Onboard Real-Time Absolute Radiometric Calibration for Thermal Infrared Channels of Chinese Geostationary Meteorological Satellites
Bibliographic record
Abstract
Abstract Forty-one cloud-free images of Qinghai Lake (QHL) in China and the corresponding digital numbers (DNs) of FengYun-2C (FY-2C) at 0000, 0600, 1200, and 1800 UTC from 1 July to 30 September 2005 are analyzed. The corresponding surface water temperatures of QHL measured by the automated hydrometeorological buoy (AHMB) system and the atmospheric profiles over QHL from the National Centers for Environmental Prediction (NCEP) reanalysis data are inputted into the atmospheric transfer model MODTRAN3.7 to calculate the entrance pupil radiance and brightness temperatures for thermal infrared (TIR) channels of FY-2C. Then, the absolute radiometric calibration coefficients of FY-2C, which are used to calculate the equivalent blackbody (EBB) temperatures TEBB, are calculated by comparing the entrance pupil radiance and brightness temperatures with the corresponding DNs. In addition, the temperatures of onboard blackbody (OBB) TOBB, primary, secondary, refraction, and calibration mirrors on the multichannel scanning radiometer (MSR) of FY-2C are detected remotely. Based on the linear correlation between TEBB − TOBB and temperatures of various mirrors, the transform equations from TOBB to TEBB are developed. Finally, the onboard real-time absolute radiometric calibration for TIR channels of geostationary meteorological satellite FY-2C is implemented with an uncertainty of about 1.5 and 2.1 K for TIR 1 and TIR 2 of FY-2C, respectively.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".