Topography-correlated atmospheric signal mitigation for InSAR applications in the Tibetan plateau based on global atmospheric models
Bibliographic record
Abstract
Atmospheric heterogeneity mainly exposes itself as tropospheric-phase delay in Satellite Interferometric Synthetic Aperture radar (InSAR) observations, which smears or even overshadows the deformation component of InSAR measurements. In this study, we estimated the performance of four Global Atmospheric Models (GAMs), i.e. European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), ERA-Interim (ERA-I), Modern-Era Retrospective analysis for Research and Applications v2 (MERRA2) and Generic Atmospheric Correction Online Service for InSAR (GACOS), for tropospheric-phase delay reduction in InSAR applications in the Tibetan plateau, of which ERA5 is the latest GAM released by ECMWF. We demonstrated the effectiveness of Atmospheric Phase Screen (APS) correction using the four GAMs for more than 700 Sentinel-1 Terrain Observation by Progressive Scans interferograms covering two study areas in the southern (R1) and northwest margins (R2) of the Tibetan plateau. Topography-correlated signals have been widely observed in these interferograms, which are most likely due to the APS effects. We calculated the Standard Deviations (SD) and Pearson’s Correlation Coefficients (r) between InSAR Line of Sight measurements and topography before and after applying APS correction. The results show that the SDs of non-deformation areas from the GAMs decrease to approximately 4 mm from around 10 mm and 12 mm originally on average for R1 and R2, respectively, and the r after the APS correction are reduced below 0.4 from around 0.8 for the selected interferometric pairs. In addition, as the newly released GAM, ERA5 has similar performance with GACOS products and outperforms other models generally. This suggests that GAMs, particularly ERA5, have great potential in the APS correction for InSAR applications in the Tibetan plateau.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".