GOES Aerosol/Smoke Product (GASP) over North America: Comparisons to AERONET and MODIS observations
Bibliographic record
Abstract
The GOES Aerosol/Smoke Product (GASP) is a retrieval of the aerosol optical depth (AOD) using visible imagery. The product currently runs operationally at NOAA/NESDIS in near‐real time at 30 min intervals. This high temporal resolution is not possible with polar orbiting instruments which produce one daily image. This work evaluates the GASP AOD from the GOES‐12 Imager over North America at various temporal and spatial scales based on comparisons with AOD from the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS). We find a mean GASP/AERONET correlation of 0.79, rms difference of 0.13 and slope of 0.8, based on a statistical analysis at 10 northeastern U.S. and Canadian sites. The GASP AOD has a slight negative bias when the AOD is greater than 0.35 and a positive bias elsewhere. The absolute bias is less than 30% when the AOD is greater than 0.1. When the AOD is less than 0.15 we find poor correlation and biases greater than 30%. The GASP/AERONET statistics also indicate that GASP can be used to examine the seasonal and diurnal variability in the AOD over the eastern United States between 1215 and 2115 UTC. GASP/AERONET AOD correlations were generally less than 0.5 elsewhere in the continental United States. Comparisons between the MODIS and GASP AOD over the eastern United States in the summer of 2004 showed agreement within 20% and correlations greater than 0.7 under elevated AOD conditions. Simultaneous comparisons between GASP, MODIS, and AERONET AODs showed good agreement over the northeastern United States and Canada, with higher correlation and lower rms differences in the MODIS/AERONET comparisons than in the GASP/AERONET comparisons.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".