Automated rain rate estimates using the Ka-band ARM Zenith Radar (KAZR)
Bibliographic record
Abstract
Abstract. The use of millimeter wavelength radars for probing precipitation has recently gained interest. However, estimation of precipitation variables is not straightforward due to strong attenuation, radar receiver saturation, antenna wet radome effects and natural microphysical variability. Here, an automated algorithm is developed for routinely retrieving rain rates from profiling Ka-band (35-GHz) ARM zenith radars (KAZR). A 1-D simple, steady state microphysical model is used to estimate the impact of microphysical processes and attenuation on the profiles of the radar observables at 35-GHz and thus provide criteria for identifying when attenuation or microphysical processes dominate KAZR observations. KAZR observations are also screened for saturation and wet radome effects. The proposed algorithm is implemented in two steps: high rain rates are retrieved by using the amount of attenuation in rain layers, while lower rain rates by the Ze–R (reflectivity-rain rate) relation is implemented. Observations collected by the KAZR, disdrometer and scanning weather radars during the DYNAMO/AMIE field campaign at Gan Island of the tropical Indian Ocean are used to validate the proposed approach. The results indicate that the proposed algorithm can be used to derive robust statistics of rain rates in the tropics from KAZR observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".