Comparative Assessment of Two Objective Forecast Models for Cases of Persistent Extreme Precipitation Events in the Yangtze–Huai River Valley in Summer 2016

Abstract

Two persistent extreme precipitation events (PEPEs) that caused severe flooding in the Yangtze–Huai River valley in summer 2016 presented a significant challenge to operational forecasters. To provide forecasters with useful references, the capacity of two objective forecast models in predicting these two PEPEs is investigated. The objective models include a numerical weather prediction (NWP) model from the European Centre for Medium-Range Weather Forecasts (ECMWF), and a statistical downscaling model, the Key Influential Systems Based Analog Model (KISAM). Results show that the ECMWF ensemble provides a skillful spectrum of solutions for determining the location of the daily heavy precipitation (≥25 mm day−1) during the PEPEs, despite its general underestimation of heavy precipitation. For lead times longer than 3 days, KISAM outperforms the ensemble mean and nearly one-half or more of all the ensemble members of ECMWF. Moreover, at longer lead times, KISAM generally performs better in reproducing the meridional location of accumulated rainfall over the two PEPEs compared to the ECMWF ensemble mean and the control run. Further verification of the vertical velocity that affects the production of heavy rainfall in ECMWF and KISAM implies the quality of the depiction of ascending motion during the PEPEs has a dominating influence on the models’ performance in predicting the meridional location of the PEPEs at all lead times. The superiority of KISAM indicates that statistical downscaling techniques are effective in alleviating the deficiency of global NWP models for PEPE forecasts in the medium range of 4–10 days.

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

“…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., ). Hence, deeper understanding of thermodynamic–dynamic mechanisms for PPEs is fundamental to improve the prediction with respect to PPEs.…”

Explaining Rainfall Accumulations over Several Days in the French Alps Using Low-Dimensional Atmospheric Predictors Based on Analogy

“…Application of the analog method to sequences of synoptic situation is much less developed. Matulla et al (2008) and Zhou and Zhai (2016) downscale precipitation at a given target day from the state of the atmosphere on that day and the preceding 7 days. The main motivation for looking at sequences is that the localscale variable in a given day may also result from the atmospheric states of the antecedent days.…”

“…The main motivation for looking at sequences is that the localscale variable in a given day may also result from the atmospheric states of the antecedent days. They evidence some benefit in accounting for antecedent days for estimating persistent ordinary features such as dry and wet spells in California and Austria (Matulla et al 2008) or persistent large precipitation (exceeding the 95% quantile) in China (Zhou and Zhai 2016). Zhou et al (2018) also consider the analog method accounting for precedent days to forecast two persistent extreme precipitation events that caused severe flooding in the Yangtze-Huai River valley (China) in summer 2016.…”

“…They evidence some benefit in accounting for antecedent days for estimating persistent ordinary features such as dry and wet spells in California and Austria (Matulla et al 2008) or persistent large precipitation (exceeding the 95% quantile) in China (Zhou and Zhai 2016). Zhou et al (2018) also consider the analog method accounting for precedent days to forecast two persistent extreme precipitation events that caused severe flooding in the Yangtze-Huai River valley (China) in summer 2016. They show the method outperforms the ensemble mean and about half the ensemble members of a numerical weather model for lead times longer than 3 days.…”

Anomalous Circulation Patterns in Association with Summertime Regional Daily Precipitation Extremes over Northeast China

“…In arid regions of China, all the datasets show a positive correlation between extreme precipitation index and SST in the equatorial eastern Pacific; in humid regions of China, however a more complicated relationship is found between extreme precipitation index and SST. Zhou et al [18] compared and evaluated two objective prediction models for their simulations of the persistent rainfall event occurred in the Yangtze-Huaihe River Valley in the summer of 2016. Based on the CMIP5 global models, Li et al [19] and Wu et al [20] analyzed the characteristics of extreme precipitation index simulated by the models and evaluated the performance of these models on the simulation of extreme precipitation.…”