A Preliminary Analysis of Spatial Variability of Raindrop Size Distributions during Stratiform Rain Events
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The spatial variability of raindrop size distributions (DSDs) and precipitation fields is investigated utilizing disdrometric measurements from the four Precipitation Occurrence Sensor Systems (POSS) and radar reflectivity fields from S-band dual-polarization radar and vertically pointing X-band radar. The spatial cross correlation of the moments of DSDs, their ratio, error in rainfall estimate, and normalization parameters are quantified using a “noncentered” correlation function. The time-averaged spatial autocorrelation function of observed radar reflectivity factor (Ze) is smaller than that of estimated rainfall rate from Ze because of power-law R–Z transformation with its exponent larger than unity. The important spatial variability of DSDs and rain integral fields is revealed by the significant differences among average DSDs and leads to an average fractional error of 25% in estimating rainfall accumulation during an event. The spatial correlation of the reflectivity from POSS is larger than that of Ze because of larger measurement noise in Ze. The higher moments of DSDs are less correlated in space than lower moments. The correlation of rainfall estimate error is higher than that of estimated rainfall rate and of rainfall rate calculated from DSDs. The correlation of the characteristic number density is low (0.87 at 1.3-km distance), suggesting that the assumed homogeneity of the characteristic number density in space could result in larger errors in the retrieval of DSDs and rain-related parameters. However, the characteristic diameter is highly correlated in space.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it