The Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST): instrument design and signal processing
Bibliographic record
Abstract
Abstract. The Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) is a Fourier-transform spectrometer designed, fabricated, and sold by LR Tech Inc., which operates in the thermal infrared. When attached to its automated radiometric calibration module, it functions as an infrared spectroradiometer (IRS) that passively measures the absolute spectral radiance within a 46 mrad full field of view and over the 525 to 3300 cm−1 (3 to 19 µm) spectral range. For atmospheric studies, the ASSIST IRS is integrated into a mobile enclosure enabling autonomous and reliable operation across a range of environmental conditions. It is typically configured for downwelling radiance measurements (zenith view) at 0.5 cm−1 bin spacing, 0.6 cm−1 resolution, and 4 min−1 sampling rate, closely replicating the behavior of the Atmospheric Emitted Radiance Interferometer (AERI, in rapid-sampling mode), a similar but older IRS. Atmospheric variables affecting the shape of the downwelling thermal infrared radiance spectrum at ground level can be retrieved from the ASSIST high-resolution measurements using dedicated inversion algorithms. This includes the properties of some aerosols and simple clouds, the mixing ratios of trace gases, and the vertical distribution of temperature and water vapor (thermodynamic profile) in the lower troposphere above the instrument. Due to the form of the radiative transfer equation, thermodynamic profiles can only be retrieved with low to moderate vertical resolution but with sufficient accuracy and temporal resolution to help fill the current boundary layer observational gap. This paper provides a detailed description of the ASSIST design and near-real-time processing algorithm producing the calibrated radiance spectra that are useful in a variety of applications.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".