耦合降水集合预报信息与水文模型是水文预报发展的一个重要方向。然而由于大气运行初始条件及模式的 不确定性数值降雨预报不可避免地存在误差。基于全球集合预报系统( GFS) 提供的 1~ 8 d 预见期的降雨集合预 报数据, 研究了基于扩展型 Log istic 算法和异方差扩展型Log istic 算法发展的5 个统计后处理模型对淮河流域息县 子流域 GFS 预报降雨的校正效果。结果表明, 5 个模型对 GFS 预报降雨均具有较好的校正效果, 但随着预见期的 增长, 各个模型的校正能力呈衰减趋势。总体而言, 相较于基于扩展型 Lo gist ic 算法的 3 个模型, 基于异方差扩展 型 Lo gist ic 算法的 2 个模型具有更优的校正能力。

Coupling ensemble pr ecipitatio n predictio n w ith hydro lo gical mo dels is an impo rtant develo pment direction o f hydro2 log ical fo recasting. Howev er, due to the uncertaint y of the initial atmo spheric conditions and model physics, numerical pr ecipita2 tion for ecasts inevitably hav e errors. In this study , based on GFS ensemble precipitation r efor ecasts with a 1282day lead time, w e analyzed five statistical po st2pr ocessing models that wer e developed based on extended log istic reg ressio n ( ELR) and heterosce2 dastic ext ended lo gistic r egr essio n ( H ELR) alg orithms, and compared t heir co rr ect ion effects on the GFS ensemble precipitatio n forecasts in Xix ian sub2basin of H uai river basin. T he r esults indicated that these five models all made significant impro vements to the GFS raw for ecast; but w ith the ex tensio n o f t he lead time, their correction effects tended to attenuate. In g ener al, the tw o HELR2based models had better perfo rmance compar ed to the other thr ee ELR2based models.

国家自然科学基金( 41730750; 51709073) ; 江苏省自然科学基金( BK20170878)