Application of satellite observations for the evaluation of the land surface temperature from GEM model forecast
Bibliographic record
Abstract
The Global Environmental Multiscale model (GEM) was evaluated against satellite observations and measurements from synoptic stations. The computational grid was set up in the global variable mode with the resolution of ~25 km over Central Europe. Model evaluation was performed over Central Europe within a window of 43-56°N latitude and 10-25°E longitude. Surface temperature forecasts were compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product. Air temperature measured at the height of 2 metres was obtained from about 480 synoptic stations from 13 Central Europe countries. Air temperature measurements collected at 9 UTC and 12 UTC during five days (31 January, 2 February, 3 March, 27 April and 18 June 2012) was compared with the GEM model results. Evaluation showed good agreement between modelled and observed data. In case of air temperature, the averaged value of the Mean Bias Error (MBE) was -0.42, the averaged Root Mean Square Error (RMSE) and the Mean Absolute Gross Errors (MAGE) were 3.21 and 2.32, respectively. Land surface temperature comparisons gave results of -2.01; 3.91 and 3.24 of the (MBE), (RMSE), and (MAGE), respectively. Also, correlation of derived modelling errors between surface temperature and air temperature are discussed. In each case the correlation coefficient was positive. The highest value (0.70) was obtained for periods when surface – atmosphere radiative exchange processes were dominant.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".