Forecasting rapid intensification (RI) of tropical cyclones (TC) is a mission known for large errors. One under-researched factor that affects TC intensification is sea surface salinity (SSS), which is an important proxy for density stratification in certain ocean regions and can affect the surface enthalpy flux under a strengthening hurricane. To investigate the effects of SSS on TC RI, we use a previously built statistical model consisting of a variety of machine learning (ML) methods. A calibrator was trained on top of the ML models to correct probability forecasts. The ML model performance is improved with the addition of SSS in the Eastern North Pacific and the Caribbean subregion of the North Atlantic. Limited improvement is found in the Western North Pacific. In the Indian Ocean, SSS is also notably correlated with RI occurrence, but the TC samples are not sufficient to train ML models.

Severe Typhoon Koinu passed south of Hong Kong on 8 and 9 October 2023, triggering the issurance of the Increasing Gale or Storm Signal No. 9, the second highest tropical cyclone warning signal in Hong Kong. Koinu was a difficult case for TC warning service due to its compact size and rather erratic movement over coastal waters of Guangdong. To monitor Koinu's movement and wind structure, the Hong Kong Observatory utilized various observational platforms, including meteorological aircraft, ocean radar, and synthetic aperture radar on polar orbiting satellites. The paper presents major observations derived from these measurements. The aircraft probe and drosonde data suggested boundary layer inflow, warm core structure, eyewall updraft and downdraft, and high turbulence in the eyewall of the typhoon. The weather radar observations indicated occurrence of a waterspout in the vicinity of the typhoon. Additionally, the study evaluates the forecasting performance of the AI-based Pangu-Weather model, and the results highlight its better performance than the global numerical weather prediction models in forecasting tropical cyclones in the region. The documentation of these observations aims to provide valuable references for weather forecasters and stimulate further research on forecasting this type of tropical cyclones.