Investigating Deep Convective Cores Combining CloudSat Observations and Model Simulations 
Bibliographic record
Abstract
The launch of the joint ESA JAXA Earth Cloud Aerosol and Radiation Explorer (EarthCARE) mission (May, 2024) marks the beginning of a new era of spaceborne radar measurements that target atmospheric convection. In addition to the EarthCARE mission that features the first Cloud Profiling Radar (CPR) with Doppler capability, NASA’s Investigation of Convective Updrafts (INCUS) and Atmosphere Observing System (AOS) missions aim to provide unique observations of convective dynamics. Prior to this upcoming decade of the study of atmospheric convection from space, the CloudSat CPR collected remarkable data of convective cores over a period of 15 years. Despite its high frequency that results in significant attenuation and multiple scattering effects, the 94-GHz CloudSat CPR offers a relatively small footprint (compared to the TRMM/GPM radar footprint of 5 km) and collocated radar-radiometer (passive) brightness temperatures (Tb). Here, we propose a refined deep convective core (DCC) identification scheme by first selecting the CPR profiles with continuous echoes between below 2 and above 10 km. The 10-dBZ echo top height is also required to exceed 10 km and located within 2 km from cloud top. Additionally, profiles with stratiform precipitation flags in the CloudSat products are not included in the analysis.We investigated the CloudSat observations from 2006 to 2019 globally and also with a focus over 4 convective basins where model simulations are performed by the NASA’s INCUS science team. The four deep convection basins are Amazon, Congo, Philippines, and Western Pacific, which represent a decent spectrum of atmospheric environments. It is found that the DCCs over the Congo basin are featured with larger size and likely more intensified updrafts, while the Western Pacific is characterized with finer-scale cores. The analysis shows that the DCCs with size below 5 km predominate, implying the narrow cores can be under detected by the large-footprint radars such as GPM. The distinct depressions of 94-GHz Tb due to the presence of high-density ice particles lend complementary information on DCC classifications. In addition, multiple scattering can be a confounding factor in interpretating the CPR measurements within deep convective clouds. Our preliminary calculations suggest the impact of multiple scattering becomes significant at ~2.5 km from radar cloud top on average and is subject to the DCC updraft intensity. Moreover, profiles of 94-GHz radar reflectivity and Tb are forward calculated from the high-resolution model simulation outputs to understand the constraints that such observations can afford on key measures such as convective mass fluxes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".