Retrieving frozen ground surface temperature under the snowpack in the Arctic permafrost area from SMOS observations
Bibliographic record
Abstract
We developed and evaluated a new method to retrieve ground surface temperatures T g below the snowpack from Soil Moisture and Ocean Salinity (SMOS) satellite L-band brightness temperatures (BTs). The study was performed over 21 reference sites providing in situ ground temperatures T g-insitu in Northern Alaska from 2011 to 2020, representative of Arctic tundra underlined by continuous permafrost, and with various open water fractions. Values of T g were obtained by inverting two types of microwave emission models (MEMs) tailored for winter Arctic tundra environments. The first MEM assumed homogeneous SMOS pixels and optimized the surface roughness H r,gs . We observed the important influence of the frozen water bodies on T g retrievals. Accordingly, we used a second more advanced MEM that accounts for the water surfaces within the SMOS pixels and describes their emission using an optimized water–ice interface roughness parameter, H r,wi . For sites with water fraction < 0.04, our methods (median 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 T g retrievals remained lower than that of ERA5. This study opens a new avenue for monitoring T g 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 T g in Arctic environments is extremely 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".