Ice clouds microphysical retrieval using 94‐GHz Doppler radar observations: Basic relations within the retrieval framework
Bibliographic record
Abstract
High quality measurements of ice cloud properties from ground‐ and space‐based sensors are key for improving our understanding of processes that affect ice cloud radiative effects and lifetime. Doppler cloud radars provide two independent measurements (reflectivity and Doppler velocity) to constrain the ice clouds microphysical retrievals. However, the retrievals are highly sensitive to the choice of the scattering forward model for non‐spherical particles at millimeter‐wavelengths and the selection of parameters in the mass‐ and velocity‐size relationships, as well as to the representation of the particle size distribution (PSD). In this paper (part 1), the development of the basic relations used in the retrieval is presented. A novel approach for reducing the number of free parameters required to describe the microphysical properties of ice particles is described. The new proposed form of the mass‐size relationship significantly reduces the sensitivity of the quantities of interest to the power law mass exponent, leaving only one parameter controlling mass dimensional relationship. A similar approach is adopted in the velocity calculation. In order to reduce the retrieval's dependence on the size distribution, the PSD defined for liquid‐equivalent diameter is described using the concept of double moment normalization. The two normalizing quantities, mean mass‐weighted diameter (Dm) and ice water content (IWC) are controlled mainly by the PSD size interval that is also an important contributor to the two Doppler observables. Both Dm and IWC are generally not very sensitive to the PSD segments of the smallest and largest particles that are considered as very uncertain.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".