The experiment setting is similar to Twin Experiment 2, but with a different truth (at the upstream location, CHC and CHG are non zero in the summer).
Results
At assimilation station
Time series:
RMSE (computed over 51 analyses cycles over 2015):
At validation stations
Time series (Location 5006A40):
RMSE (Location 5006A40):
Over all locations:
Discussions
- Are we satisfied with the error modelling (std of Observation & settings of noise statistics & which parameters (and tributaries) to include)? Discuss with NIER how they came up with this setting. Perhaps we can check the resulting error statistics against observed error statistics. We can also take a pragmatic approach: check if the filter performance is sensitive to the specified error statistics.
- EnKF works well in improving forecast accuracy at the assimilation station and all down stream stations, but not the upstream stations. (do we need to check other water quality variables as well? which ones are of important?)
- The RMSE of the deterministic run from the assimilation station to down stream is more or less uniform. Yet the EnKF impact gets bigger in the downstream direction. Why?
- The RMSE at the most downstream location is especially small, even the deterministic one, as if it is not really affected by the noise defined upstream. The model has apparently a different dynamic there. Why?
- Water temperature is not included in the noise model definition nor in the state definition of the filter. Yet it is affected as it has quite a significant RMSE. The filter has also a slightly positive effect downstream on water temperature. It is likely due to the inclusion of solar radiation in both the noise model and state definition of the filter.
- Can we say anything about optimality of the filter? Have we reached the maximum possible improvement? what can be done to improve the accuracy even more? (1) use a larger ensemble, (2) use more stations for assimilation, i.e. upstream (3) check if other algorithms with the same settings used here can produce more improvement
- What experiments to include in paper? e.g.:
- comparison DA algorithms
- comparison different noise settings
- comparison different assimilation settings (which locations do we assimilate)
- comparison nr. of ensembles (comparison EnKF-GS limited nr vs. ensembles with EnKF)
- discussion nr. of observation to enhance forecast using DA vs. improving internal EFDC parameter settings if Det systematically differs from observations?
