Evaluation of Operational Monsoon Moisture Surveillance and Severe Weather Prediction Utilizing COSMIC-2/FORMOSAT-7 Radio Occultation Observations

• Main Authors: Yu-Chun Chen, Chih-Chien Tsai, Yi-chao Wu, An-Hsiang Wang, Chieh-Ju Wang, Hsin-Hung Lin, Dan-Rong Chen, Yi-Chiang Yu
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Remote Sensing
• Subjects: monsoon moisture surveillance; severe weather prediction; COSMIC-2/FORMOSAT-7; radio occultation; data assimilation; typhoon Hagupit
• Online Access: https://www.mdpi.com/2072-4292/13/15/2979

Operational monsoon moisture surveillance and severe weather prediction is essential for timely water resource management and disaster risk reduction. For these purposes, this study suggests a moisture indicator using the COSMIC-2/FORMOSAT-7 radio occultation (RO) observations and evaluates numerical model experiments with RO data assimilation. The RO data quality is validated by a comparison between sampled RO profiles and nearby radiosonde profiles around Taiwan prior to the experiments. The suggested moisture indicator accurately monitors daily moisture variations in the South China Sea and the Bay of Bengal throughout the 2020 monsoon rainy season. For the numerical model experiments, the statistics of 152 moisture and rainfall forecasts for the 2020 Meiyu season in Taiwan show a neutral to slightly positive impact brought by RO data assimilation. A forecast sample with the most significant improvement reveals that both thermodynamic and dynamic fields are appropriately adjusted by model integration posterior to data assimilation. The statistics of 17 track forecasts for typhoon Hagupit (2020) also show the positive effect of RO data assimilation. A forecast sample reveals that the member with RO data assimilation simulates better typhoon structure and intensity than the member without, and the effect can be larger and faster via multi-cycle RO data assimilation.