Advancing Spaceborne Observations of Atmospheric Convection: Addressing Sampling Challenges with the WIVERN Mission
Bibliographic record
Abstract
Convective storms occur globally, especially over the tropical oceans, and span a wide range of scales influenced by diverse environmental factors. Advancing our understanding of convective storms requires unraveling the complex relationships between convective dynamics, microphysical processes, and environmental forcing. These critical relationships demand statistically significant observations to inform model development and enable robust verification.Satellite observations along with reanalysis have provided a wealth of information on the relationship between the environment and the mesoscale organization of convection, however, no such comprehensive global dataset exist for convective dynamics. Key attributes of such a dataset (e.g. capturing updrafts, mass fluxes, and storm three-dimensional structure) remain undefined, particularly for exploring the relationship between convective dynamics and the near-storm environment.In this research, we use kilometer-scale simulations from diverse tropical oceanic basins, to explore the attributes of a global convective dynamics dataset, including sampling size, sensitivity to updraft magnitude, and associated uncertainties. By under-sampling the model, we define the minimum sampling size required for a statistically significant dataset capable of mapping the relationship between updrafts and environmental conditions. The analysis will allow us to specify the sampling characteristics needed for a satellite-based observing system to provide such data globally.Our findings support the case for the Wind Velocity Radar Nephoscope (WIVERN) mission which is one of two candidate missions currently in Phase A studies for potential selection as the Earth Explorer 11 mission under the European Space Agency’s FutureEO programme. WIVERN proposes a conically scanning Doppler radar in polar orbit, offering a swath of approximately 800 km at a viewing angle of 42o. We demonstrate how these measurements, offered by WIVERN’s unprecedented spatiotemporal sampling, facilitate the reconstruction of vertical motions and the three-dimensional vertical distribution of ice mass in mesoscale systems. Additionally, we examine the robustness of the relationship between the convective updrafts dataset and the environment, focusing on the sensitivity to the updraft magnitude detection limit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".