Remote sensing and analysis of tropical cyclones: Current and emerging satellite sensors
Bibliographic record
Abstract
This article describes recent advances in the capability of new satellite sensors for observing Tropical Cyclones (TC) fine structure, wind field, and temporal evolution. The article is based on a World Meteorological Organization (WMO) report prepared for the 10th International Workshop on Tropical Cyclones (IWTC), held in Bali in December 2022, and its objective is to present updates in TC research and operation every four years. Here we focus on updates regarding the most recent space-based TC observations, and we cover new methodologies and techniques using polar orbiting sensors, such as C-band synthetic aperture radars (SARs), L-band and combined C/X-band radiometers, scatterometers, and microwave imagers/sounders. We additionally address progress made with the new generation of geostationary and small satellites, and discuss future sensors planned to be launched in the next years. We then briefly describe some examples on how the newest sensors are used in operations and data assimilation for TC forecasting and research, and conclude the article with a discussion on the remaining challenges of TC space-based observations and possible ways to address them in the near future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".