Considerations for spaceborne 94 GHz radar observations of precipitation
Why this work is in the frame
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Bibliographic record
Abstract
Spaceborne 94 GHz radars offer sufficient sensitivity to observe all types of precipitation and their associated clouds without stretching the instrument requirements. In this study, considerations for precipitation classification and detection from space using 94 GHz radars are presented. First, a technique that uses the path‐integrated attenuation normalized to the depth of the rain layer, the snow‐integrated reflectivity and the reflectivity difference from snow to rain to discriminate convective and stratiform profiles is proposed. Second, we present a critical view of sampling issues for precipitation and Doppler measurements from space at 94 GHz. A new sampling strategy for spaceborne 94 GHz radars with alternating cloud and precipitation modes is discussed that can improve our ability to detect and measure precipitation without losing sight of the main objective of deploying such high frequency radars in space, to map the global distribution of clouds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it