首页|MODE降水检验评价指标改进及卷积半径应用

MODE降水检验评价指标改进及卷积半径应用

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基于对象的诊断检验方法(MODE)受降水临界值、卷积半径、属性权重等参数的影响,合理选取卷积半径并准确表征预报场与观测场之间的空间相似度决定了 MODE的应用效果.本文基于2020年夏季贵州54个降水个例,以多源融合降水(CMPA)作为实况,使用MODE和FSS评分(Fractions Skill Score)对中国气象局广东快速更新同化数值预报系统(CMA-GD)24 h日降水预报进行空间检验.结果表明:卷积半径过小易造成MODE提取降水对象过多,而卷积半径过大则导致局部降水信息丢失,无法从降水场中提取到降水对象.不同卷积半径下计算的最大相似度中值(MMI)存在突变.在MMI基础上引入面积权重构造面积平均最大相似度(AMMI).AMMI不受提取降水对象个数的影响,较MMI更具有稳定性,用于表征降水场之间的整体空间相似程度更为合理.根据对象总面积随卷积半径的变化将降水分为大范围降水和局部降水2类.大范围降水平均总面积随着卷积半径的增加而增加,AMMI随卷积半径变化不大.随着卷积半径的增加,局部降水平均总面积减小,平均AMMI有所减小.局部降水对卷积半径选取较为敏感,以观测场对象面积变化不超过10%的最大半径作为卷积半径有助于保留降水场大部分信息.
Improvement of MODE Precipitation Evaluation Index and Application of Convolution Radius
The Method for Object-Based Diagnostic Evaluation(MODE)has been widely applied in spatial evaluation in recent years.MODE is affected by many parameters such as precipitation critical value,convolution radius and attribute weight;the application effect of MODE depends on reasonable selection of convolution radius and accurate characterisation of spatial similarity between forecast and observation fields.Taking the CMPA as observation,MODE and FSS(Fractions Skill Score)are used to test the CMA-GD 24 h daily precipitation forecast based on 54 precipitation cases in Guizhou in this paper.The number of objects extracted by MODE falls with convolution radius;too small convolution radius easily results in too many precipitation objects extracted;if the convolution radius is too large,local precipitation information will be lost and precipitation objects cannot be extracted from the precipitation field.Therefore,an appropriate convolution radius should be adopted to extract precipitation objects with MODE.It is found that the MMI(the Median Maximum Interest Value)of MODE is very sensitive to the convolution radius change and even has a mutation,so it cannot stably represent the overall spatial similarity of precipitation fields.Based on the MMI,the area weight is introduced to construct the AMMI(the Area Mean of Maximum Interest Value)to distinguish the contribution of different objects.The AMMI is more reasonable to characterise the overall similarity of the forecast and observation precipitation fields,and is unaffected by the number of precipitation objects,which is more stable than the MMI.In general,AMMI is larger than FSS,and the difference in the change of AMMI and FSS with spatial scale is due to the different calculation basis.According to the change of object's total area with the convolution radius,precipitation can be divided into large-area precipitation and local precipitation.The average total area of large-scale precipitation grows with the convolution radius,while AMMI has little change.As the convolution radius goes up,the average total area and AMMI of local precipitation go down.Taking the maximum convolution radius which makes the total area change not exceeding 10%in the observation field as the critical radius,there is a large difference between the probability of the critical radius of large-scale precipitation and local precipitation from 0.05° to 0.4°.Local precipitation is sensitive to the selection of convolution radius and determining the convolution radius with critical radius is helpful to retain most of the information of the precipitation field.

precipitationMODEspatial evaluationparameter

杨富燕、陈百炼、彭芳、胡欣欣、李彦霖

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贵州省山地环境气候研究所,贵阳 550002

贵州省山地气候与资源重点实验室,贵阳 550002

贵州省气象台,贵阳 550002

降水 MODE 空间检验 参数

国家自然科学基金中国气象局复盘总结专项广东省区域数值天气预报重点实验室开放基金

42165001FPZJ2023-117J201802

2024

气象科技
中国气象科学研究院 北京市气象局 中国气象局大气探测技术中心 国家卫星气象中心 国家气象信息中心

气象科技

CSTPCD
影响因子:1.154
ISSN:1671-6345
年,卷(期):2024.52(2)
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