Recent observations of clouds and precipitation by the airborne precipitation radar 2<sup>nd</sup>generation in support of the GPM and ACE missions
Bibliographic record
Abstract
The Ku-/Ka-band, Doppler, scanning, polarimetric airborne radar, known as the Airborne Dual-Frequency Precipitation Radar (APR-2) has been collecting data since 2001 in support of many spaceborne instruments and missions aiming at the observation of clouds and precipitation (e.g., TRMM, AMSR-E, GPM, CloudSat, ACE). The APR-2 suite of processing and retrieval algorithms (ASPRA) produces Level 1 (L1) products, microphysical classification and retrievals, and wind intensity estimates. ASPRA was also generalized to operate on an arbitrary set of radar configuration parameters to study the expected performance of multi-frequency spaceborne cloud and precipitation radars such as the GPM DPR (Global Precipitation Measurement mission, Dual-Frequency Precipitation Radar) and a notional radar for the Aerosol/Clouds/Ecosystem (ACE) mission. In this paper we illustrate the unique dataset collected during the Global Precipitation Measurement Cold-season Precipitation Experiment (GCPEx, US/Canada Jan/Feb 2012). We will focus on the significance of these observations for the development of algorithms for GPM and ACE, with particular attention to classification and retrievals of frozen and mixed phase hydrometeors.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 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 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".