TICFIRE: a far infrared payload to monitor the evolution of thin ice clouds
Bibliographic record
Abstract
The TICFIRE mission concept developed with the support of the Canadian Space Agency aims: 1) to improve measurements of water-vapour concentration in the low limit, where cold regions are most sensitive and 2) to determine the contribution of Thin Ice Clouds (TIC) to the energy balance and the role of their microphysical properties on atmospheric cooling. TICFIRE is a process-oriented mission on a micro-satellite platform dedicated to observe key parameters of TIC forming in the cold regions of the Poles and globally, in the upper troposphere. It locates cloud top profiles at the limb and measures at nadir the corresponding upwelling radiance of the atmosphere directly in the thermal window and in the Far Infrared (FIR) spectrum over cold geographical regions, precisely where most of the atmospheric thermal cooling takes place. Due to technological limitations, the FIR spectrum (17 to 50 μm) is not regularly monitored by conventional sensors despite its major importance. This deficiency in key data also impacts operational weather forecasting. TICFIRE will provide on a global scale a needed contribution in calibrated radiance assimilation near the IR maximum emission to improve weather forecast. TICFIRE is therefore a science-driven mission with a strong operational component. The TICFIRE payload consists of two instruments; the main one being a Nadir-looking multiband radiometer based on uncooled microbolometer technology and covering a large spectral range from 7.9 μm to 50 μm. The secondary one is an imager that performs Limb measurements and provides cloud vertical structure information. This paper presents the key payload requirements, the conceptual design, and the estimated performance of the TICFIRE payload. Current technology developments in support to the mission are also presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".