Thursday A: Advanced model features¶
Note that tutorial Data assimilation in 4D is a prerequisite for this tutorial. You can obtain the solutions from these earlier tutorials by issuing:
In this tutorial we will explore some of the more advanced features of the assimilation runs as they relate to the model interface, including:
Running with a pseudo model.
Running with lower resolution increments.
Running with dynamically generated B matrix models.
Step 1: Revive your environment¶
Before anything can be run we need to revive the parts of the environment that are not preserved when the instance is stopped and restarted.
Begin by entering the container again:
cd ~/ singularity shell -e jedi-gnu-openmpi-dev_latest.sif
Your prompt should now look something like:
Once in the container be sure also to remove limits the stack memory to prevent spurious failures:
ulimit -s unlimited ulimit -v unlimited
We installed FV3-JEDI-TOOLS in previous tutorials but the path still needs to be set in each session:
Set the path to the JEDI build directory
Step 2: Using a pseudo model and variables changes¶
So far we have been running 3DEnVar-FGAT and 4DVar with the dynamical core model, which is a simplified version of the forecast model that does not have any physics. FV3-JEDI supports running with the full versions of GFS and GEOS but running those models is too expensive for a tutorial. When developing a new model interface for JEDI, running with the full forecast model may not always be immediately available. There are often complexities with build systems and interfacing, especially if the models are not designed to be driven by the data assimilation.
Alternatively it’s possible to use the so-called pseudo model, which just involves reading states from disk instead of running the forecast model. This separate executable mode would be more expensive in practice but it is useful for getting things running during development. OOPS provides an explicit “HofXNoModel.h” application, where a list of states is read in instead of reading the model for the \(h(x)\) calculation. Examples of this application are provided in the Ctests of the various models.
FV3-JEDI provides a pseudo model that can be used with the variational application. In this first part of the tutorial we will learn how to run the 4DVar application with the pseudo model. Begin by copying the 4DVar configuration from the earlier tutorial:
cd ~/jedi/tutorials/20201001_0000z cp Config/4dvar.yaml Config/4dvar_pseudo.yaml
If you added multiple outer loops to your 4DVar in the last practical you should revert to using a single outer loop. With the pseudo model it is not possible to run the forecast for the second outer loop in the same executable.
For the previous 4DVar run the forecast model part of the configuration looked like:
cost function: [...] # Forecast model model: name: FV3LM nml_file: Data/fv3files/input_geos_c25.nml trc_file: Data/fv3files/field_table nml_file_pert: Data/fv3files/inputpert_4dvar.nml lm_do_dyn: 1 lm_do_trb: 0 lm_do_mst: 0 tstep: PT1H model variables: [u,v,ua,va,t,delp,q,qi,ql,o3ppmv,phis,frocean,frlake, frseaice,vtype,stype,vfrac,sheleg,ts,soilt,soilm,u10m,v10m] [...]
To run with the pseudo model change this to:
cost function: [...] # Forecast model model: name: PSEUDO pseudo_type: geos datapath: Data/bkg filename_bkgd: geos.bkg.%yyyy%mm%dd_%hh%MM%ssz.nc4 filename_crtm: geos.crtmsrf.c25.nc4 run stage check: 1 tstep: PT1H model variables: [u,v,ua,va,t,delp,q,qi,ql,o3ppmv,phis, qls,qcn,cfcn,frocean,frland,varflt,ustar,bstar, zpbl,cm,ct,cq,kcbl,tsm,khl,khu,frlake,frseaice,vtype, stype,vfrac,sheleg,ts,soilt,soilm,u10m,v10m] [...]
Recall from the previous tutorials that when editing Yaml files the indent level is important. Use two spaces to indent directives that live within a particular section. Do not use tabs.
Note that the name of the model is now ‘PSEUDO’, telling the system to instantiate the pseudo model
object instead of the the FV3LM model. The only information the pseudo model really needs is the path to
some files to read. It interprets the date time templates and reads the appropriate file for that
time step. In the directory
Data/bkg you can see that files are available hourly, so the
time step is
tstep: PT1H. Note that the list of variables has increased. Now that we have
the full forecast model (through files) we have some additional potential variables for the trajectory of the
tangent linear and adjoint version of the model.
As it stands these additional variables would also need to be added to the background configuration so that when the background is passed to the model it would have the correct variables. But this could lead to potential inefficiencies or complexities. For example if the model needs variables not available from the background directly. Instead we can use a variable change between the background and the model. FV3-JEDI and other models employ variable changes extensively to move between different parts of the cost function, where different variables are required. The code below shows how to add the variable change between the background and model. The other parts of the configuration are included to show the indent level required for the variable change.
cost function: [...] # Background background: filetype: geos datapath: Data/bkg filename_bkgd: geos.bkg.20200930_210000z.nc4 filename_crtm: geos.crtmsrf.c25.nc4 psinfile: true state variables: *anvars # Variable change from background to model variable change: Analysis2Model filetype: geos datapath: Data/bkg filename_bkgd: geos.bkg.%yyyy%mm%dd_%hh%MM%ssz.nc4 filename_crtm: geos.crtmsrf.c25.nc4 # Forecast model model: name: PSEUDO [...]
Note that we can now drastically reduce the number of variables in the background configuration,
so they contain only the variables necessary to create the increment. The more extensive list of
variables is restricted only to the model, which can save memory. The
special characters in Yaml files that eliminate the need to specify the same information more than
once. Therefore the
anvars are just taken from where they are specified above.
Previously, when looking at diagnostics we examined \(h(x)\) for the background and analysis.
These statistics, including the
Jo/n quantity, cannot be examined for the analysis in this
case because the forecast is not run for the analysis, the pseudo model can only read the same set
of files. We would have to run another forecast for the full model in another application. You can
still look at the convergence and increment at the beginning of the window though.
Before running, be sure to update the names of the analysis and \(h(x)\) output so as not to overwrite the previous 4DVar runs. Now you can run the pseudo model 4DVar:
mpirun -np 6 $JEDIBUILD/bin/fv3jedi_var.x Config/4dvar_pseudo.yaml Logs/4dvar_pseudo.log
Step 3: Running with different resolution increment¶
In practice variational data assimilation is performed with the increment at a lower resolution. This is often called incremental variational data assimilation. For algorithms such as 4DVar, the minimization step can be quite expensive since operators like the model adjoint and the B matrix have to be applied a large number of times. By running the minimization at lower resolution these costs can be reduced. Similarly, producing ensembles of perturbed forecasts is expensive so they are often run at a lower resolution than the deterministic forecast. Developing an operational data assimilation system is all about a balancing resolution, numbers of observations and complexity in the various operators to produce the most accurate analysis given the time constraints to deliver the forecast. Having the ability to run with low resolution increments is a very effective way to deliver a better analysis.
So far we have run all of the assimilation integrations with 25 grid points along each dimension of the cube face. This is controlled in the geometry part of the configuration file:
cost function: [...] # Background/anaysis geometry # --------------------------- geometry: npx: &npx 25 npy: &npy 25 [...]
This geometry lies in the
cost function part of the configuration. Note that there is
another geometry section in the configuration which lies in the
variational part of the
configuration. This horizontal resolution there is given as:
# Inner loop(s) configuration variational: [...] iterations: [...] # Increment geometry geometry: npx: *npx npy: *npy [...]
Begin by creating a copy of the 3denvar_backup.yaml configuration file, that will be where we start from:
cp Config/3denvar_backup.yaml Config/3denvar_lowresinc.yaml
In order to run with an increment with a different resolution to the background the geometry in the
variational part of the configuration needs to be changed. For this testing we will use
13 grid points along each dimension of the cube. The new geometry will be:
# Inner loop(s) configuration variational: [...] iterations: [...] # Increment geometry geometry: trc_file: *trc akbk: *akbk layout: *layout io_layout: *io_layout npx: 13 npy: 13 npz: *npz ntiles: *ntiles fieldsets: - fieldset: Data/fieldsets/dynamics.yaml - fieldset: Data/fieldsets/ufo.yaml [...]
Only change the geometry in the
variational part of the configuration, and not in the
cost function part. We do not want to change the resolution of the background.
In practice the background and ensemble are typically already at different resolutions, the background being the resolution of the deterministic forecast and the ensemble typically some lower resolution so as to afford the multiple required forecasts. Here we lower the resolution of the ensemble artificially to demonstrate and learn about this capability of the system.
Right now the ensemble resolution is with the 25 grid points. FV3-JEDI comes with an
application for changing the resolution of states. There exists a configuration file called
Config/change_resolution_ensemble.yaml for changing the resolution of the ensemble members
that are valid at the beginning of the window. Before running the low resolution 3DEnVar we need to
call the application to lower the resolution of the ensemble:
mpirun -np 6 $JEDIBUILD/bin/fv3jedi_convertstate.x Config/change_resolution_ensemble.yaml
There should now be files like
Data/ens/*/geos.ens.c13.20201001_000000z.nc4 for each
member. Note that the output files have a slightly different name from before, to prevent them
being overwritten. This new name has to be taken into account in
Demonstrated for the first two members the configuration should look something like:
cost function: [...] # Background error covariance background error: [...] # Ensemble members members: - filetype: geos state variables: *anvars datapath: Data/ens/mem001 filename_bkgd: geos.ens.c13.20201001_000000z.nc4 psinfile: true - filetype: geos state variables: *anvars datapath: Data/ens/mem002 filename_bkgd: geos.ens.c13.202001001_000000z.nc4 psinfile: true [...]
Since the resolution of the ensemble has changed so must the localization model. This is achieved by
altering the geometry in the
localization_parameters_fixed.yaml configuration. To be safe
first make a copy of that file:
cp Config/localization_parameters_fixed.yaml Config/localization_parameters_fixed_c13.yaml
Now open this file and edit the
geometry part of the configuration by reducing the
nyp parameters from 25 to 13.
Also change the name of the output files that will contain the localization model so as not to overwrite the higher resolution model.
This can be done by modifying the
prefix directive as follows:
[...] # BUMP setup bump: prefix: Data/bump/locparam_c13 [...]
Now you can create this new localization model:
mpirun -np 6 $JEDIBUILD/bin/fv3jedi_parameters.x Config/localization_parameters_fixed_c13.yaml
Since the name of the files containing the localization model has changed the corresponding update
needs to happen in
Config/3denvar_lowresinc.yaml. In the
of the config look for the
prefix key and append it with c13 identically to how you did so in
Now you should be ready to run the low resolution 3DEnVar:
mpirun -np 6 $JEDIBUILD/bin/fv3jedi_var.x Config/3denvar_lowresinc.yaml Logs/3denvar_lowresinc.log
You may notice that this run is faster than the previous 3DEnVar run.
Step 4: Using localization length scales determined from the ensemble¶
The Config directory contains a configuration file for generating the localization model from the ensemble rather than using specified length scales. To invoke this method of generating the localization model enter:
mpirun -np 6 $JEDIBUILD/bin/fv3jedi_parameters.x Config/localization_parameters_dynamic.yaml
Note that this will take quite a bit longer to run than when generating a fixed scale localiztion
model. Once it completes additional files in the
Data/bump directory called
Data/bump/locparam_dynamic_00_nicas_* should appear. To use this new localization model it
is necessary to update the variational configuration file. First make a copy:
cp Config/3denvar_backup.yaml Config/3denvar_dynamicloc.yaml
Now you need to edit the
localization part of the configuration to use the newly generated
model. The configuration should be:
cost function: [...] # Background error covariance background error: [...] # Apply a localization operator localization: localization variables: *anvars localization method: BUMP bump: prefix: Data/bump/locparam_dynamic method: loc strategy: common load_nicas: 1 mpicom: 2 verbosity: main [...]
Note that the
io_key references can be removed now. These were used previously because variables
with different names would share a localization model. Remember to also change the names of the output files so they are not overwritten. Now try running this new case:
mpirun -np 6 $JEDIBUILD/bin/fv3jedi_var.x Config/3denvar_dynamicloc.yaml Logs/3denvar_dynamicloc.log
Try looking at some of the diagnostics for this run compared to the run with fixed localization length scales.