潘旸,谷军霞,宇婧婧,沈艳,师春香,周自江. 2018. 中国区域高分辨率多源降水观测产品的融合方法试验[J]. 气象学报, 76(5):755-766, doi:10.11676/qxxb2018.034
中国区域高分辨率多源降水观测产品的融合方法试验
Test of merging methods for multi-source observed precipitation products at high resolution over China
投稿时间:2017-05-02  修订日期:2018-04-20
DOI:10.11676/qxxb2018.034
中文关键词:  多源降水融合方法  高分辨率降水产品  独立检验
英文关键词:Multi-source precipitation merged method  High-resolution precipitation product  Independent validation
基金项目:国家国际科技合作专项项目(2011DFG23150)、公益性行业专项(GYHY201406001)、中国气象科学研究院灾害天气国家重点实验室开放课题(2013LASW-B11)。
作者单位
潘旸 国家气象信息中心, 北京, 100081 
谷军霞 国家气象信息中心, 北京, 100081 
宇婧婧 国家气象信息中心, 北京, 100081 
沈艳 国家气象信息中心, 北京, 100081 
师春香 国家气象信息中心, 北京, 100081 
周自江 国家气象信息中心, 北京, 100081 
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中文摘要:
      高质量、高分辨率降水产品研制对于数值天气模式检验、水文陆面模拟、山洪地质灾害监测有着重要意义。利用中国近4万自动气象站逐时降水资料、中国雷达定量降水估计和CMORPH卫星反演降水产品,开展0.05°×0.05°和0.01°×0.01°两种高分辨率下的三源降水融合方法研究试验,探讨如何有效引入雷达高分辨率信息来提高降水产品质量。一方面,在0.05°分辨率上,先以自动气象站观测降水数据为基准,采用概率密度函数(PDF)匹配法订正雷达和卫星估测降水产品的系统偏差,将雷达降水产品的偏差从-0.05 mm/h降至-0.008 mm/h;再采用贝叶斯模型平均(BMA)方法融合雷达和卫星降水产品,形成0.05°分辨率的中国区域覆盖完整且最优的联合降水背景场。此外,在0.01°分辨率上,以0.05°分辨率的卫星-雷达贝叶斯模型平均联合降水产品为背景,采用1 km雷达估测降水的空间结构信息进行降尺度,亦能有效提高0.01°分辨率背景场的质量。然后,分别以不同分辨率的卫星-雷达联合降水产品为背景,采用统计方法量化误差估计,再采用最优插值方法融入地面观测。通过2419个中国国家级气象台站的独立样本检验,评估了多种类型的降水资料及融合试验产品在中国地区的质量。结果表明,两种分辨率的三源融合试验产品的精度均优于任何单一来源的降水产品,特别是在站点稀疏地区,降水精度均较融合前有显著提高,达到了较好的融合效果,其中在0.05°分辨率上采用“概率密度函数+贝叶斯模型平均+最优插值”方法的三源融合降水产品整体质量最好,而0.01°分辨率上基于“概率密度函数+贝叶斯模型平均+降尺度+最优插值”方法的三源融合降水产品在强降水监测上更有优势。
英文摘要:
      In order to meet the needs of operational weather forecast and research, a method of merging three sources of precipitation data, i.e. the rain gauge precipitation data collected at about 40,000 automatic weather stations, the radar QPE (Quantity Precipitation Estimate) and the CMORPH satellite retrieved precipitation products, is developed to produce high resolution datasets at 0.05°×0.05° and 0.01°×0.01°, respectively. At 0.05° resolution, the biases of radar QPE and CMORPH products are corrected first based on the rain gauge data using the PDF (Probability Density Function) matching method. The bias of radar QPE is sharply reduced from -0.05 mm/h to -0.008 mm/h. The CMORPH is then merged with the radar QPE to produce an optimum first guess using the Bayesian Model Averaging (BMA) method. At 0.01° resolution, the 0.05° first guess is further downscaled (DS) to 0.01° using 1-km radar QPE data. The two first guesses are revised based on the rain gauge data using the Optimum interpolation (OI) method, and their errors are statistically sampled at the 0.01°×0.01° and 0.05°×0.05° resolutions, respectively. The verification at the 2,419 independent stations shows that the two gauge-satellite-radar merged precipitation products at two resolutions are more accurate than any of the three sources of precipitation data and the gauge-satellite merged precipitation product. In particular, the data quality is well improved in areas of sparse gauge network in China. The three-source merged product at 0.05° resolution using the "PDF+BMA+OI" method is the best in general, while the one at 0.01° resolution using the "PDF+BMA+DS+OI" method has great advantages in detecting intense precipitation.
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