Comparing Assimilation of Synthetic Soil Moisture Versus C‐Band Backscatter for Hyper‐Resolution Land Surface Modeling
Bibliographic record
Abstract
Abstract This paper presents the assimilation of synthetic surface soil moisture retrievals and C‐band backscatter signal in a 100‐meter resolution version of the Canadian Land Data Assimilation System (CaLDAS) on footprint scale soil moisture with a time interval of three hours. The synthetic surface soil moisture map was generated by extrapolating a regression relationship between in situ measurements and open loop land surface model outputs based on the soil texture of a given pixel. The surface roughness was inverted from RADARSAT‐2 imagery using a modified Integral Equation Model (IEM) model. Three hourly synthetic backscatter maps were created from this surface roughness and the synthetic soil moisture. The Ensemble Kalman filter (EnKF) with bias correction was applied to mitigate the impact of nonlinear errors introduced by multi‐sourced perturbations. Both time series and spatial maps were examined in the evaluation of the assimilation experiments. Results show that the assimilation of backscatter is as effective as assimilating soil moisture retrievals although the later has slightly better temporal statistics. Compared to the open loop, both approaches improved the analysis of surface and root zone soil moisture with significantly lower bias. For the later, the open loop average bias was reduced from −1.25 vol/vol to 0.59 vol/vol by backscatter assimilation and to 0.41 vol/vol by soil moisture assimilation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".