The High-Altitude Aerosols, Water Vapor, and Clouds Mission: Concept, Scientific Objectives, and Data Products
Bibliographic record
Abstract
Abstract The High-Altitude Aerosols, Water Vapor, and Clouds (HAWC) mission is an observing system, with a planned launch around 2031, that is being developed by the Canadian Space Agency to provide collocated global measurements of aerosols, water vapor, and thin ice clouds in the upper troposphere and lower stratosphere with vertical coverage that extends into the troposphere in the polar regions. The mission is the Canadian contribution to NASA’s Atmospheric Observing System (AOS), a satellite constellation will include multiple instruments to monitor aerosols, clouds, and precipitation as part of Earth System Observatory (ESO). The HAWC mission includes three innovative Canadian instruments: the Aerosol Limb Imager (ALI), the Thin Ice Clouds and Far Infrared Emissions (TICFIRE) instrument, and the Spatial Heterodyne Observations of Water (SHOW) instrument. ALI and SHOW will provide limb profiles of aerosol and water vapor with high spatial resolution (vertical and along track) and high sensitivity to fine aerosols and dry conditions. TICFIRE will provide nadir measurements of infrared radiation, thin ice cloud content, and cloud microphysical properties. These coordinated measurements will help build a more comprehensive picture of high-altitude aerosols, clouds, and water vapor in the atmosphere. In this paper, we present the HAWC concept and discuss the primary science objectives and requirements of the mission. The instrument payloads and data products are introduced, and synergies between HAWC and other AOS instruments are identified. Significance Statement Improving our understanding of Earth’s climate system requires reducing uncertainties in key climate variables and processes. The High-Altitude Aerosols, Water Vapor, and Clouds (HAWC) observing system will provide the first ever collocated satellite observations of aerosols, water vapor, thin cloud microphysical properties, and longwave radiation in the upper troposphere and lower stratosphere with coverage that extends into the middle stratosphere and includes lower-tropospheric altitudes at the poles. The observing system will provide a unique perspective that will be used to reduce uncertainties in these key climate variables, improve physical process understanding, and facilitate model validation in one of the most poorly understood regions of the atmosphere.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".