Overview of French efforts for the innovative characterisation of aerosols and cloud interactions with the future Atmosphere Observing System
Bibliographic record
Abstract
Aerosols and clouds play a major role in the Earth Climate systems, while the quantification and clear understanding of their variabilities, interactions and feedbacks remain a great challenge. In particular, aerosols strongly impact the energy budget by direct modification of solar and infrared radiation, alteration of cloud properties and their formation processes as well as the thermodynamic properties of the atmosphere. Aerosols are also the most harmful air pollutant, being responsible of several millions of premature deaths worldwide each year. Even though diverse observation and modelling approaches of aerosols exist, numerous unknowns remain concerning the chemical and physical mechanisms that affect them, their vertical redistribution in the atmosphere, the quantification of their environmental impacts and their interactions with clouds and convective processes.In order to tackle these major environmental issues at global scale, a new spaceborne Atmosphere Observing System (AOS) has been conceived as an international cooperation between NASA from USA, CNES from France, JAXA from Japan, CSA from Canada and ASI from Italy. This mission is built as a constellation of several satellites following two orbits, a polar orbit with global coverage in the continuity of the A-Train constellation and an inclined designed to document the diurnal variation of convection in the Tropics and mid-latitudes. They satellites will carry new generation active and passive instruments for sounding aerosols, clouds, convection, and precipitation, including an advanced multiwavelength lidar in tandem with a multi-angular polarimeter, whose launching period is planned for 2030.In the current presentation, we will provide an overview of French efforts for the innovative characterization of aerosols and its interactions with clouds for preparing the scientific exploitation of AOS. They gather relevant contributions from 8 French scientific laboratories: LISA, LOA, LATMOS, CNRM, LAERO, LACy, CERI EE and LSCE and a French industrial partner: GRASP-SAS. These efforts are threefold: (i) the development of innovative French aerosol satellite products based on AOS observations, (ii) suborbital measurements for feeding both the aerosol products and conceiving a synergetic exploitation with AOS and (iii) synergism with chemistry-transport models. The AOS aerosol observations will provide a new quantification of the vertical profile of aerosol concentration simultaneously for different particle types and chemical species. This information will be derived from lidar only and the synergism of lidar and polarimeter measurements using a so-called GRASP retrieval approach. Additional products aim the quantification of cloud condensation nuclei for studying aerosols/cloud interactions. The suborbital contribution will characterize aerosol optical, microphysical, and chemical properties from airborne, ground-based from several French sites and laboratory instrumentation. While documenting aerosols properties for different aerosol types and species, they provide a scientific framework for studying complex interactions such as the impact of aerosols on convective activity in specific regions. This is the case of the BACCOPA French field campaign aiming the studying of the impact of biomass burning aerosols emitted from Central Africa on convective activity. Finally, synergetic approaches with chemistry transport-models aim the development of data assimilation methods of AOS measurements and the use of these last ones for evaluating their numerical simulations.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".