Sensitivity Differences Between 118 GHz and 183 GHz Radiance in All‐Sky Assimilation With Hydrometeor Control Variables and the Impact on a Typhoon Structure Forecast
Bibliographic record
Abstract
Abstract All‐sky microwave radiance assimilation plays a crucial role in numerical weather prediction, with the 183 GHz absorption band recognized for its strong sensitivity to humidity and clouds, and the 118 GHz absorption band primarily responsive to temperature but also sensitive to hydrometeors. The Microwave Humidity Sounder‐II (MWHS‐II) aboard the FengYun‐3 satellite includes channels near both bands, offering complementary capabilities. Although the assimilation performances of instrument channels in these bands have been studied, their comparative impacts on hydrometeor fields remain insufficiently explored. Through the separate and joint assimilation of 118 and 183 GHz MWHS‐II channels using hydrometeor control variables, this study investigates how directly adjusting hydrometeor analysis for thermodynamic consistency influences the analysis and forecasting of Typhoon Lekima (2019). Results show that the 183 GHz band has higher sensitivity to solid hydrometeors at higher levels, whereas the 118 GHz band is more sensitive to liquid hydrometeors at lower levels. These differences are clearly manifested in the hydrometeor analysis. Joint assimilation of both sets of channels improves the representation of temperature, humidity, and hydrometeor distribution. This not only enhances the analysis and forecasting of typhoon intensity but also deepens the understanding of typhoon structure. In particular, the sensitivity of the 183 GHz band to solid hydrometeors provides valuable insights into their role in the secondary eyewall formation for Typhoon Lekima (2019). With the use of hydrometeor control variables, the synergistic assimilation of MWHS‐II channels demonstrates potential for advancing tropical cyclone analysis, forecasting, and understanding of dynamics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".