Validating snow surface radiative transfer models between 89 and 243GHz using airborne observations over Arctic tundra
Bibliographic record
Abstract
Satellite microwave radiances in atmospheric sounding bands, such as the 183GHz water vapour band, are an important source of data for Numerical Weather Prediction. However, these observations are frequently discarded in polar regions as they are also sensitive to the surface, and there is large uncertainty in the background surface emissivity which depends on the microphysical properties of the snowpack. We evaluate simulations of brightness temperature and emissivity from the Snow Microwave Radiative Transfer (SMRT) model for Arctic tundra snow at frequencies between 89 and 243GHz to assess the potential of being able to assimilate observations at key sounding frequencies, such as 183GHz. In-situ measurements of the surface snowpack were collected for 36 snow pits in Trail Valley Creek, near Inuvik, Canada during the March 2018 Measurements of Arctic Cloud, Snow, and Sea Ice nearby the Marginal Ice Zone (MACSSIMIZE) campaign, a collaboration between the Met Office, Northumbria University, Edinburgh University and the Universite de Sherbrooke. These snowpack measurements provide realistic microphysical snow properties as input to SMRT. We present the evaluation of SMRT simulations against surface-based radiometer observations and airborne observations taken with the Microwave Airborne Radiometer Scanning System (MARSS) and International Submillimetre Airborne Radiometer (ISMAR) on the Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 research aircraft.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".