Evaluation of quantitative precipitation forecasts by TIGGE ensembles for south China during the presummer rainy season

Abstract

Based on The Observing System Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) data set, this study evaluates the ability of global ensemble prediction systems (EPSs) from the European Centre for Medium‐Range Weather Forecasts (ECMWF), U.S. National Centers for Environmental Prediction, Japan Meteorological Agency (JMA), Korean Meteorological Administration, and China Meteorological Administration (CMA) to predict presummer rainy season (April–June) precipitation in south China. Evaluation of 5 day forecasts in three seasons (2013–2015) demonstrates the higher skill of probability matching forecasts compared to simple ensemble mean forecasts and shows that the deterministic forecast is a close second. The EPSs overestimate light‐to‐heavy rainfall (0.1 to 30 mm/12 h) and underestimate heavier rainfall (>30 mm/12 h), with JMA being the worst. By analyzing the synoptic situations predicted by the identified more skillful (ECMWF) and less skillful (JMA and CMA) EPSs and the ensemble sensitivity for four representative cases of torrential rainfall, the transport of warm‐moist air into south China by the low‐level southwesterly flow, upstream of the torrential rainfall regions, is found to be a key synoptic factor that controls the quantitative precipitation forecast. The results also suggest that prediction of locally produced torrential rainfall is more challenging than prediction of more extensively distributed torrential rainfall. A slight improvement in the performance is obtained by shortening the forecast lead time from 30–36 h to 18–24 h to 6–12 h for the cases with large‐scale forcing, but not for the locally produced cases.

Application of a convection‐permitting ensemble prediction system to quantitative precipitation forecasts over southern China: Preliminary results during SCMREX

“…At 1200 UTC when the quasi‐linear MCS reached maturity (fig. 9a3 of Huang and Luo, ), large EM RMSEs (≥10 mm) were present in the regions with rainfall above 10 mm/h for all the experiments (not shown). In these regions, both the area and magnitude of large ensemble spreads (≥10 mm) in ALL were most consistent with those of large EM RMSEs, among all the experiments (Figure c), whereas the opposite was true for noIDSC, noEDA, noTLA and CTL, all of which had small spreads (Figure a,b).…”

“…During the whole 12 h forecasts, the EM RMSE of the 1 h accumulated rainfall in ALL was the smallest (Figure ). The differences of RMSE among the experiments became more evident for the forecasts beyond 8 h when most heavy rainfall occurred (Huang and Luo, ) than for the whole 12 h forecasts. Thus, including all the perturbations mentioned in section 2.2 led to the highest deterministic QPF skill, especially for heavy rainfall.…”

“…Among these events, those forecasts with initial times of 0000 UTC 8 May and 1200 UTC 10 May were selected to test the rationale behind the design of GM-CPEPS and to demonstrate the GM-CPEPS performance intuitively. Detailed descriptions of these events can be found in L17 and Huang and Luo (2017). This study focuses on the performance of both deterministic and probabilistic guidance of GM-CPEPS for QPFs (Table 1).…”

Cloud Microphysical Factors Affecting Simulations of Deep Convection During the Presummer Rainy Season in Southern China

“…During this period, the region experiences approximately half of its annual accumulated rainfall, due to the frequent occurrence of extreme rainfall associated with organized mesoscale convective systems (MCSs; with horizontal extents of 200–2,000 km and typical lifetimes of about 18 hr), leading to severe flooding that endangers lives and livelihoods (Luo et al, ). There has been steady progress in understanding the mechanisms leading to formation of these heavy‐rain‐producing MCSs (S. S. Huang, ; Luo et al, ; Zhang et al, ; Zhou, ), but the skill of regional precipitation forecasts remains low (particularly for extreme rainfall; L. Huang & Luo, ). This is in part due to a lack of understanding of the microphysical and dynamical drivers that are operating and how well these are represented by numerical weather models (Luo et al, ).…”

The Relationship Between Anomalous Presummer Extreme Rainfall Over South China and Synoptic Disturbances

“…Numerous studies have examined the mechanisms of heavy rainfall production in MCSs and their associated favorable environmental conditions. Some major favorable conditions include quasi‐stationary fronts (Chen, ), low‐level vortices (Chang et al, ; Fu et al, ; Huang & Meng, ; Ninomiya & Akiyama, ; Tao & Ding, ), and low‐level, southwesterly jets (Chen & Yu, ; Chen, Wang, & Lin, , ; Huang & Luo, ; C. C. Wang, Hsu, et al, ). In addition, onshore oceanic flow, surface mesoscale outflow boundary, frictional contrast between land and sea, and coastal orography could all play critical roles in the initiation and development of heavy‐rain‐producing MCSs in the coastal areas of south China (H. Wang, Luo, et al, ; Wu & Luo, ).…”