Selecting a first‐guess sea surface temperature field as input to forward radiative transfer models
Bibliographic record
Abstract
Advanced Clear‐Sky Processor for Oceans (ACSPO), developed at National Environmental Satellite, Data, and Information Service, reports observed top‐of‐atmosphere clear‐sky brightness temperatures (BT) in AVHRR channels 3B (3.7), 4 (11), and 5 (12μm) along with the sea surface temperatures (SST) retrieved from these BTs as a level 2 (L2) product. Additionally, ACSPO provides the corresponding BTs simulated with Community Radiative Transfer Model (CRTM), using Reynolds daily Level 4 (L4) optimum interpolation SST (OISST) and NCEP‐GFS profiles as inputs. Accuracy of simulated BTs is critical for ACSPO functionalities, including detecting clouds, retrieving physical SSTs, monitoring sensor performance, and validating CRTM. This paper tests 11 gap‐free gridded L4 SSTs for their potential use as first‐guess fields in ACSPO to improve accuracy of simulated BTs. As a first step toward the objective, this study checks for consistency between various L4 products and ACSPO L2 SSTs. This SST consistency was shown earlier to be representative of, and equivalent to, the consistency between measured and simulated BTs, thus avoiding expensive CRTM calculations. The metrics employed in L4 comparisons include the global spatial L4‐L2 SST biases and variances and their temporal stability. Also, the effect of L4 fields on the corresponding satellite‐to‐satellite consistency (calculated as L2‐L2 double differences) is examined. Several L4 products, including the GHRSST Multi‐Product Ensemble and Canadian Meteorological Centre analysis (CMC‐0.2°), show better consistency with ACSPO L2 SST and will be explored in the future versions of ACSPO.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".