The use of remote sensing in addressing scaling issues for numerical models of atmospheric processes
Bibliographic record
Abstract
The problems of thermal forcing during cyclogenesis in the Arctic Polar Basin and numerical modelling are discussed. For the past three years, the authors have been taking detailed surface radiation and energy balance measurements over the sea ice in the Canadian Arctic to more fully understand the process linkages and feedbacks between the atmosphere dynamics and surface processes. The link between the mesoscale models and micro-scale in situ observations is remote sensing. The authors have been exploring a variety of techniques to scale up from detailed micrometeorological measurements to the quasi-geostrophic models through aggregation of data based upon remote sensing information. In this paper the authors review the problems of scaling from observations to models through discussion of the results of the authors' modelling efforts at the synoptic scale and their high resolution observations at the ice surface as part of the SIMMS Programme (Seasonal Sea Ice Monitoring and Modelling Site) in Lancaster Sound. By use of an example, the authors illustrate how microwave scatter can be used to infer the climatological albedo, thereby demonstrating the role which remote sensing can play in bridging the scale gap. The authors outline their research plans over the next five years for addressing other terms of the surface interaction with the atmosphere.>
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".