The spatial scale of model errors and assimilated retrievals in a terrestrial water storage assimilation system
Bibliographic record
Abstract
Synthetic satellite observations (or retrievals) of terrestrial water storage (TWS) in the Mackenzie River basin located in northwestern Canada were assimilated into the Catchment land surface model to evaluate the impact (i) assimilating TWS retrievals at subbasin (∼105 km2) or basin (∼106 km2) scales and (ii) incorrectly specifying the model error correlation length that is used for the perturbation of model forcing and prognostic variables in the ensemble‐based assimilation system. Specifically, a total of 16 experiments were conducted over a 9 year study period using different combinations of the spatial scale of the assimilated TWS retrievals and the horizontal model error correlation length. In general, assimilation of the TWS retrievals at the subbasin scale (∼2.7 × 105 km2 on average) yielded the best agreement relative to the synthetic truth. Greater improvement in TWS and snow water equivalent, in general, was witnessed as the (designed) horizontal model error correlation length increased. Conversely, subsurface soil water, evaporation, and runoff estimates typically improved (or remained unchanged) as the horizontal model error correlation length decreased. As the scale of the assimilated TWS retrieval decreased, more mass was effectively transferred from snow water equivalent into the subsurface, thereby dampening the hydrologic runoff response in the study area and correcting for improper model physics related to the runoff routing scheme. In general, TWS retrievals should be assimilated at the smallest spatial scale for which the observation errors can be considered uncorrelated while the specification of the horizontal error correlation length scale is of secondary importance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".