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国家级格点实况分析产品在江苏地区的适用性评估分析
俞剑蔚1, 李聪2, 蔡凝昊1, 刘梅1, 赵启航3
(1.江苏省气象台;2.南京市气象台;3.江苏省气象局)
Applicability Evaluation of the National Gridded Real-time Observation Datasets in Jiangsu region
Yu Jianwei1, Li Cong2, Cai Ninghao1, Liu Mei1, Zhao Qihang3
(1.Jiangsu Meteorological Observatory;2.Nanjing Meteorological Observatory;3.Jiangsu Meteorological Bureau)
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投稿时间:2018-11-16    修订日期:2019-07-12
中文摘要: 利用国家级格点实况分析资料与地面气象站实况数据,采用误差分析、技巧评分等方法评估了2017年7月~2018年6月逐时的格点实况产品在江苏地区的地面2m气温、2m相对湿度、10m风和降水要素的一致性和准确性,同时采用MODE检验方法对格点降水产品空间分布偏差进行了分析。结果表明:2m气温格点实况与自动站观测基本一致,但仍存在一定的差异,平均绝对误差在0.5~0.8℃之间,均方根误差在0.8℃左右,误差≤2℃的准确率为95%,其中最高气温误差较小。格点实况和自动站2m相对湿度之间的平均绝对误差在5%左右,均方根误差在6~7%之间,误差≤10%的准确率为92%,表现出较高的准确性和稳定性。格点实况10m风向准确率达到70%左右,而风速准确率仅为56%,与气象站点观测相比有明显差异。通过与自动站观测降水相比,格点降水产品的全年有无降水准确率为90%~98%,对于晴雨检验存在带来较大影响的可能。降水分级检验结果表明,格点实况产品对小雨级别降水的准确率最高,随着降水量级增大,格点降水数据准确率显著降低。对大量级降水,格点实况降水场相比站点观测存在较多的降水漏报,因此,对于降水分量级检验还不适合用格点实况场来替代气象站点观测。设计了一种基于空间形态的降水准确率评分方法,对格点实况降水场的降水空间落区进行检验,结果表明格点实况降水场的空间形态准确率评分在0.9左右,较准确的反映了实际降水空间分布。总之,从各气象要素的误差空间分布来看,格点实况数据在江苏平原地区都有较高的精度,误差在可接受的范围内,基本可以代替自动站观测作为预报和模式检验的真实实况场,。但也存在以下几个方面的问题:(1)格点2m气温、2m相对湿度产品在江苏的丘陵地带误差较大,降水产品在海岛气象站准确性较低;(2)格点降水产品一定程度地弱化了大雨以上量级降水强度;(3)格点实况风速产品误差较大,与业务服务需求有一定差距。今后在改进格点实况分析产品性能时,针对以上方面可能要进一步开展工作,从而提高格点实况产品的精度。
Abstract:By using National Gridded Real-time Observation Datasets covering Jiangsu province released by National Meteorological Information Center, compared with automatic stations, the consistency and accuracy of hourly 2M air temperature, 2M relative humidity, 10M wind and precipitation during July 2017 to June 2018 are evaluated in details by error statistical analysis, skill score and other methods. MODE is applied to reveal the spatial deviation of precipitation between gridded products and observed rainfall records. The result indicates that the mean absolute error of 2M temperature is between 0.5 and 0.8℃, the maximum temperature exhibits a better accuracy than the 2M and minimum temperature. The root mean square error range of 2M relative humidity is 6~7%, which means that the gridded 2M temperature and 2M relative humidity data are well consistent with the observed data. The accuracy of the wind direction is about 70% while that of wind speed is only 56%. The verification results of precipitation show that the gridded data performs well on the capability of the rain or no-rain, nonetheless, it may still have a great impact on the precipitation frequency evaluation by using the gridded precipitation product. TS score of light rain is higher than that in other classes, it declines sharply when rainfall magnitude increases. Moderate rain or above have a relatively higher probability of detection, which means that the precipitation event is less detected than observed, and that’s the main reason that affects the skill score. Therefore, for the quantitative precipitation verification, it may be not suitable to replace the observed data by the gridded datasets as actual precipitation. Further study on 24h accumulated rainfall bias between gridded data and observation indicates that the spatial structure of precipitation can be well described by gridded data, the spatial scores of precipitation which is designed in this article is above 0.9. Generally, the gridded data in Jiangsu plateau area can basically replace the automatic stations as the real-time meteorological field for forecast and model verification. However, there are still some problems as follows: (1) 2M temperature and 2M relative humidity have large errors in the hilly areas of Jiangsu province, precipitation product in island stations has a lower accuracy; (2) the intensity of precipitation above heavy rain is weakened by the gridded data; (3) wind speed value is lower than the observations. Further research may be needed to improve the performance of the gridded product.
文章编号:201811160503     中图分类号:    文献标志码:
基金项目:江苏省气象局预报员专项(JSYBY201802)资助
引用文本:
俞剑蔚,李聪,蔡凝昊,刘梅,赵启航,0.[en_title][J].Meteor Mon,():-.
Yu Jianwei,Li Cong,Cai Ninghao,Liu Mei,Zhao Qihang,0.Applicability Evaluation of the National Gridded Real-time Observation Datasets in Jiangsu region[J].Meteor Mon,():-.