Investigation of observational error sources in multi Doppler radar vertical air motion retrievals: Impacts and possible solutions
Bibliographic record
Abstract
Abstract. Multi-Doppler radar network observations have been used in different configurations over the last several decades to conduct three-dimensional wind retrievals in mesoscale convective systems. Here, the impact of the selected radar volume coverage pattern (VCP), the sampling time for the VCP, the number of radars used, and the added value of advection correction on the retrieval of the vertical air motion in the upper part of convective clouds is examined using the Weather Research and Forecasting (WRF) model simulation, the Cloud Resolving Model Radar SIMulator (CR-SIM) and a three-dimensional variational multi-Doppler radar retrieval technique. Comparisons between the model truth (i.e., WRF kinematic fields) and updraft properties (updraft fraction, updraft magnitude, and mass flux) retrieved from the CR-SIM-generated multi-Doppler radar field are used to investigate these impacts. In overall, the VCP elevation strategy and sampling time is found to have a significant effect on the retrieved updraft properties above 6 km altitude. Retrievals conducted using a 2-min or shorter VCPs show small impacts on the updraft retrievals, and the errors are comparable to retrievals using a snapshot cloud field. Increasing the density of elevations angles and/or an addition of data from one more radar can reduce this uncertainty. It is found that the VCP with dense elevation angles appears to be more effective than the addition of data from the fourth radar, if the VCP is performed in 2 minutes. The use of dense elevation angles combined with an advection correction applied to the 2-min VCPs can effectively improve the updraft retrievals. For longer VCP sampling periods (5 min) the errors are considerably larger, and the value of advection correction is challenging due to the rapid deformation of the dynamical structures in the simulated mesoscale convective system. This study highlights several limiting factors in the retrieval of upper-level vertical velocity from multi-Doppler radar networks and suggests that the use of rapid-scan radars can substantially improve the quality of wind retrievals if conducted in a limited spatial domain.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".