Gravity waves and turbulence
Bibliographic record
Abstract
Introduction Wind motions in the atmosphere can cover a wide range of temporal and spatial scales. They may include variations on annual, seasonal, monthly, daily, hourly, and minute scales, down to scales of seconds. Spatially, motions may cover global scales down to synoptic (continental-sized), meso- (city-sized) and microscales (e.g., Ahrens , 1999, Figure 10.1).Windprofiler radars can study all of these scales. However, we cannot possibly cover all of them in this chapter. Larger scale motions (including planetary waves and tides) can be studied well with satellites and in-situ instruments carried by balloons and rockets, as well as numerical computer models. While profilers can also contribute here, it is at the smaller scales that windprofilers really make their best contributions. We will therefore concentrate in this chapter on synoptic, mesoscale and microscale motions, with strongest emphasis on the last two. The primary focus will be on height regions where MST radars have made a significant contribution, restricting discussion to the troposphere, lower stratosphere (below 25–30 km), and the upper mesosphere and lower thermosphere (60 to 100 km altitude). Other height regimes will be discussed primarily in their relation to these regions. In meteorology, atmospheric mesoscales motions refer to spatial scales between a few kilometers and one or two hundred kilometers, and temporal scales of the order of minutes to a few hours. In the troposphere, mesoscale events include thunderstorms, tornadoes, and various types of local circulations like land and sea breezes and valley breezes. Typical synoptic scale events include hurricanes, high and low pressure systems, and frontal systems. Some or all of these events may be quite familiar to many readers. In fact, these events are only really dominant in the lowest few kilometers of the atmosphere. MST atmospheric radars can be used to investigate these phenomena, and this has been done in the past (e.g. Strauch et al. (1984); Gage et al. (1991a); Webster and Lukas (1992); Teshiba et al. (2001) (and references therein); Röttger and Larsen (1990); Hooper andPavelin (2003), among others). Since this book has a special chapter on meteorology, these events will not be pursued here in any detail. When MST radars are used for studies to heights of ten kilometers and more, and even into the upper atmosphere, a different class of mesoscale/synoptic scale motion becomes apparent. This motion is often well organized and can propagate over large distances.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".