MATLAB tools for the post-processing of GRACE temporal gravity field solutions
Bibliographic record
Abstract
We present a collection of MATLAB tools for the post-processing of temporal gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. GRACE final products are in the form of monthly sets of spherical harmonic coefficients and have been extensively used by the scientific community to study the land surface mass redistribution that is predominantly due to ice melting, glacial isostatic adjustment, seismic activity and hydrological phenomena. Since the launch of GRACE satellites, a substantial effort has been made to develop processing strategies and improve the surface mass change estimates. The MATAB software presented in this work is developed and used by the Gravity and Earth Observation group at the department of Geomatics Engineering, University of Calgary. A variety of techniques and tools for the processing of GRACE data are implemented, tested and analyzed. Some of the software capabilities are: filtering of GRACE data using decorrelation and smoothing techniques, conversion of gravity changes into mass changes on the Earth’s spherical, ellipsoidal and topographical surface, implementation of forward modeling techniques for the estimation and removal of long-term trends due to ice mass melting, basin-specific spatial averaging in the spatial and spectral domain, time series smoothing and decomposition techniques, and data visualization. All tools use different levels of parameterization in order to assist both expert users and non-specialists. Such a software makes the comparison between different GRACE processing methods and parameters used easier, leading to optimal strategies for the estimation of surface mass changes and to the standardization of GRACE data post-processing. It could also facilitate the use of GRACE data to non-geodesists.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.224 | 0.117 |
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".