Operational calibration of the Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared channels
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Bibliographic record
Abstract
AbstractThe Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared channels must be calibrated after launch to maintain the accuracy of data derived from these channels for quantitative utilizations. The postlaunch calibration of these channels can only be carried out vicariously. The National Oceanic and Atmospheric Administration (NOAA) – National Environmental Satellite, Data, an Information Service (NESDIS) has been using the Libyan Desert as reference for operational calibration of AVHRR visible and near-infrared channels since 1995. A previous algorithm was successful correcting for the long-term instrument degradation in recalibration but had difficulty updating instrument calibration in near-real-time operation. This paper describes the operational calibration algorithm implemented since 2003, which overcomes the existing shortcomings by reducing target contamination and accounting for the effects of target bidirectional reflectance distribution function. Application of the algorithm shortens the latency of postlaunch calibration from 3 to 4 years for NOAA-14 and NOAA-16 to less than 2 years for NOAA-17 and to a few months for later satellites. Compared with the previous algorithm, the current algorithm enhances the calibration precision from 1.7% to 0.9% for channel 1.Les bandes du visible et du proche infrarouge d'AVHRR (« Advanced Very High Resolution Radiometer ») doivent être étalonnées après lancement pour assurer la précision des données dérivées de ces bandes pour des utilisations quantitatives. L'étalonnage post-lancement de ces bandes ne peut se faire que de façon vicariante. NOAA–NESDIS (« National Oceanic and Atmospheric Administration – National Environmental Satellite, Data, and Information Service ») utilise depuis 1995 le désert de Libye comme site de référence pour l'étalonnage opérationnel des bandes du visible et du proche infrarouge d'AVHRR. Un algorithme précédent a permis de corriger la dégradation à long terme de l'instrument au niveau du réétalonnage, mais ce dernier a connu des difficultés dans la mise à jour de l'étalonnage de l'instrument dans le contexte des opérations en temps quasi réel. Dans cet article, on décrit l'algorithme d'étalonnage opérationnel implémenté depuis 2003 qui pallie cette lacune en réduisant la contamination de la cible et en tenant compte des effets de la fonction de distribution de la réflectance bidirectionnelle de la cible. L'application de l'algorithme diminue la latence de l'étalonnage post-lancement de 3 à 4 ans dans le cas de NOAA-14 et NOAA-16 à moins de deux ans pour NOAA-17 et à quelques mois pour les satellites suivants. Comparativement à l'algorithme précédent, l'algorithme actuel améliore la précision de l'étalonnage de 1,7 % à 0,9 % pour la bande 1.[Traduit par la Rédaction] AcknowledgementsNOAA's AVHRR operational calibration was developed from the early work of C.R.N. Rao and associates, with contributions from N. Zhang, F. Sun, and F. Yu. The authors are grateful for the funding support from NOAA–NESDIS Product System Development and Implementation (PSDI) program, the moral support from M. Weinreb, and the helpful discussions with R. Galvin and J. Bobilya of ITT Industries. The contents of this paper are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NOAA or the US Government.Notes2 The requirement that the input signals be controlled is desirable but not always practical on orbit; however, that does not affect the use of the CEOS definition in this paper.3 This is not to be confused with the "relative calibration" that is used to account for variation among detectors in some instrument to limit banding and striping. AVHRR employs a single detector for each channel and therefore has no need to normalize among detectors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it