Improvement of 6–15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method简

A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs,all at the same forecast valid time.This lagged average forecast （LAF） method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean.Our analyses of the Equitable Threat Score,the Hanssen and Kuipers Score,and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d-1 and 5 mm d-1 in many regions of China,and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members.In particular,significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40％-50％ in the summer season; these regions include northeastern and central to southern China,and the southeastem Tibetan Plateau.     

NUMERICAL ANALYSIS OF THE DIFFERENCES IN DIFFUSION BETWEEN p－SURFACE AND σ－SURFACE

Dimensional analysis and reduction are done to two existing schemes of 4th-order linear horizontal diffusion, and detailed control experiments between them are made using a topographyincluded mesoscale model. Horizontal diffusion is calculated on the or surface in one (known as Scheme A afterwards ), and on the p-surface in another (Scheme B). Experiments show that differences are small in smooth-terrain areas and very large in steep mountain areas, with the 24h rainfall prediction deviating by 50 mm between forecasts of the two sChemes. The reason may be that temperature and humidity are falsely diffused in Scheme A, which causes abnormal temperature and humidity, and results in the anomalies of the unstable layer and convective processes. In addition, Scheme A could also bring about circulation anomalies which assumingly have direct link to the convective anomalies in the scheme. Furthermore, perturbation may also affect surrounding areas by wave-like propagation such that precipitation anomalies may occur in the area. The analysis indicate that Scheme B is necessary and feasible for it minimizes diffusion-involved forecast abnormality in steep mountains and areas around.     

北京地区夏季降水概率预报业务应用研究

作者分析了降水概率预报的基本特征，在北京中尺度数值预报模式（ＢＭＮＦＭ）的基础上应用事件概率回归估计（ＲＥＥＰ）方法，建立北京地区夏季两个量级（≥０．１ｍｍ和≥１０．０ｍｍ）的降水概率预报方程，并结合最新天气观测资料和预报员知识订正客观预报结果。应用Ｂ评分，Ｂｓ评分和Ｂｉａｓ方法对主观降水概率预报和客观降水概率预报进行评估。     

Evaluation of monthly precipitation forecasting skill of the National Multi‐model Ensemble in the summer season

As a test bed, the National Multi‐model Ensemble (NMME) comprises seven climate models from different sources, including the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration, the National Center for Atmospheric Research and the International Research Institute for Climate and Society. It provides 89 ensemble members of precipitation forecasts at different lead times. Precipitation forecasting from climate models has been applied to provide streamflow forecasts, and its utility in water resource system operation has been demonstrated in the literature. In this study, 1‐month‐ahead precipitation forecasts from NMME are evaluated for 945 grid points of 1°‐by‐1° resolution over the continental USA using mean square error and rank probability score. The temporal and spatial variabilities of the forecasting skill over different months of the summer season are discussed. The relation between forecasting uncertainty and observed precipitation is investigated. Such analyses have implications for monthly operational forecasts and water resource management at the watershed scale. Copyright © 2013 John Wiley & Sons, Ltd.     

Exploratory analysis of statistical post‐processing methods for hydrological ensemble forecasts

Despite many recent improvements, ensemble forecast systems for streamflow often produce under‐dispersed predictive distributions. This situation is problematic for their operational use in water resources management. Many options exist for post‐processing of raw forecasts. However, most of these have been developed using meteorological variables such as temperature, which displays characteristics very different from streamflow. In addition, streamflow data series are often very short or contain numerous gaps, thus compromising the estimation of post‐processing statistical parameters. For operational use, a post‐processing method has to be effective while remaining as simple as possible. We compared existing post‐processing methods using normally distributed and gamma‐distributed synthetic datasets. To reflect situations encountered with ensemble forecasts of daily streamflow, four normal distribution parameterizations and six gamma distribution parameterizations were used. Three kernel‐based approaches were tested, namely, the ‘best member’ method and two improvements thereof, and one regression‐based approach. Additional tests were performed to assess the ability of post‐processing methods to cope with short calibration series, missing values or small numbers of ensemble members. We thus found that over‐dispersion is best corrected by the regression method, while under‐dispersion is best corrected by kernel‐based methods. This work also shows key limitations associated with short data series, missing values, asymmetry and bias. One of the improved best member methods required longer series for the estimation of post‐processing parameters, but if provided with adequate information, yielded the best improvement of the continuous ranked probability score. These results suggest guidelines for future studies involving real operational datasets. Copyright © 2014 John Wiley & Sons, Ltd.     

卫星导风在台风路径预报中的应用进展

台风作为全球危害巨大的自然灾害,对其路径预报的准确度尤为重要.在常规观测资料稀少的热带洋面上,卫星云图是监测热带气旋的主要工具,其中卫星导风技术,通过对云及晴空区特征的追踪,反演出的卫星风矢,在一定程度上弥补了海洋上由于风场观测资料稀缺对台风路径研究的限制,显著提高了台风路径预报水平.结合近年来逐步成熟的卫星导风中的云导风技术在台风预报上的应用,总结了国内云导风技术在改善台风路径预报中的贡献,如加密观测资料、台风中心定位、优化数值模式初始场和客观分析场、构造人造台风Bogus模型以及由其揭示的发展和不发展热带气旋的对流层上部环流特征,同时展望了卫星导风技术及其资料在台风预报实际应用中有待进一步研究的内容.     

决策级融合技术在环渤海地区短期天气预报中的应用

设计一种基于决策级融合的短期天气预报二级决策模型,并以线性回归法、极值剔除法、支持向量机法为初级决策方法,采用常规气象资料对该模型进行了系统实现和效果检验。结果表明,该预报模型的决策级融合结果取得了较高的平均历史拟合率和试报准确率,较初级决策方法平均提升约2%—3%;决策级融合的决策结果将具有更高的可信度,其有利于提升数理统计预报的应用水平,促进信息化气象保障系统的建设。     

数值天气预报检验方法研究进展

数值天气预报检验是改进及应用数值模式的重要环节。近年来,模式检验中的观念不断更新,适用于不同预报产品及不同用户需求的模式检验方法也不断涌现。首先简单回顾了以列联表为基础的传统的模式检验方法。其次重点总结了伴随高分辨率数值预报而出现的空间诊断检验技术,按照检验目的的不同,诊断方法可以归纳为:①基于滤波技术的分辨模式在不同时空尺度上预报能力的邻域法、尺度分离法;②利用位移偏差诊断模式预报位置、面积、方位、轴角等与观测差异的属性判别法、变形评估法。然后阐述了集合样本成员的概率分布函数(PDF)、集合预报与观测概率分布函数相似程度、事件发生的概率预报等集合预报检验方法。最后论述了空间诊断技术、集合预报检验方法的适用领域,并讨论了模式检验中存在的一些问题及未来的发展方向。     

用一种动力－统计方法提高中国夏季降水的季节预报

A dynamical-statistical post-processing approach is applied to seasonal precipitation forecasts in China during the summer. The data are ensemble-mean seasonal forecasts in summer (June-August) from four atmospheric general circulation models (GCMs) in the second phase of the Canadian Historical Forecasting Project (HFP2) from 1969 to 2001. This dynamical-statistical approach is designed based on the relationship between the 500 geopotential height (Z500) forecast and the observed sea surface temperature (SST) to calibrate the precipitation forecasts. The results show that the post-processing can improve summer precipitation forecasts for many areas in China. Further examination shows that this post-processing approach is very effective in reducing the model-dependent part of the errors, which are associated with GCMs. The possible mechanisms behind the forecast's improvements are investigated.     

A comparison study of the methods of conditional nonlinear optimal perturbations and singular vectors in ensemble prediction

The authors apply the technique of conditional nonlinear optimal perturbations (CNOPs) as a means of providing initial perturbations for ensemble forecasting by using a barotropic quasi-geostrophic (QG) model in a perfect-model scenario. Ensemble forecasts for the medium range (14 days) are made from the initial states perturbed by CNOPs and singular vectors (SVs). 13 different cases have been chosen when analysis error is a kind of fast growing error. Our experiments show that the introduction of CNOP provides better forecast skill than the SV method. Moreover, the spread-skill relationship reveals that the ensemble samples in which the first SV is replaced by CNOP appear superior to those obtained by SVs from day 6 to day 14. Rank diagrams are adopted to compare the new method with the SV approach. The results illustrate that the introduction of CNOP has higher reliability for medium-range ensemble forecasts.