Assimilation of GNSS PWV with NCAR-RTFDDA to Improve Prediction of a Landfall Typhoon

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 1; p. 178
• Main Authors: Wang, Haishen, Liu, Yubao, Liu, Yuewei, Cao, Yunchang, Liang, Hong, Hu, Heng, Liang, Jingshu, Tu, Manhong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2022
• Subjects: Algorithms; assimilation; Atmospheric models; Atmospheric water; Data assimilation; Data collection; Experiments; Global navigation satellite system; GNSS; Humidity; observation nudging; precipitable water vapor; Precipitation; Predictions; Rain; Rainfall; RTFDDA; Satellite observation; Temporal resolution; typhoon; Typhoons; Variables; Water vapor; Weather forecasting; Wind; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14010178

Precipitable water vapor (PWV) retrieved from ground-based global navigation satellite system (GNSS) stations acquisition signal of a navigation satellite system provides high spatial and temporal resolution atmospheric water vapor. In this paper, an observation-nudging-based real-time four-dimensional data assimilation (RTFDDA) approach was used to assimilate the PWV estimated from GNSS observation into the WRF (Weather Research and Forecasting) modeling system. A landfall typhoon, “Mangkhut”, is chosen to evaluate the impact of GNSS PWV data assimilation on its track, intensity, and precipitation prediction. The results show that RTFDDA can assimilate GNSS PWV data into WRF to improve the water vapor distribution associated with the typhoon. Assimilating the GNSS PWV improved the typhoon track and intensity prediction when and after the typhoon made landfall, correcting a 5–10 hPa overestimation (too deep) of the central pressure of the typhoon at landfall. It also improved the occurrence and the intensity of the major typhoon spiral rainbands.