Daily Microwave-Derived Surface Temperature over Canada/Alaska
Bibliographic record
Abstract
Abstract The land surface temperature variation over northern high latitudes in response to the increase in greenhouse gases is challenging because of the lack of meteorological stations. A new method to derive the surface temperature from satellite microwave measurements that improves the frequency of measurements relative to that of infrared data is presented. The daily Special Sensor Microwave Imager 25 km × 25 km Equal-Area Scalable Earth Grid (EASE-Grid) dataset provided by the National Snow and Ice Data Center in Boulder, Colorado, is processed to derive the surface temperature using the method proposed by Fily et al. A normalization approach based on the 40-yr ECMWF reanalysis (ERA-40; 2.5°) temperature diurnal cycle fitted for each pixel is applied to overcome the time acquisition variation of measurements as well as to interpolate missing data. An adaptive mask for discriminating between ice-free pixels and snow-free pixels is also applied. The resulting database is thus a new consistent hourly series of near-surface air temperatures during the summer (without snow). The mean accuracy is on the order of 2.5–3 K when compared with the synchronous in situ air temperature and different gridded datasets over Canada and Alaska. The trend over the last 10 yr confirms observed climate evolution: an increase in summer surface temperature of +0.09° ± 0.04°C yr−1, at the 90% confidence level, for Canada between 1992 and 2002, whereas a decrease of −0.15° ± 0.05°C yr−1, at the 95% confidence level, is observed for Alaska. Spatial and temporal anomalies show regional impacts of meteorological phenomena such as the El Niño extreme warm summer episode of 1998, the decrease in temperatures in 1992 in Canada following the volcanic eruption of Mount Pinatubo in June 1991, and the strong drought in the prairies in 2001. The annual sum of positive degree-days (thawing index) has been related to the permafrost distribution. The lower values of the derived thawing index (<1400 degree-days) are related well to the presence of continuous and dense discontinuous permafrost. The observed increase in the thawing index during the 1992–2002 period represents a decrease of classified permafrost area of 7%.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".