Vertical distribution of the concentrations of multiple aerosol types derived from the multiwavelength spaceborne lidar of the future Atmosphere Observing System 
Bibliographic record
Abstract
Atmospheric aerosols play a key role in influencing the Earth's radiation budget. Still, their impacts remain poorly quantified due to the complex mechanisms involved in the interaction between aerosols and clouds. Indeed, aerosols act as ice-nucleating particles and cloud condensation nuclei, significantly altering the formation of precipitation and clouds. These environmental impacts of aerosols are strongly dependent on their composition, origin, and types. Moreover, high concentrations of aerosols in the atmosphere degrade air quality, posing large health risks which are also highly dependent on their composition (which is related to their types).In recent decades, a space-borne lidar called the cloud–aerosol lidar with orthogonal polarization (CALIOP) onboard cloud–aerosol lidar and infrared pathfinder satellite observation (CALIPSO) satellite was providing aerosol vertical distribution from space using two wavelengths, namely 532 nm which provides attenuated backscatter and depolarization profiles and 1064 nm which deliver attenuated backscatter profile. Combining its 3 channels, CALIOP measurements provide a purely qualitative aerosol typing detection, indicating the presence or absence of a single aerosol type at each altitude of the atmosphere. To provide a more detailed and quantitative characterization of aerosols and to gain new insights into the interactions between aerosols, clouds, convective processes and precipitation, the upcoming mission called Atmosphere Observing System (AOS), which includes contributions from the space agencies NASA (United States), CNES (France), ASI (Italy), JAXA (Japan) and CSA (Canada) is currently in preparation for launching in a horizon near 2030. AOS payload will include an advanced high-energy 3-wavelength lidar with Raman capabilities during nighttime, called CALIGOLA.In our present work, we examine the potential of CALIGOLA lidar during the daytime (three wavelength attenuated backscatter and depolarization measurements) and nighttime (one additional Raman channel in the UV which is suitable for nighttime measurements) flying in a polar orbit. By utilizing our newly developed retrieval approach, we quantitatively discriminate the concentration vertical profiles of five distinct aerosol types, such as smoke, continental, oceanic, dust and urban polluted. The development and first implementation of the method were performed using the pseudo-reality simulations obtained from the chemistry-transport model called Modèle de Chimie Atmosphérique de Grande Echelle (MOCAGE). In addition, the first tests of our innovative retrieval approach are planned using real lidar measurements from the High Spectral Resolution Lidar-2 (HSRL-2) airborne lidar from NASA Langley Research Center (LaRC).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".