First insights into deep convection by the Doppler velocity measurements of the EarthCARE Cloud Profiling Radar
Bibliographic record
Abstract
Convective updrafts and downdrafts play a vital role in Earth’s energy and water cycles by modulating vertical energy and moisture transport and shaping precipitation patterns. Despite their importance, the characteristics of convective motions and their relationship to the near-storm environment remain poorly constrained by observations. Doppler radars, in principle, are able to measure the vertical air motion within clouds, thus providing critical insight into convective dynamics and enabling estimates of convective mass flux. The payload of the recently launched EarthCARE satellite mission includes a 94 GHz Cloud Profiling Radar (CPR) with Doppler capability. In this study, we present first-light CPR Doppler velocity observations in deep convective clouds. These early examples offer a first glimpse into the dynamic nature of cloud systems. The narrow footprint of the CPR helps reduce the impact of multiple scattering and non-uniform beam filling (NUBF) on the Doppler velocity measurements. However, the instrument’s low Nyquist velocity presents a significant challenge for recovering the true Doppler velocity profiles in deep convective systems. The CPR Doppler velocity observations are expected to challenge traditional methodologies for identifying deep convective cores, which typically rely on reflectivity-based thresholds. We showcase examples that demonstrate the synergy between CPR Doppler velocity measurements and geostationary satellite observations, illustrating how their combined use can help capture the evolution of the convective lifecycle. These results align with EarthCARE’s broader mission objectives and highlight the potential of spaceborne Doppler radars to significantly advance our understanding of cloud dynamics and convection in the climate system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".