Convective-Scale Sampling Error and Its Impact on the Ensemble Radar Data Assimilation System: A Case Study of a Heavy Rainfall Event on 16 June 2008 in Taiwan

Pin-Ying Wu,Shu-Chih Yang,Hsiang-Wen Cheng,Chih-Chien Tsai

doi:10.1175/mwr-d-19-0319.1

Abstract

ABSTRACT Sampling error stems from the use of ensemble-based data assimilation (EDA) with a limited ensemble size and can result in spurious background error covariances, leading to false analysis corrections. The WRF-LETKF radar assimilation system (WLRAS) is performed separately with 256 and 40 members to investigate the characteristics of convective-scale sampling errors in the EDA and its impact on precipitation prediction based on a heavy rainfall event on 16 June 2008. The results suggest that the sampling errors for this event are sensitive to the relationships between the simulated observations and model variables, the intensity of reflectivity, and how the prevailing wind projects to the radial wind in the areas that the radar cannot resolve U or V wind. The sampling errors lead to an underprediction of heavy rainfall when the horizontal localization radius is inadequately large, but this can be mitigated when a more accurate moisture condition is provided. In addition, being able to use a larger vertical localization plays an important role in providing necessary adjustments for representing the vertical thermodynamical structure of convection, which further improves precipitation prediction. A strategy mitigating the impact of sampling errors associated with the limitation of radial wind measurement by inflating the observation error over sensitive areas can bring benefits to precipitation prediction.

Highlights

Data assimilation (DA) methods, which estimate the optimal initial condition for numerical modeling, synergize the information provided by numerical model simulation and observations and have becomeDenotes content that is immediately available upon publication as open access.an important component for numerical weather prediction (NWP)
With the purpose of improving very short-term (,6 h) rainfall forecasting termed as quantitative precipitation nowcasting (QPN), an ensemble-based radar data assimilation (EnRDA) framework, which coupled the local ensemble transform Kalman filter (LETKF) with the Weather Research and Forecasting (WRF) Model, was established to assimilate data from the radar network in Taiwan (Tsai et al 2014, hereafter TYL14)
This study aims to investigate the characteristics of sampling errors accompanied by the EnRDA and how they affect the convective-scale background error correlation (BECR) and the performance of very short-term precipitation prediction

Summary

Introduction

Data assimilation (DA) methods, which estimate the optimal initial condition for numerical modeling, synergize the information provided by numerical model simulation and observations and have becomeDenotes content that is immediately available upon publication as open access.an important component for numerical weather prediction (NWP). With the purpose of improving very short-term (,6 h) rainfall forecasting termed as quantitative precipitation nowcasting (QPN), an EnRDA framework, which coupled the local ensemble transform Kalman filter (LETKF) with the Weather Research and Forecasting (WRF) Model, was established to assimilate data from the radar network in Taiwan (Tsai et al 2014, hereafter TYL14). This WRF-LETKF radar assimilation system (WLRAS) has been demonstrated its skills for heavy rainfall predictions for cases of typhoons and heavy precipitation episodes (Tsai et al 2016; Yang et al 2020; Cheng et al 2019, 2020)

Objectives

Conclusion