Impact of Synoptic‐Scale Factors on Rainfall Forecast in Different Stages of a Persistent Heavy Rainfall Event in South China

Abstract

This study investigates the stage‐dependent rainfall forecast skills and the associated synoptic‐scale features in a persistent heavy rainfall event in south China, Guangdong Province, during 29–31 March 2014, using operational global ensemble forecasts from the European Centre for Medium‐Range Weather Forecasts. This persistent rainfall was divided into two stages with a better precipitation forecast skill in Stage 2 (S2) than Stage 1 (S1) although S2 had a longer lead time. Using ensemble‐based sensitivity analysis, key synoptic‐scale factors that affected the rainfall were diagnosed by correlating the accumulated precipitation of each stage to atmospheric state variables in the middle of respective stage. The precipitation in both stages was found to be significantly correlated with midlevel trough, low‐level vortex, and particularly the low‐level jet on the southeast flank of the vortex and its associated moisture transport. The rainfall forecast skill was mainly determined by the forecast accuracy in the location of the low‐level jet, which was possibly related to the different juxtapositions between the direction of the movement of the low‐level vortex and the orientation of the low‐level jet. The uncertainty in rainfall forecast in S1 was mainly from the location uncertainty of the low‐level jet, while the uncertainty in rainfall forecast in S2 was mainly from the width uncertainty of the low‐level jet with the relatively accurate location of the low‐level jet.

Practical and Intrinsic Predictability of a Warm‐Sector Torrential Rainfall Event in the South China Monsoon Region

“…Ensemble‐based sensitivity analysis (ESA; Ancell & Hakim ; Torn & Hakim, ; Hakim & Torn, ) is a new and efficient tool for gaining quantitative insights and estimating the forecast sensitivity to the thermodynamic ICs or earlier forecast times. More recently, the use of ESA has been investigated for convection‐permitting forecasting in the southern Great Plains region (Bednarczyk & Ancell, ; Hill et al, ), in MPEX (Torn & Romine, ; Weisman et al, ), and in China (Yu & Meng, ; Zhang & Meng, ). In the present study, ESA was applied to illustrate the forecast sensitivities of the CI of coastal warm‐sector torrential rainfall to the thermodynamic conditions at earlier forecast times.…”

Persistent precipitation extremes in the Yangtze River Valley prolonged by opportune configuration among atmospheric teleconnections

“…In particular, in populous monsoonal regions like eastern China, damage caused by PPEs is further exacerbated by extensive spatial coverage of the monsoonal rain band and high exposure of local population and infrastructure (Sun et al., ; Li and Mao, ). However, current accuracy and forecast lead time for PPE prediction are far from satisfactory for decision‐making (Joseph et al., ; Zhao et al., ; Zhang and Meng, ). This is mainly due to the lack of understanding about determinants for the intensity and duration of PPEs, and consequently ill‐representations of relevant physical processes in numerical models (Zhou and Zhai, ; Zhao et al., ; Blanchet et al., ; Zhou et al., ).…”

Ensemble Sensitivity Analysis of Heavy Rainfall Associated With Three MCSs Coexisting Over Southern China

“…The differences in the water vapor flux between good members and bad members reached 4 g (cm·hPa·s) −1 at 700–850 hPa, whereas those at 925 hPa were no more than 3 g (cm·hPa·s) −1 . Zhang and Meng () found similar characteristics of low‐level jets that dominantly influenced moisture transport.…”

“…However, in bad members, the axes of LLJ‐B are located more to the east (Figure d), transporting moisture from the South China Sea to the east. The wide distribution of LLJ‐A and LLJ‐B partly accounts for the difficulty in accurately predicting precipitation (Zhang & Meng, ). Furthermore, the complex interaction of the two low‐level jets (LLJ‐A and LLJ‐B) might increase the forecast uncertainty of MCS‐B, resulting in the worse quantitative forecast skill of MCS‐B.…”

“…It should be noted that the ESA method has a hypothesis that the model can describe the dynamic and physical process of precipitation accurately and reasonably. To enlarge the ensemble sample size, two ensemble sets with different initial times of 0000 UTC 19 and 1200 UTC 19 May were combined into a larger ensemble set of 100 members in this study (Du & Chen, ; Zhang & Meng, ). The Pearson correlation coefficient was used to present the relation between the area‐averaged 6‐hr accumulated precipitation (0000–0600 UTC 20 May) in the three MCS regions and various weather variables at 0000 UTC 20 May.…”