概率预报是由集合预报衍生、包含不确定性信息的客观产品,对业务决策服务有重要的参考价值。传统的邻域集合概率法中,邻域半径固定不变,不符合实际天气过程中牵涉甚广的尺度谱。为此引入基于集合匹配尺度的邻域集合概率法(Neighborhood ...概率预报是由集合预报衍生、包含不确定性信息的客观产品,对业务决策服务有重要的参考价值。传统的邻域集合概率法中,邻域半径固定不变,不符合实际天气过程中牵涉甚广的尺度谱。为此引入基于集合匹配尺度的邻域集合概率法(Neighborhood Ensemble Probability based on Ensemble Agreement Scale,EAS_NEP),并在中国南方典型的梅雨锋暴雨中开展准确性和预报技巧的定量检验评估,以期验证该方法在此类过程中的适用性,并促进其在实际业务中的推广使用。联合扰动初始场、侧边界和物理过程所得到的集合预报能较好地表征实际的预报不确定性,进一步在此基础上比较了格点概率法、不同半径的邻域集合概率法以及EAS_NEP的优劣。试验结果表明,EAS_NEP能根据集合成员间的一致性程度,自适应地调整邻域半径,其在集中型降水中所确定的邻域半径通常大于分散型降水。动态调整的邻域半径既避免了半径过大时的过度平滑与关键信息丢失,又消除了半径较小所带来的奇异点,其空间分布呈阶梯型,空间连续性更优。此外,BS(布莱尔评分)、FSS(分数技巧评分)和ROC曲线(相对作用特征曲线)等定量评估结果也体现出EAS_NEP相比传统方法正的预报技巧,尤其是在分散型降水和高阈值检验时优势更明显。以上结果表明,EAS_NEP在梅雨锋暴雨的预报中具有较好的应用前景,运用在业务中能有效提升概率预报质量。展开更多
Erceg in [1] extended the Hausdorff distance function betwen subsets of a set X to fuzzy settheory, and introduced a fuzzy pseudo-quasi-metric(p. q. metric) p: L^x xL^x -[0,∞] where L is acompletely distributive latt...Erceg in [1] extended the Hausdorff distance function betwen subsets of a set X to fuzzy settheory, and introduced a fuzzy pseudo-quasi-metric(p. q. metric) p: L^x xL^x -[0,∞] where L is acompletely distributive lattice, and the associated family of neighborhood mappings {Dr |r>O}. Thefuzzy metric space in Erceg's sense is denoted by (L^x, p, Dr) since there exists a one to one correspondence between fuzzy p. q. metrics and associatcd families of neighborhood mapping, a family{Dr|r>0}satisfying certain conditions is also callcd a (standard) fuzzy p. q metric. In [2] Liang defimed apointwise fuzzy p. q. metric d: P(L^x)×P(L^x)-[0, ∞) and applicd it to the construction of theproduct fuzzy metric. In this paper, we give axiomatic definitions of molcculewise and展开更多
文摘概率预报是由集合预报衍生、包含不确定性信息的客观产品,对业务决策服务有重要的参考价值。传统的邻域集合概率法中,邻域半径固定不变,不符合实际天气过程中牵涉甚广的尺度谱。为此引入基于集合匹配尺度的邻域集合概率法(Neighborhood Ensemble Probability based on Ensemble Agreement Scale,EAS_NEP),并在中国南方典型的梅雨锋暴雨中开展准确性和预报技巧的定量检验评估,以期验证该方法在此类过程中的适用性,并促进其在实际业务中的推广使用。联合扰动初始场、侧边界和物理过程所得到的集合预报能较好地表征实际的预报不确定性,进一步在此基础上比较了格点概率法、不同半径的邻域集合概率法以及EAS_NEP的优劣。试验结果表明,EAS_NEP能根据集合成员间的一致性程度,自适应地调整邻域半径,其在集中型降水中所确定的邻域半径通常大于分散型降水。动态调整的邻域半径既避免了半径过大时的过度平滑与关键信息丢失,又消除了半径较小所带来的奇异点,其空间分布呈阶梯型,空间连续性更优。此外,BS(布莱尔评分)、FSS(分数技巧评分)和ROC曲线(相对作用特征曲线)等定量评估结果也体现出EAS_NEP相比传统方法正的预报技巧,尤其是在分散型降水和高阈值检验时优势更明显。以上结果表明,EAS_NEP在梅雨锋暴雨的预报中具有较好的应用前景,运用在业务中能有效提升概率预报质量。
文摘Erceg in [1] extended the Hausdorff distance function betwen subsets of a set X to fuzzy settheory, and introduced a fuzzy pseudo-quasi-metric(p. q. metric) p: L^x xL^x -[0,∞] where L is acompletely distributive lattice, and the associated family of neighborhood mappings {Dr |r>O}. Thefuzzy metric space in Erceg's sense is denoted by (L^x, p, Dr) since there exists a one to one correspondence between fuzzy p. q. metrics and associatcd families of neighborhood mapping, a family{Dr|r>0}satisfying certain conditions is also callcd a (standard) fuzzy p. q metric. In [2] Liang defimed apointwise fuzzy p. q. metric d: P(L^x)×P(L^x)-[0, ∞) and applicd it to the construction of theproduct fuzzy metric. In this paper, we give axiomatic definitions of molcculewise and
