DART quality control field
DART has a quality control (QC) field in the obs_seq.final file to report on the status of the assimilation of the variable. The most common reason for exploring the DART QC value is to help determine if the observation was assimilated (or evaluated) - or if the observation was rejected or …
To learn more about how to intepret the QC field as well as other values in an observation sequence file, see Detailed structure of an obs_seq file. The ‘DART QC’ field is usually the second of the 2 “quality control” copies.
A list of all the DART QC values can be found in the QC table in MODULE quality_control_mod.
If the DART QC values are 4, the forward operators have failed. Look at the model_interpolate() routine in your model_mod.f90 file, or the forward operator code in observations/forward_operators/obs_def_xxx_mod.f90 for your observation type. A successful forward operator must return a valid obs_val and an istatus = 0. If the forward operator code returns different istatus values for different error types, you can set &filter_nml::output_forward_op_errors = .true. and rerun filter to see exactly what error istatus codes are being set. See MODULE filter_mod for more information on how to use the ‘output_forward_op_errors’ option. Negative istatus values are reserved for the system, istatus = 0 is success, and any positive value indicates a failed forward operator. The code is free to use different positive values to signal different types of errors.
If the DART QC values are 5, those observation types were intentionally ignored because they were not listed in the &obs_kind_nml namelist, in the ‘assimilate_these_obs_types’ stringlist.
If the DART QC values are 6, the data quality control that came with the original observation data indicates this is a bad quality observation and it was skipped for this reason.
If the DART QC values are 7, the observation value is too far away from the ensemble mean. Set &filter_nml::outlier_threshold = -1 to ignore this for now and rerun. In general, this is not the optimal strategy as the number of observations inconsistent with the ensemble is a very powerful indicator of the success or failure of the assimilation.
If the DART QC values are 8, it was not possible to convert the observation to the required vertical coordinate system.
If the prior and posterior values in the
obs_seq.final are not -888888.0 but
are identical, your obs are being assimilated but are having no impact.
The most common reasons assimilated obs have no impact on the model state include:
Zero spread in ensemble members Your initial ensemble members must have different values for each state item. If all members have identical values, the observations cannot make a change. To diagnose this condition, look at the prior ensemble spread. This is either in
preassim_sd.nc, depending on your model. If all the values are 0, this is your problem. One way to generate an ensemble with some spread is to set &filter_nml::perturb_from_single_instance = .false., (which will still require a single filter initial condition file) but then the filter code will add random gaussian perturbations to each state vector item to generate an initial ensemble with spread. The magnitude of the gaussian noise added is controlled by the &filter_nml::perturbation_amplitude. It is also possible to write your own perturbation routine in your
Cutoff value too small If the localization radius is too small, the observation may not be ‘close enough’ to the model grid to be able to impact the model. Check the localization radius (&assim_tools_nml::cutoff). Set it to a very large number (e.g. 100000) and rerun. If there is now an impact, the cutoff was restricting the items in the state vector so your obs had no impact before. Cutoff values are dependent on the location type being used. It is specified in radians for the threed_sphere locations module (what most large models use), or in simple distance (along a unit circle) if using a low order model (lorenz, ikeda, etc).
Obs error values too large (less likely) If the observation error is very large, it will have no impact on the model state. This is less likely a cause than other possibilities.
No correlation (unlikely) If there is no correlation between the distribution of the forward observation values and the state vector values, the increments will be very tiny. However there are generally still tiny increments applied, so this is also a low likelyhood case.
Errors in forward operator location computations, or get_close_obs() If there is an error in the
model_mod.f90code in either get_state_meta_data(), model_interpolate(), or the vertical conversion code in get_close_obs(), it is possible for the forward operators to appear to be working correctly, but the distances computed for the separation between the obs and the state vector values can be incorrect. The most frequent problem is that the wrong locations are being passed back from get_state_meta_data(). This can result in the increments being applied in the wrong locations or not at all. This is usually one of the things to test carefully when developing a new model interface, and usually why we recommend starting with a single observation at a known location.
Incorrect vertical conversion If the model is using 3d coordinates and needs the capability to convert between pressure, height, and/or model level, the conversion may be incorrect. The state vector locations can appear to be too high or too low to be impacted by an observation. Some models have a height limit built into their model_mod code to avoid trying to assimilate observations at the model top. The observations cannot make meaningful changes to the model state there and trying to assimilate them can lead to problems with the inflation. If the code in the model_mod is excluding observations incorrectly, or you are testing with observations at the model top, this can result in no impact on the model state.