Bibliographic record
Abstract
Abstract. The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and Atmospheric Lidar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targets present in the profiles. The instruments are sensitive to different parts of the particle size distribution, and provide independent but overlapping information in optical and microwave wavelengths. ATLID is sensitive to aerosols and small cloud particles and CPR to large ice particles, snowflakes and raindrops. It is therefore possible to better classify atmospheric targets when collocated radar and lidar measurements exist compared to a single instrument. The cloud phase, precipitation and aerosol type within the column sampled by the two instruments can then be identified. ATLID-CPR Target Classification (AC-TC) is the product created for this purpose by combining the ATLID Target Classification (A-TC) and CPR Target Classification (C-TC). AC-TC is crucial for the subsequent synergistic retrieval of cloud, aerosol and precipitation properties. AC-TC builds upon previous target classifications using CloudSat/CALIPSO synergy, while providing richer target classification using the enhanced capabilities of EarthCARE's instruments: CPR's Doppler velocity measurements to distinguish snow and rimed snow from ice clouds, and ATLID's lidar ratio measurements to objectively discrimination between different aerosol species and optically thin ice clouds. In this paper we first describe how the single-instrument A-TC and C-TC products are derived from ATLID and CPR measurements. Then the AC-TC product, which combines the A-TC and C-TC classifications using a synergistic decision matrix, is presented. Simulated EarthCARE observations are used to test the processors generating the target classifications, with results presented using the Halifax scene. Finally, the target classifications are evaluated by quantifying the fractions of ice and snow, liquid clouds, rain and aerosols in the atmosphere that can be successfully identified by each instrument and their synergy. We show that radar-lidar synergy helps better detect ice and snow, with ATLID detecting radiatively-important optically thin cirrus and cloud-tops while CPR penetrates most deep and highly concentrated ice clouds. The detection of rain and drizzle is entirely due to C-TC, while that of liquid clouds and aerosols is due to A-TC. The evaluation also shows that simple assumptions can be made to compensate for when the instruments are obscured by extinction (ATLID) or surface clutter and multiple scattering (CPR); this allows for the recovery of a majority of liquid cloud not detected by the active instruments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.023 | 0.019 |
| Insufficient payload (model declined to judge) | 0.175 | 0.141 |
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".