Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

Chih-Chien Tsai; Shu-Chih Yang; Yu-Chieng Liou

doi:10.3402/tellusa.v66.21804

Improving quantitative precipitation nowcasting with a local ensemble transform Kalman filter radar data assimilation system: observing system simulation experiments

Tellus

Volume 66 (2014): Issue 1

By: Chih-Chien Tsai, Shu-Chih Yang and Yu-Chieng Liou

Open Access

|Dec 2014

Abstract

This study develops a Doppler radar data assimilation system, which couples the local ensemble transform Kalman filter with the Weather Research and Forecasting model. The benefits of this system to quantitative precipitation nowcasting (QPN) are evaluated with observing system simulation experiments on Typhoon Morakot (2009), which brought record-breaking rainfall and extensive damage to central and southern Taiwan. The results indicate that the assimilation of radial velocity and reflectivity observations improves the three-dimensional winds and rain-mixing ratio most significantly because of the direct relations in the observation operator. The patterns of spiral rainbands become more consistent between different ensemble members after radar data assimilation. The rainfall intensity and distribution during the 6-hour deterministic nowcast are also improved, especially for the first 3 hours. The nowcasts with and without radar data assimilation have similar evolution trends driven by synoptic-scale conditions. Furthermore, we carry out a series of sensitivity experiments to develop proper assimilation strategies, in which a mixed localisation method is proposed for the first time and found to give further QPN improvement in this typhoon case.

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Articles in this issue

DOI: https://doi.org/10.3402/tellusa.v66.21804 | Journal eISSN: 3035-9554

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Language: English

Page range: 21804 - 21804

Submitted on: Jun 20, 2013

Accepted on: Jan 29, 2014

Published on: Dec 1, 2014

Published by: Stockholm University Press

In partnership with: Paradigm Publishing Services

Keywords:

radar data assimilation,

local ensemble transform Kalman filter,

quantitative precipitation nowcasting,

observing system simulation experiment,

mixed localisation method

© 2014 Chih-Chien Tsai, Shu-Chih Yang, Yu-Chieng Liou, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 66 (2014): Issue 1