Toward a Near Real-Time Product of Air Temperature Maps from Satellite Data and <i>In Situ</i> Measurements in Arid Environments
Bibliographic record
Abstract
Earth observation data and in situ measurements were used to derive a method for the estimation of Air Temperature(AirT) and thereby produce maps. The methodology was developed and validated using data acquired at five stations located in the United Arab Emirates(UAE) during the year 2012. In the first step, the effect of different variables on the estimation of AirT values was investigated and the variables of high importance were used to build the final model. Land surface temperature (LST), relative humidity, global horizontal irradiance, direct normal irradiance, and diffuse horizontal irradiance were identified as the most important inputs for Air Testimation. Models were developed separately for four different cases based on the seasons (Winter/Summer) and the time of the day (Day/Night). The models were evaluated using jackknife validation on the stations resulting in root mean square errors of 2.25°C(Winter/Day), 2.24°C (Winter/Night), 2.56°C (Summer/Day), and 2.69°C (Summer/Night) and an overall average accuracy of 2.44°C. Finally, the model has been applied on a larger scale by assimilating the relevant Earth observation data and in situ measurements for the creation of AirT maps for the entire country. The resulting AirT maps were generated in near real time at a temporal resolution of 15 min from METEOSAT/SEVIRI LST and the above-mentioned in situ measurements. These maps can be considered as important resources for several applications (e.g, climate studies, solar energy applications, thermal comfort studies, etc.).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".