Evaluating the Effectiveness of Current Atmospheric Refraction Models in Predicting Sunrise and Sunset Times
Bibliographic record
Abstract
The standard value for atmospheric refraction on the horizon of 34', used in all publicly available sunrise and sunset calculators, is found to be inadequate. The assumptions behind atmospheric models that predict this value fail to account for real meteorological conditions. The result is an uncertainty of one to five minutes in sunrise and sunset predictions at mid-latitudes (0° - 55° N/S). A sunrise/set calculator that interchanges the refraction component by varying the refraction model was developed. Two atmospheric refraction models of increasing complexity were tested along with the standard value. The predictions were compared with data sets of observed rise/set times taken from Mount Wilson Observatory in California, University of Alberta in Edmonton, Alberta, observations from various locations in Chile, and on-board the SS James Fergus in the Atlantic Ocean. Increasing the complexity of the model did not yield significantly better results. These observations make up the entirety of documented sunrise and sunset times. A thorough investigation of the problem requires a more substantial data set of observed rise/set times and corresponding meteorological data from around the world. A mobile application, Sunrise & Sunset Observer, was developed so that anyone can capture this astronomical and meteorological data using their smartphone as part of a citizen science project. Data analysis will lead to more complete models that will provide higher accuracy rise/set predictions to benefit astronomers, navigators, and outdoorsmen everywhere.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".