Trends and uncertainties in thermal calibration of AVHRR radiometers onboard NOAA‐9 to NOAA‐16
Bibliographic record
Abstract
Satellite measurements from the infrared (IR) channels of the Advanced Very High Resolution Radiometer (AVHRR)/NOAA have been used to derive many important atmospheric, cloud, and surface parameters for weather prediction, climate modelling, and a variety of environmental studies. Calibration accuracy of the satellite data directly affects accuracies of the derived parameters. So far, very limited attention has been given to the calibration uncertainties of the IR channels. In this study, we analyzed the calibration data of AVHRR radiometers onboard polar orbiting satellites NOAA‐9 to NOAA‐16. We utilized Global Area Coverage (GAC) data, approximately one orbit per month throughout the lifetime of the instruments, available from the NOAA Satellite Active Archive (SAA). AVHRR IR channels 3B, 4, and 5 are calibrated in‐flight. Calibration coefficients are derived from measurements of radiance emitted from an internal calibration target (ICT) and deep‐space (SP). The overall budget of uncertainties has been evaluated using an in‐flight calibration system that includes four thermal platinum resistance thermometers (PRTs) to monitor the ICT temperature. The measurement noise (NEΔT) was found to vary from 0.03 K to 0.3 K at 300 K depending on the channel and radiometer, and it increases significantly as temperature decreases. Systematic degradation of the radiometric sensitivity of the IR detectors was observed during the lifetime of a radiometer, although the annual rate of degradation is rather small (typically below 1% per year). A significant correlation between the calibration gain and temperature of a radiometer is often observed. The degradation of a sensor's radiometric sensitivity reduces the radiometric resolution of the AVHRR measurements and expands the upper limit of the measured brightness temperature. PRT measurements are subject to significant orbital variation (up to 7 K) and inconsistency for some AVHRR radiometers. The inconsistency was especially large for the AVHRR onboard NOAA‐12 (up to 4 K) and NOAA‐14 (up to 3 K), but it is less than 0.5 K for NOAA‐15 and ‐16. The inconsistency may signify the presence of a thermal gradient across the ICT. Some systematic differences between PRT measurements may also indicate inaccurate characterization of the PRT sensors, for example for AVHRR/NOAA‐11 and ‐14. The impact of the varying thermal state of the AVHRR environment on the accuracy of AVHRR in‐flight thermal calibration was assessed. We found this impact to be significant (up to 0.5 K and more), and proposed a physical model to explain it. We recommend this model for AVHRR operational in‐flight calibration, especially during solar radiative contamination events. Estimates of the PRT thermal response time constant were derived and found to vary between 0.5 and 1.5 min among AVHRR radiometers. Overall, we found somewhat higher uncertainties in AVHRR thermal measurements than were assumed previously.
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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 | 0.002 | 0.007 |
| 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) | 0.000 | 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".