Synergistic radar and radiometer retrievals of ice hydrometeors
Bibliographic record
Abstract
Abstract. Remote sensing observations at sub-millimeter wavelengths provide higher sensitivity to small hydrometeors and low water content than observations at millimeter wavelengths, which are traditionally used to observe clouds and precipitation. They are employed increasingly in field campaigns to study cloud microphysics and will be integrated into the global meteorological observing system to measure the global distribution of ice in the atmosphere with the launch of the Ice Cloud Imager (ICI) radiometer on board the second generation of European operational meteorological satellites (Metop-SG). Observations at these novel wavelengths provide valuable information not only on their own but also in combination with complementary observations at other wavelengths. This study investigates the potential of combining passive sub-millimeter radiometer observations with a hypothetical W-band cloud radar for the retrieval of frozen hydrometeors. An idealized cloud model is used to investigate the information content of the combined observations and establish their capacity to constrain the microphysical properties of ice hydrometeors. A synergistic retrieval algorithm for airborne observations is proposed and applied to simulated observations from a cloud-resolving model. Results from the synergistic retrieval are compared to equivalent radar- and passive-only implementations in order to assess the benefits of the synergistic sensor configuration. The impact of the assumed ice particle shape on the retrieval results is assessed for all retrieval implementations. We find that the combined observations better constrain the microphysical properties of ice hydrometeors, which reduces uncertainties in retrieved ice water content and particle number concentrations for suitable choices of the ice particle model. Analysis of the retrieval information content shows that, although the radar contributes the largest part of the information in the combined retrieval, the radiometer observations provide complementary information over a wide range of atmospheric states. Furthermore, the combined observations yield slightly improved retrievals of liquid cloud water in mixed-phase clouds, pointing towards another potential application of combined radar–radiometer observations.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".