Analysis of Gravity Recovery and Climate Experiment time‐variable mass redistribution signals over North America by means of principal component analysis
Bibliographic record
Abstract
Four years of data provided by the NASA/German Aerospace Center Gravity Recovery and Climate Experiment (GRACE) satellite mission are analyzed over North America using principal component analysis (PCA). Three hydrology models [Global Land Data Assimilation System (GLDAS), Climate Prediction Center (CPC), and Land Dynamics (LaD)] are used to analyze the water mass changes over the same area and time period. The GRACE‐observed and the hydrology models mass changes are compared spatially and temporally, and good agreement is observed. Two signal modes are found to represent more than 65% of the GRACE‐observed mass variability. The first mode represents mainly mass changes related to the snow accumulation and melting and has maximum amplitude in the western Cordillera and Québec‐Labrador regions. The second mode comprises long‐term positive mass changes in central and eastern Canada and negative mass changes in Alaska. In addition, two more spatiotemporal patterns that explain 14% of the GRACE‐observed mass variability are extracted and studied, but no definite relation to hydrology is established. While the GLDAS model agrees very well with the GRACE observations, it is found that the CPC model also provides useful information for validating the GRACE‐observed mass changes in North America. On the basis of the results of this study, we can state that principal component analysis is a useful technique for extracting and validating regional hydrology signals from GRACE gravity field data. The main advantage of PCA is the capability to extract interannual and nonperiodic mass changes in addition to long‐term and periodic variations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".