A Deterministic Method for Profile Retrievals From Hyperspectral Satellite Measurements
Bibliographic record
Abstract
Different aspects of the operational constraints of remote sensing inverse problems are thoroughly investigated by simulation studies, using a deterministic method, namely regularized total least squares (RTLS). For demonstration purposes, water vapor profiles retrievals from simulated Suomi NPP Cross-track Infrared Souder (CrIS) hyperspectral measurements are considered. Synthetic CrIS radiances are generated using a line-by-line radiative transfer model (GENSPECT) with ~424 realistic radiosonde profiles and US 1976 standard atmosphere as inputs. These results are also compared with those from a prevalent stochastic method. Our findings show that the stochastic method, even with additional deterministic constraints (truncated singular value decomposition) applied on top of it, is often unable to produce useful retrieval results, i.e., posterior error is more than the a priori error. In contrast, RTLS is able to produce deterministically unique results according to the available information content in the measurements, which could result in a paradigm shift in operational satellite inversion.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".