Characterization of Shallow Oceanic Precipitation using Profiling and Scanning Radar Observations at the Eastern North Atlantic ARM Observatory
Bibliographic record
Abstract
Abstract. Shallow oceanic precipitation variability is documented using 2nd generation radars located at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic observatory: the Ka-band ARM zenith radar (KAZR2), the Ka-band scanning ARM cloud radar (KaSACR2) and the X-band scanning ARM precipitation radar (XSAPR2). First, the radars and measurement post-processing techniques, including sea clutter removal and calibration against collocated disdrometer and Global Precipitation Mission (GPM) observations are described. Then, we present how a combination of profiling radar and lidar observations can be used to estimate adaptive (in both time and height) parameters that relate radar reflectivity (Z) to precipitation rate (R) in the form Z = αRβ which we use to estimate precipitation rate over the domain observed by XSAPR2. Furthermore, Constant Altitude Plan Position Indicator (CAPPI) gridded XSAPR2 precipitation rate maps are also constructed. Hourly precipitation rate statistics estimated from the three radars differ; that is because KAZR2 is more sensitive to shallow virga and because XSAPR2 suffers from less attenuation that KaSACR2 and as such is best suited to characterize intermittent and mesoscale-organized precipitation. Further analysis reveals that precipitation rate statistics obtained by averaging 12 h of KAZR2 observations can be used to approximate that of a domain of 2500 km2 averaged over similar time periods. However, it was determined that KAZR2 is unsuitable to characterize domain average precipitation rate over shorter periods. But even more fundamentally, these results suggest that observations cannot produce objective domain precipitation estimate and that forward-simulators should be used to guide high temporal-resolution model evaluation studies.
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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 | 0.000 |
| 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 | 0.000 | 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 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".