短期集合降水概率预报试验

以MM5模式作为试验模式, 通过选取不同的物理过程参数化方案产生8个集合成员, 分别用平均法、相关法和Rank法对2001年11月至2002年5月期间的22个降水个例进行短期集合降水概率预报试验。试验结果显示对小雨—大暴雨6类降水的概率预报, Rank法的综合预报效果明显好于相关法和平均法, 相关法的综合预报效果与平均法基本相同; 无论从均方误差角度还是从命中率和假警报率的相对大小角度, 对小雨、中雨、大雨和暴雨各量级以上降水的概率预报, Rank法的平均预报效果是三种方法中最好的, 相关法的平均预报效果与平均法相同; Rank法好于平均法的平均幅度从均方误差角度较大, 从命中率和假警报率的相对大小角度则较小。平均而言, 三种方法对各量级以上降水的概率预报都是有技巧预报, 对量级小的降水的概率预报技巧高于对量级大的降水的概率预报技巧。     

Targeting seasonal climate forecasts for integration into household level decisions: the case of smallholder farmers in Lesotho

Seasonal climate forecasts have been promoted as a means to increase the resilience of marginal groups in Africa. The manifestations of this are still to be seen. This paper argues that successful dissemination and adoption of the forecast requires an in-depth profile of the characteristics and needs of user groups. The case study of a mountainous village in southern Lesotho is used to highlight the decisions which one group of marginal users – smallholder farmers – might make in response to the forecast. A participatory role-play exercise explores what information households presently receive and how new climate forecast information could be integrated into seasonal decision making. Results show that there are a number of low-input options available to these farmers for responding to the forecast. Adoption, however, is going to require repeated exposure to the forecast in conjunction with forecast development that is suited to users' needs. The case study is linked back to the larger scale by suggesting paths that seasonal climate forecast development could take if it is to contribute to improving livelihood sustainability among marginal groups.     

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts

Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.     

Cartograms for Use in Forecasting Weather-Driven Natural Hazards

This paper evaluates the potential of using cartograms for visualizing and interpreting forecasts of weather-driven natural hazards in the context of global weather forecasting and early warning systems. The use of cartograms is intended to supplement traditional cartographic representations of the hazards in order to highlight the severity of an upcoming event. Cartogrammetric transformations are applied to forecasts of floods, heatwaves, windstorms and snowstorms taken from the European Centre for Medium-range Weather Forecasts (ECMWF) forecast archive. Key cartogram design principles in standard weather forecast visualization are tested. Optimal cartogram transformation is found to be dependent on geographical features (such as coastlines) and forecast features (such as snowstorm intensity). For highly spatially autocorrelated weather variables used in analysing several upcoming natural hazards such as 2m temperature anomaly, the visualization of the distortion provides a promising addition to standard forecast visualizations for highlighting upcoming weather-driven natural hazards.     

基于频率匹配法的中国降水多模式预报订正研究

基于TIGGE资料中欧洲中期天气预报中心、日本气象厅、美国国家环境预报中心及英国气象局1～7 d日降水量预报以及中国自动站观测资料与CMORPH降水产品融合的逐时降水量网格数据集,利用频率匹配法(Frequency-Matching Method,FMM)对中国降水预报进行客观订正。首先利用卡尔曼滤波方法对降水频率进行调整,并根据不同区域降水强度差异将全国分为7个子区域分别进行频率匹配。结果表明,FMM可以有效减小降水量预报的误差。经过频率匹配法订正后各模式降水预报的平均绝对误差(Mean Absolute Error,MAE)大幅减小,且订正后各量级降水的雨区面积更加接近实际观测值。FMM对小于5 mm和大于15 mm的降水预报技巧改进明显。此外,FMM降低了模式预报的小雨空报率和大雨漏报率,并且明显提高了晴雨预报的准确率。FMM明显消除了大范围小雨空报区域,但是对强降水预报FMM仅能调整降水量大小,强降水落区预报并不能得到明显改善。     

加强目标观测,服务防灾减灾

以深入浅出的方式介绍了目标观测的定义、历史、确定目标观测敏感区的方法,以及实施目标观测与防灾减灾的关系;并以欧洲中期天气预报中心目标观测业务化为例,简明地概括了目标观测的实施过程;结合国内目标观测研究现状,对中国目标观测的未来提出了看法。     

A comparison of state space and multiple regression for monthly forecasts: U.S. Fuel consumption

Monthly consumption forecasts for U.S. oil, natural gas, and coal are made using state space and multiple regression applied to the same data. These forecasts are compared with actual consumption for a test period. The forecasts made using state space are preferred to those made using multiple regression models for both expost and exante cases. The state space forecasts track data cycles better than do the regression forecasts. Average absolute forecast errors are less for the state space models than they are for the multiple regression models.     

1991 年江淮暴雨的定量预报检验

利用Threat Score方法，对1991年6月12—15日，6月29日—7月12日江淮持续暴雨的定量预报进行了检验。结果表明，对于大雨和暴雨预报，目前主观预报优于客观预报。数值预报在降雨定量预报的某些方面有了一定突破。     

STUDY OF THE POSSIBILITIES OF USING AN AIR MASS TRANSFORMATION MODEL IN TAIYUAN

An AMT-model,consisting of a trajectory model and a one-dimensional boundary layer model,is tested fortrajectories arriving in Taiyuan to study the possibility of using it in Taiyuan.The sensitivity of the model tothe different processes was studied.Some parameters of the model were modified for the purpose of forecast-ing in specific mountainous terrain and dry climate conditions.Results of examples which we have workedout for Taiyuan circumstances for the periods of July(summer)1985 and January(winter)1986,show that the12h runs of the AMT-model are able to reproduce(on historical data)the sounding of Taiyuan.The AMT-modelcontributes fruitfully to short-range weather forecasts(12—36h ahead)during periods of severe air pollution andwhen cold waves occur.