A New Spatiotemporal Estimator to Downscale GRACE Gravity Models for Terrestrial and Groundwater Storage Variations Estimation
Bibliographic record
Abstract
This study proposes a new mathematical approach to downscale monthly terrestrial water storage anomalies (TWSA) from the Gravity Recovery and Climate Experiment (GRACE) and estimates groundwater storage anomalies (GWSA) at a daily temporal resolution and a spatial resolution of 0.25° × 0.25°, simultaneously. The method combines monthly 3° GRACE gravity models and daily 0.25° hydrological model outputs and their uncertainties in the spectral domain by minimizing the mean-square error (MSE) of their estimator to enhance the quality of both low and high frequency signals in the estimated TWSA and GWSA. The Global Land Data Assimilation System (GLDAS) was the hydrological model considered in this study. The estimator was tested over Alberta, Saskatchewan, and Manitoba (Canada), especially over the Province of Alberta, using data from 65 in-situ piezometric wells for 2003. Daily minimum and maximum GWS varied from 14 mm to 32 mm across the study area. A comparison of the estimated GWSA with the corresponding in-situ wells showed significant and consistent correlations in most cases, with r = 0.43–0.92 (mean r = 0.73). Correlations were >0.70 for approximately 70% of the wells, with root mean square errors <24 mm. These results provide evidence for using the proposed spectral combination estimator in downscaling GRACE data on a daily basis at a spatial scale of 0.25° × 0.25°.
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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 | 0.002 |
| 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) | 0.001 | 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".