Why Scanning Instruments Are a Necessity for Constraining Temperature and Humidity Fields in the Lower Atmosphere
Bibliographic record
Abstract
Abstract The ability of different ground-based measurement strategies for constraining thermodynamic variables in the troposphere, particularly at the mesoscale, is investigated. First, a preliminary assessment of the capability of pure-vertical sounders for constraining temperature and water vapor fields in clear-sky conditions to current accuracy requirements is presented. Using analyses over one month from the Rapid Refresh model as input to an optimal estimation technique, it is shown that the horizontal density of a network of nonexisting, ideal vertical profiling instruments must be greater than 30 km in order to achieve accuracies of 0.5 g kg−1 for water vapor and 0.5 K for temperature. Then, an assessment of a scanning microwave radiometer’s capability for retrieving water vapor and temperature fields in a cloud-free environment over two- and three-dimensional mesoscale domains is also presented. The information content of an elevation and azimuthal scanning microwave radiometer is assessed using the same optimal estimation framework. Even though, in any specific pointing direction, the scanning radiometer does not provide much information, it is capable of providing considerably more constraints on thermodynamic fields, particularly water vapor, than a near-perfect vertical sounder. These constraints on water vapor are largely located within 80 km of the radiometer and between 1000- and 7000-m altitude, while temperature constraints are limited to within 35 km of the instrument at altitudes between the ground and 1500 m. The findings suggest that measurements from scanning radiometers will be needed to properly constrain the temperature and especially moisture fields to accuracies needed for mesoscale forecasting.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".