An Intercomparison of the Sparv Windsond S1H2 and Vaisala RS41-SGP in Severe Weather Environments
Bibliographic record
Abstract
Abstract High spatiotemporal resolution sounding data collection in convective environments has been limited in the past, preventing effective sampling of storm-scale features by field campaigns. In the last decade, a new low-cost multisonde sounding system developed by Sparv Embedded, the Windsond S1H2, has attempted to fill this gap. Several recent field campaigns have used Windsonds to analyze low-level convective environments, but evaluations of Windsond data are scarce. This study compares the Windsond S1H2 to the Vaisala RS41-SGP through a series of 33 flights launched in a variety of conditions and times of day within environments supportive of convective storms. The sondes were tethered to the same balloon, and the data were interpolated to constant height levels (every 10 m) to allow a direct comparison of variables. The comparisons show that the Windsond’s slower response time and lack of solar radiation correction result in noticeable differences in temperature and dewpoint temperature, particularly during clear daytime flights and in dry layers. A difference in the wind-smoothing approach between the two instruments results in slower wind speeds from the Vaisala systems near the surface (0–150 m) but becomes negligible above the surface layer. This indicates that Windsonds can be used confidently to supplement more traditional sounding systems for obtaining profiles in lower portions of the atmosphere that are critical for severe-weather-related research efforts but have less confidence at higher altitudes and in conditions outside of convection. Results from one case study are shown to highlight the comprehensive results found in the intercomparison.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".