Constraining CloudSat-based snowfall profiles using surface observations and C-band ground radar
Bibliographic record
Abstract
[1] The CloudSat Precipitation Radar, launched in 2006, provides vertical profiles of W-band (94 GHz) reflectivity and is sensitive to falling snow through all but the most intense precipitating cloud structures. Precipitation retrievals of falling snow are affected by a wide diversity of factors describing the medium, such as snow particle shape, size, and composition, which in turn are controlled by ambient factors including the environmental temperature and humidity. Because satellite-based radiometric sounders such as the Microwave Humidity Sounder (MHS) operate without the benefit of coordinated space radar observations, microphysical descriptions of the snow particle medium derived from CloudSat or other radar observations are beneficial to passive microwave (PMW) radiometer-only snowfall retrieval methods. At the coarse scale of these PMW observations, the radiative signal due to the snow is relatively weak compared to the contributions from the atmosphere and the land surface emissivity. Using the C-band (5 GHz) polarization-agile King City radar (WKR) operated by Environment Canada, we examined the vertical structure of winter precipitation events from coordinated overpasses of CloudSat and NOAA 18 (MHS). Two-dimensional video disdrometer observations are used to limit (constrain) the range of the drop-size distribution parameters that are provided through a priori databases to dual-frequency (C/W-band) radar retrieval. Bayesian retrievals using the constrained database produce water content profiles that more closely replicate the observed radar reflectivity profiles and transition smoothly between the single-frequency (CloudSat only) and dual-frequency regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".