94 GHz doppler wind radar satellite mission concept
Bibliographic record
Abstract
Extreme weather such as storms, hurricanes and typhoons, also called ‘high impact weather’, is a high priority area of research for the atmospheric dynamics and meteorological science communities. 94 GHz Doppler wind radar satellite mission concepts have been elaborated, which use cloud and precipitation droplets/particles as tracers to measure 3-D wind fields. The so-called polarisation-diversity pulse-pair (PDPP) technique enables to derive line-of-sight wind speed with good accuracy (< 2-3 m/s) and large unambiguous dynamic range (e.g. 75 m/s). Two distinct system concepts have been elaborated: (1) a conically scanning radar concept with large coverage (> 800 km) and ∼50 km along-track sampling, and; (2) a stereo viewing concept with high sampling resolution (< 4 km) within an inclined cut through the atmosphere. The former concept is adequate for studying large-scale severe/extreme weather systems, whereas the latter would be more suitable for understanding of small-scale convective phenomena. For demonstrating the potential of the FDPP technique for deriving accurate Doppler observations, ground-based and airborne Doppler radar campaigns are in preparation. The Galileo 94 GHz radar, upgraded recently to include a FDPP capability, at Chilbolton in the UK, will be used for an extended ground-based campaign (6 months). For the airborne campaign, the dual-frequency (9.4 + 94 GHz) NAWX radar on board a Convair-580 aircraft of the National Science Council of Canada will be upgraded and flown. This paper describes the observation requirements, preliminary satellite mission concepts, associated wind retrieval aspects and the planned demonstration campaigns.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".