An Investigation of the Sensitivity of Predicting a Severe Rainfall Event in Northern Taiwan to the Upstream Condition with a WRF-based Radar Data Assimilation System

Abstract

This study investigates the forecast sensitivity of an afternoon thunderstorm in northern Taiwan to the upstream condition associated with the prevailing warm and moist southwesterly winds on 16 June 2008. This event was initiated near noon and lasted for several hours with a maximum hourly precipitation rate of 69 mm hr −1 at 14 LST. Experiments are conducted to assimilate radial velocity only or both radial velocity and reflectivity data from radars at southwestern and southern Taiwan with the WRF-Local Ensemble Transform Kalman Filter Radar assimilation system. Results show that these experiments can predict the rainfall occurrence in northern Taiwan, but the location and rainfall amount is very sensitive to upstream environmental conditions. Assimilating the unfiltered topography-associated reflectivity noise upstream generates unrealistic light rain and cooling, which leads to a great reduction of rainfall in the target area. The precipitation prediction suggests that a careful topography-based quality control performed on the radar data can be essential to restore the necessary environmental conditions for forecasting the afternoon thunderstorm event.

Including observation error correlation for ensemble radar radial wind assimilation and its impact on heavy rainfall prediction

“…The observation errors in the WLRAS are 5 dB Z and 3 ms −1 for $$$ {Z}_h $$$ and $$$ {V}_r $$$, respectively (Tsai et al ., 2014). This setting has been suggested to perform skilfully at radar assimilation and very short‐term precipitation prediction (Cheng et al ., 2020; Wu et al ., 2020; Yang et al ., 2020).…”

“…The observation errors in the WLRAS are 5 dBZ and 3 ms −1 for Z h and V r , respectively (Tsai et al, 2014). This setting has been suggested to perform skilfully at radar assimilation and very short-term precipitation prediction (Cheng et al, 2020;Wu et al, 2020;. The superobbing approach (Lindskog et al, 2004;Zhang et al, 2009) is applied to the observations after performing the QC procedures.…”

“…consists of 50 vertical levels with a top pressure of 50 hPa. The physical parameterizations follow Cheng et al (2020) and are the same for all experiments. All experiments are initialized from the same 42 ensemble members.…”

“…Both 5 km‐FULL and 3 km‐FULL use the settings for the required condition number ($$$ {\kappa}_{req} $$$ = 10,000) and $$$ {L}_r $$$= 25 km, which is based on the results in Section 3.2. The configuration of the 5 km by $$$ 5{}^{\circ} $$$ superobservation has been used in previous studies with WLRAS (Tsai et al ., 2014; Cheng et al ., 2020; Wu et al ., 2020), and thus, 5 km‐DIAG is considered the baseline. The impact of correlated error is first investigated by comparing 5 km‐DIAG and 5 km‐FULL, while the comparison between 3 km‐FULL and 3 km‐DIAG allows us to examine the effect of introducing error correlations for the assimilation of higher spatial resolution data.…”

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

“…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 2019Cheng et al , 2020.…”