Assessment of the impacts on data assimilation performance caused by spatio-temporal gaps in satellite soil moisture data
Bibliographic record
Abstract
Previous studies have shown that assimilating satellite soil moisture data in land surface models can improve the estimations of soil moisture. One of the limitations of these satellite soil moisture products is that there are often spatial gaps (in the horizontal direction) in data availability over certain areas due to issues such as dense vegetation or hilly terrain. These products are also limited in the vertical direction because, for the microwave-based products for example, the microwave radiation captured by the satellite sensors to estimate soil moisture is usually representative of a very thin top layer of soil (up to about 5 cm). Lastly, data over a specific watershed may not be available every day (i.e. temporal gaps) because of the orbital configuration of the satellite in question. From the existing literature, it is not clear what the benefits will be for soil moisture modeling, if these spatio-temporal gaps in satellite soil moisture datasets could somehow be minimized or eliminated. To answer this question, a synthetic assimilation study was carried out on the Noah-MP land surface model within the WRF-Hydro modeling system. The study was conducted with ERA5 forcing data on the Susquehanna River Basin and the Ensemble Kalman Filter was the chosen assimilation algorithm. Multiple scenarios were explored in which spatio-temporal gaps were introduced in the synthetic observations by mimicking the actual spatio-temporal gaps that are present in the SMAP soil moisture product. Results indicate that the model’s ability to accurately simulate soil moisture is much lower when assimilated observations have spatio-temporal gaps, compared to model simulations where there are no gaps in the assimilated observations. However, it was found that this lower model accuracy can be improved if the model grids with missing observations are updated based on the covariance between the soil moisture of those grids and their surrounding grids.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".