Improving Very Short-Term Rainfall Prediction in Taiwan: Insights from assimilation of innovative water vapor measurements

Presented on February 25, 2026, by

Professor Shu-Chih Yang
Atmospheric Sciences
National Central University
Taiwan

ABSTRACT:

Improving Very Short-Term Rainfall Prediction in Taiwan: Insights from assimilation of innovative water vapor measurements

A convective-scale ensemble data assimilation (EDA) system has been developed in Taiwan to improve very short-term heavy rainfall prediction. The main observation source is the radar data. However, without directly observing the moisture variable, the impact of radar assimilation on short-term rainfall prediction is limited. Recently, water vapor measurements have been available from innovative instruments and retrieving techniques. This talk will cover two investigations of innovative water vapor observations, including GNSS tropospheric gradient (TG) and the Micropulse Differential Absorption Lidar (MPD).

The ground-based GNSS Zenith Total Delay (ZTD) data has been the observation used in the operational convective-scale data assimilation system at Central Weather Administration (CWA) in Taiwan. GNSS-ZTD provides fast moisture information, which captures the precursor of convection initialization over complex terrain. Including non-CWA-operated stations, there are more than 400 GNSS stations in Taiwan, forming a uniquely dense GNSS observation network. In addition to ZTD observation, the TG measurement provides spatial moisture variations in the low troposphere and the availability of a fast TG observation operator makes TG assimilation feasible. Based on a severe afternoon thunderstorm on 24 June 2022 in the Taipei Basin, we conducted rapid-update data assimilation experiments to investigate the impact of the dense ground-based GNSS data. Compared to standard ZTD assimilation using CWA-operated stations, the assimilation of dense ZTD observations improves the moisture representation near the Taipei Basin, which is critical for the timing of convection initialization. For this case, TG observation reveals a strong moisture gradient into an inland river valley upstream of the Basin, complementing the primary moisture transport associated with the sea breeze. Additional TG assimilation enhances moisture from multiple pathways, facilitating the rapid convection development and the merging of the convection cells. Consequently, assimilating both dense ZTD and TG leads to significant improvements in the forecasted intensity and location of heavy rain, as well as the forecast performance at a longer lead time. Notably, the impact of TG assimilation is more pronounced when combined with dense ZTD data.

During the TAHOPE field campaign, NCAR MPDs were deployed over northern Taiwan to provide high-resolution water vapor profiles. We investigate how assimilating water vapor data from an MPD deployed upstream of an afternoon thunderstorm event on May 31, 2022, affects short-term precipitation prediction. While assimilating radar radial velocity effectively adjusted the dynamic condition, resulting in stronger convergence over the southeastern mountain slopes, the inability to correct the moisture field limits convection development and leads to an underestimation of rainfall intensity. The additional assimilation of MPD data introduced significant moisture corrections along the northwestern coast. This moisture was subsequently transported into the Taipei basin by enhanced westerly winds in the upper boundary layer. Although the MPD data was available for only a short duration ( within 30 minutes), its impact on the thermodynamic structure of PBL persisted and improved the effectiveness of radar reflectivity assimilation. Consequently, convective instability in the upper PBL of the Taipei Basin increased. Once convection cells were initialized by orographic lifting over the slopes south of the Taipei Basin, the convection intensified rapidly into higher altitude, leading to extreme heavy rainfall.
In summary, water vapor measurement in PBL becomes critical information for capturing the thermodynamic structure and thus convective instability in PBL. However, vertical and horizontal moisture distribution play different roles in convection initialization and heavy rainfall. Further investigation will be conducted to optimize the observing strategy for improving short-term extreme rainfall prediction.

BIO

Shu-Chih Yang received her B.S. and M.S. degree in Atmospheric sciences from the National Central University, Taiwan, in 1997 and 1999, respectively, and the Ph.D. degree in Meteorology from the University of Maryland, USA, in 2006. She is currently Distinguished Professor and department chair of Atmospheric Sciences with the National Central University in Taiwan. Her research interests include data assimilation, atmospheric predictability, and severe weather prediction, with the applications of atmospheric remote sensing observations including radar and GNSS satellite.

Dr. Yang was a recipient of Taiwanese Science Council’s Outstanding Young Research award in 2012, and young Taiwan Outstanding Women in Science in 2013, and Academia Sinica research award for junior research investigator in 2015. She was a recipient of President Luo Jia-Lun young outstanding research award from the National Central University in 2018. She serves as a topical editor for Geoscientific Model Development.