Bibliographic record
Abstract
Abstract. A retrieval algorithm of integrated water vapor content above cloud, using shortwave infrared observations, is developed and evaluated through idealized and realistic atmospheric profiles. Water vapor plays a crucial role in cloud formation and development, particularly those resulting from convective processes. The resulting convective cloud locally influences the spatio-temporal variability of atmospheric water vapor content, through exchanges between cloud and its immediate environment. Therefore, a better understanding of the water vapor content above and around clouds is necessary to improve our comprehension of interactions between water vapor and cloud to better constrain Large-Eddy Simulations (LES) and numerical weather forecasting models. The developed algorithm is part of the Cluster for Cloud evolution, ClImatE and Lightning, C3IEL space mission project. This mission, scheduled for early 2028, aims to enhance our knowledge of the 3D convective cloud development velocities, the electrical activity associated with convective systems, and the water vapor content above and around the cloud. The retrieval algorithm presented in this study is achieved through a Bayesian probabilistic approach, the optimal estimation method. The atmosphere is assumed to be composed of homogeneous plane-parallel layers, and synthetic radiance datasets were generated to test the developed retrieval algorithm. The feasibility of retrieving the integrated water vapor content above the cloud over the ocean from SWIR radiances is shown to have, under idealized vertically homogeneous cloud profiles, absolute errors less than 2 kg.m−2 for optically thick clouds or when the integrated water vapor content is below 20 kg.m−2 and less than 1 kg.m−2 for very thick clouds with an optical thickness exceeding 150. Tests using realistic water vapor and cloud extinction profiles that present non homogeneous vertical distributions show that integrated water vapor content above water type clouds could be retrieved with a Root-Mean-Square Error (RMSE) related to cloud vertical penetration of approximately less than 1 kg.m−2 except for optically thin and low-level clouds (cloud optical thickness less than 50 and cloud top height less than 2 km). For very low water vapor content encountered in the presence of high deep convective clouds, the retrieval algorithm tends to systematically overestimate the retrieved water vapor content due to an overestimation of the cloud extinction profile in the upper part of the cloud in the inversion model.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.008 |
| Insufficient payload (model declined to judge) | 0.267 | 0.182 |
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".