Impacts of tropical cyclones on the thermodynamic conditions in the tropical tropopause layer observed by A-Train satellites
Bibliographic record
Abstract
The tropical tropopause layer (TTL) is the transition layer between the troposphere and the stratosphere. Tropical cyclones may impact the TTL by perturbing the vertical distributions of cloud, temperature, and water vapor. This study combines several A-Train instruments, including radar from CloudSat, lidar from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite, and the Atmospheric Infrared Sounder (AIRS) on the Aqua satellite, to detect signatures of cyclone impacts on the distribution patterns of cloud, water vapor, temperature, and radiation by compositing these thermodynamic fields relative to the cyclone center location. Based on the CloudSat 2B-CLDCLASS-LIDAR product, this study finds that tropical cyclone events considerably increase the occurrence frequencies of TTL clouds, in the form of cirrus clouds above a clear troposphere. The amount of TTL cloud ice, however, is found to be mostly contributed by overshooting deep convection that penetrates the base of the TTL at 16 km. To overcome the lack of temperature and water vapor products in cloudy conditions, this study implements a synergistic method that retrieves temperature, water vapor, ice water content, and effective radius simultaneously by incorporating observations from AIRS, CloudSat, and CALIPSO. Using the synergistic method, we find a vertically oscillating pattern of temperature anomalies above tropical cyclones, with warming beneath the cloud top (around 16 km) and cooling above. Based on water vapor profiles retrieved by the synergistic method, we find that the layer integrated water vapor (LIWV) above 16 km is higher above tropical cyclones, especially above overshooting deep convective clouds, compared to climatological values. Moreover, we find that the longwave and net radiative cooling effect of clouds prevails within 1000 km of tropical cyclone centers. The radiative heating effects of clouds from the CloudSat 2B-FLXHR-LIDAR product are well differentiated by the collocated brightness temperature of an infrared window channel from the collocated AIRS L1B product. By performing instantaneous radiative heating rate calculations, we further find that TTL hydration is usually associated with radiative cooling of the TTL, which inhibits the diabatic ascent of moist air across isentropic surfaces to the stratosphere. Therefore, the radiative balance of the TTL under the impact of the cyclone does not favor the maintenance of moist anomalies in the TTL or transporting water vertically to the stratosphere.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".