Spectral combination theory for downscaling GRACE gravity models and estimating terrestrial and groundwater storage variations
Bibliographic record
Abstract
<!--!introduction!--> The Gravity field and Climate Experiment (GRACE) satellite mission and its follow-on (GRACE-FO) are designed for modelling the temporal changes of gravity field of the Earth. So far, these variations are presented on monthly basis, which are not enough for the hydrological applications. In order to obtain temporal variations with higher temporal and spatial resolutions, extra information, like terrestrial Global Land Data Assimilation System (GLDAS) hydrological models are needed. Therefore, an optimal method for enhancing the long wavelength portions of the GLDAS models using the GRACE models is needed. Here. a new method for estimating the uncertainties of the GLDAS models is presented and later the spectral combination theory is applied to optimally weigh the spectra of the GLDAS and GRACE models. In other words, an estimator is provided to combine low frequencies of the monthly GRACE models and convert them to the daily terrestrial and groundwater storage anomalies using the GLDAS daily models. The developed method is applied over Alberta for visualisation and validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".