预报因子 meaning in English
forecaster
predictor
Examples
- The northeast japan is the " key region " in which the soohpa height fields of the preceding winter influence spring rainfall in the south shandong province , hence it can
冬季日本东北部500hpa高度场升高(降低) ，春季山东南部降水偏多(少) ，因此冬季日本东北部50ohpa高度场可以作为预测山东春季降水的前期预报因子。 - The analysis of the meteorological and hydrological data shows that there is close correlation between the water level of the xijiang river and the upper reach water level and areal mean rainfall
摘要根据气象和水文资料，以上游面雨量、水位值为预报因子，以西江流域的梧州水位为预报量，发现预报因子与预报量有很好的相关性。 - By using predictor puffing method , this paper calculated and analyzed the anomalies of 74 atmospheric circulation characteristics during various periods which were puffed from last january to april of this year
摘要选择上年1月至当年4月为预报时段，采用预报因子膨化技术，将大气环流特征量按月依次组合成不同时段，计算出不同膨化时段的74项大气环流特征量距平值。 - Evidence suggests that the prognostic ability of the new model with high stability , when hidden nodes changing nearby input nodes and training times changing at the certain extent , is significantly better than traditional step wise regression model mainly due to the new model condensing the more forecasting information , properly utilizing the ability of ann self - adaptive learning and nonlinear mapping . but the linear regression technique only selects several predictors by the f value , many predictors information with high relative coefficients is not included . so the new model proposed in this paper is effective and is of a very good prospect in the atmospheric sciences fields
进一步深入分析研究发现，本文提出的这种基于主成分的神经网络预报模型，预报精度明显高于传统的逐步回归方法，其主要原因是这种新的预报模型集中了众多预报因子的预报信息，并有效地利用了人工神经网络方法的自组织和自适应的非线性映射能力；而传统的逐步回归方法是一种线性方法，并且逐步回归方法只是根据f值大小从众多预报因子中选取几个预报因子，其余预报因子的预报信息被舍弃。 - By analyzing the rainfall data of 20 hydrological stations in the miyun reservoir basin from 1970 to 1993 , the relationship between 45 heavy rainfall events and synoptic situations , nwf outputs , the forecast indexes and synoptic patterns are put forward , and 24 - hour heavy rain forecast equations of june , july , august in the miyun reservoir basin are developed
通过整理1970 - 1993年24年间水库流域内20个水文站雨量资料，分析45个暴雨天气样本与历史天气形势和数值预报产品的关系，筛选出预报指标和预报因子，使用数值预报产品的解释应用方法，根据天气环流形势的分型，分别组建了6 、 7 、 8月每个月份的未来24小时暴雨天气预报方程。