Evaluates water vapor dynamics in severe rainstorms, suggesting improved forecasting methods.
Monitoring water vapor during extreme events is crucial for understanding atmospheric physics, as well as for improving the prediction of severe weather and enhancing early warning systems. The major gap in monitoring water vapor during extreme events lies in the limited spatial and temporal resolutions of existing techniques. This restricts our ability to capture rapid and localized atmospheric changes driving severe weather. The primary goal of this study is to evaluate the performance of unconstrained Global Navigation Satellite Systems (GNSS) tomography in resolving the three‐dimensional structure and evolution of atmospheric water vapor during severe weather conditions. In this study, we evaluated 10 heavy rainstorm events (each exceeding 200 mm), whose observations were processed tomographically over the Hong Kong region. Emphasis is given to the case of Typhoon Sarika and Super Typhoon Haima, 2 days apart, which is analyzed in greater detail to illustrate the technique's capability under intense weather conditions. All events were validated using radiosonde (RS) observations and ERA5 reanalysis data, allowing a comprehensive assessment of the accuracy and consistency of the tomographic retrievals. The results indicate that unconstrained GNSS tomography struggles to resolve near‐surface moisture but achieves performance comparable to ERA5.
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Mateus et al. (2026) studied this question.
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