A daily gridded high-resolution meteorological data set for historical impact studies in Switzerland since 1763
Bibliographic record
Abstract
Abstract. High-resolution gridded daily data is needed to study historical climate and weather impacts. Current daily gridded data sets in Switzerland extend to 1961 or 1971 for variables such as minimum and maximum temperature and sunshine duration. However, studying historical weather and climate events, such as the year-without-a-summer requires much longer time periods. For Switzerland, high-resolution gridded reconstructions of daily mean temperature and daily precipitation sums have recently been developed based on a large amount of early instrumental data for a period from 1763 to 1960. Here, we present an extension of these daily reconstructions to six more variables, namely, relative sunshine duration, relative humidity, minimum and maximum temperature at 2 m, and u- and v-wind at 10 m with a 1x1 km resolution. These additional reconstructions are based on the same method as the previous reconstructions by combining the analogue resampling method and data assimilation. Cross-validation results using a network representative of early 19th-century observations show a mean squared error skill score ranging from 0.70 to 0.80 for wind speed, depending on the season. For maximum and minimum temperature, values average between 0.48 to 0.82, depending on the seasons. These results indicate reasonable skill of the reconstructions and show that the wind and temperature fields outperform climatology despite the data scarcity in the historical period. However, for relative humidity and relative sunshine duration, the values of the mean squared skill score are significantly lower, ranging between -0.31 to 0.48. Furthermore, we explored the potential of the extended reconstructions by evaluating historical and contemporary wildfire events in Switzerland using the widely used Canadian Forest Fire Weather Index (FWI). The two historical fires were associated with a notably high fire danger in the reconstruction. For the contemporary winter fire, the reconstruction agrees well with the index calculated from the COSMO-1 weather forecast model, though neither indicates exceptionally high fire danger. Overall, this is the first data set that enables impact studies of weather and climate in Switzerland, reaching as far back as 1763.
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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 | 0.009 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".