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Over all locations:
Discussions
- Are we satisfied with the error modelling? 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.
- 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.