In ncview, it is useful to navigate to the copy/level of interest and re-range the data to values appropriate to the current data and then hit the ‘>>’ button to animate the image. It should be possible to get a sense of the magnitude of the innovations as a function of time.

Example from a model of intermediate complexity: the bgrid model

I ran a perfect model experiment with the bgrid model in the DART-default configuration and turned on some adaptive inflation for this example. To fully demonstrate the adaptive inflation, it is useful to have an observation network that changes through time. I created two observation sequence files: one that had a single ‘RADIOSONDE_TEMPERATURE’ observation at the surface with an observation error variance of 1.5 degrees Kelvin - repeated every 6 hours for 6 days (24 timesteps); and one that had 9 observations locations clustered in about the same location that repeated every 6 hours for 1.5 days (6 timesteps). I merged the two observation sequences into one using obs_sequence_tool and ran them through perfect_model_obs to derive the observation values and create an obs_seq.out file to run through filter.

Note

Other models may have their ensemble means and spreads and inflation values in separate files. See the table of possible filenames.

$ cd ${DARTROOT}/models/bgrid_solo/work
$ ncdiff analysis.nc preassim.nc Innov.nc
$ ncview preassim.nc &
$ ncview Innov.nc &
$ ncdump -v MemberMetadata preassim.nc
netcdf preassim {
dimensions:
        metadatalength = 64 ;
        member = 20 ;
        time = UNLIMITED ; // (24 currently)
        NMLlinelen = 129 ;
        NMLnlines = 303 ;
        StateVariable = 28200 ;
        TmpI = 60 ;
        TmpJ = 30 ;
        lev = 5 ;
        VelI = 60 ;
        VelJ = 29 ;
variables:
        char MemberMetadata(member, metadatalength) ;
                MemberMetadata:long_name = "Metadata for each copy/member" ;
        ...
        double ps(time, member, TmpJ, TmpI) ;
                ps:long_name = "surface pressure" ;
                ps:units = "Pa" ;
                ps:units_long_name = "pascals" ;
        double t(time, member, lev, TmpJ, TmpI) ;
                t:long_name = "temperature" ;
                t:units = "degrees Kelvin" ;
        double u(time, member, lev, VelJ, VelI) ;
                u:long_name = "zonal wind component" ;
                u:units = "m/s" ;
        double v(time, member, lev, VelJ, VelI) ;
                v:long_name = "meridional wind component" ;
                v:units = "m/s" ;
        double ps_mean(time, TmpJ, TmpI) ;        The ensemble mean   is now a separate variable.
        double t_mean(time, lev, TmpJ, TmpI) ;    The ensemble spread is now a separate variable.
        double u_mean(time, lev, VelJ, VelI) ;    If I was using inflation, they would also be separate variables.
        double v_mean(time, lev, VelJ, VelI) ;
        double ps_sd(time, TmpJ, TmpI) ;
        double t_sd(time, lev, TmpJ, TmpI) ;
        double u_sd(time, lev, VelJ, VelI) ;
        double v_sd(time, lev, VelJ, VelI) ;

data:
  MemberMetadata =
  "ensemble member      1 ",
  "ensemble member      2 ",
  "ensemble member      3 ",
  "ensemble member      4 ",
  "ensemble member      5 ",
  "ensemble member      6 ",
  "ensemble member      7 ",
  "ensemble member      8 ",
  "ensemble member      9 ",
  "ensemble member     10 ",
  "ensemble member     11 ",
  "ensemble member     12 ",
  "ensemble member     13 ",
  "ensemble member     14 ",
  "ensemble member     15 ",
  "ensemble member     16 ",
  "ensemble member     17 ",
  "ensemble member     18 ",
  "ensemble member     19 ",
  "ensemble member     20 " ;
}

This is an exploration of the preassim.nc file. Note that I selected the ‘t’ field, turned the coastlines ‘off’ under the ‘Opts’ button, used the ‘Repl’ instead of ‘Bi-lin’ (to more faithfully represent the model resolution), navigated to copy 23 of 24 (in this case, the inflation mean ) select the inflation mean variable of your choice and advanced to the last timestep. The image plot is pretty boring, but does indicate that the inflation values are restricted to where I put the observations. Right-clicking on the ‘Range’ button automatically re-ranges the colorbar to the min/max of the current data. Clicking on any location generates a time series figure.

This is an exploration of the Innov.nc file as created by ncdiff. Note that the titles are somewhat misleading because they reflect information from the first file given to ncdiff. This time I left the rendering as ‘Bi-lin’ (which obfuscates the model resolution), navigated to copy 1 of 24 (in this case, the ensemble mean ) selected the t_mean variable and advanced to the 6th timestep. Right-click on the ‘Range’ button to reset the colorbar. The image plot confirms that the innovations are restricted to a local region. Clicking on any location generates a time series.

This is fundamentally the same as the previous panel except that I have now selected the ‘u’ u_mean variable. Despite the fact the observations were only of ‘t’, the assimilation has generated (rightly so) increments to the ‘u’ state variable.