Bibliographic record
Abstract
Abstract. We developed and evaluated a new method to retrieve ground surface temperatures Tg below the snowpack from Soil Moisture and Ocean Salinity (SMOS) L-band brightness temperatures (BT). The study was performed over 21 reference sites providing with in situ ground temperatures Tg-insitu in Northern Alaska from 2011 to 2020, representative of Arctic tundra underlined by continuous permafrost, and with various open water fractions. Tg were obtained by inverting two types of microwave emission models (MEM) tailored for winter Arctic tundra environments. The first MEM assumed a homogeneous SMOS pixel and optimized the surface roughness Hr,gs. We observed the important influence of the frozen water bodies on Tg retrievals. Accordingly, we used an advanced MEM that accounts for the water surfaces within the SMOS pixels and describes their emission using an optimized water-ice interface roughness parameter, Hr,wi. For sites with water fraction < 0.04, our methods (median correlation R = 0.60) outperformed the European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) product (median R = 0.51) with respect to the reference sites. The bias between retrieved and in situ temperature was slightly negative (median bias = -0.2 °C). For sites with water fraction > 0.20, our water fraction correction reduced the bias, but the correlation of the Tg retrievals remained lower than that of ERA5. This study opens a new avenue for monitoring Tg below the snowpack in the Arctic using L-band BT, by inversion of a relatively simple MEM and limited auxiliary data. Extending this study to the whole Arctic area and taking advantage of the 15 years of SMOS data to study spatio-temporal variability of winter Tg in Arctic environments is excessively promising.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.007 |
| Insufficient payload (model declined to judge) | 0.499 | 0.413 |
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".