Retrieving frozen ground surface temperature under the snowpack in the Arctic permafrost area from SMOS observations
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) satellite L-band brightness temperatures (BTs). The study was performed over 21 reference sites providing 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. Values of Tg 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 Hr,gs. We observed the important influence of the frozen water bodies on Tg 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, Hr,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 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 extremely promising.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".