Synergistic radar and radiometer retrievals of ice hydrometeors
Bibliographic record
Abstract
Abstract. The upcoming Ice Cloud Imager (ICI) radiometer, to be launched on board the second generation of European operational meteorological satellites (Metop-SG), will be the first microwave imager to provide sub-millimeter observations of the atmosphere. The Microwave Imager (MWI) radiometer will be flown on the same satellites and complement the ICI sensor with observations at traditional millimeter wavelengths. The addition of these two new passive microwave sensors to the global system of earth observation satellites opens up opportunities for synergistic satellite missions aiming to maximize the scientific return of the Metop-SG program. This study analyzes the potential benefits of combining observations of the MWI and ICI radiometers with a 94-GHz cloud radar for the retrieval of frozen hydrometeors. Starting from a simplified numerical experiment, it is shown that the complementary information content in the radar and radiometer observations can help to better constrain the particle size distribution of ice particles in the atmosphere. The feasibility of the combined retrieval is demonstrated by applying a one-dimensional, variational cloud-retrieval algorithm to simulated observations from a high-resolution atmospheric model. Comparison of the results with passive- and radar-only versions of the retrieval algorithm confirms that synergies between the active and passive observations allow an improved retrieval of microphysical properties of frozen hydrometeors. The effect of the assumed ice particle shape on the results is analyzed and found to be critical for obtaining good retrieval performance. In addition to this, the synergistic retrieval shows improved sensitivity to liquid water in both warm and supercooled clouds. The results of this study clearly demonstrate the potential of the combined observations to constrain the microphysical properties of ice hydrometeors which can help to reduce errors in retrieved profiles of mass- and number densities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".