A case study on the impact of ensemble data assimilation with GNSS-zenith total delay and radar data on heavy rainfall prediction

Shu Chih Yang, Zih Mao Huang, Ching Yuang Huang, Chih Chien Tsai, Ta Kang Yeh

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The performance of a numerical weather prediction model using convective-scale ensemble data assimilation with ground-based global navigation satellite systems-zenith total delay (ZTD) and radar data is investigated on a heavy rainfall event that occurred in Taiwan on 10 June 2012. The assimilation of ZTD and/or radar data is performed using the framework of the WRF local ensemble transform Kalman filter with a model grid spacing of 2 km. Assimilating radar data is beneficial for predicting the rainfall intensity of this local event but produces overprediction in southern Taiwan and underprediction in central Taiwan during the first 3 h. Both errors are largely overcome by assimilating ZTD data to improve mesoconvective-scale moisture analyses. Consequently, assimilating both the ZTD and radar data show advantages in terms of the location and intensity of the heavy rainfall. Sensitivity experiments involving this event indicate that the impact of ZTD data is improved by using a broader horizontal localization scale than the convective scale used for radar data assimilation. This optimization is necessary in order to consider more fully the network density of the ZTD observations and the horizontal scale of the moisture transport by the southwesterly flow in this case.

Original languageEnglish
Pages (from-to)1075-1098
Number of pages24
JournalMonthly Weather Review
Volume148
Issue number3
DOIs
StatePublished - 1 Mar 2020

Fingerprint

Dive into the research topics of 'A case study on the impact of ensemble data assimilation with GNSS-zenith total delay and radar data on heavy rainfall prediction'. Together they form a unique fingerprint.

Cite this