中国区域高分辨率多源降水观测产品的融合方法试验
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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。
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.
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.
引文
段旭,王曼,张杰等.2010.低纬高原地区多元信息综合变分分析试验.高原气象,29(3):712-718.Duan X,Wang M,Zhang J,et al.2010.Variational Assimilation experiment of the multiinformation analysis over low latitude Plateau.Plateau Meteor,29(3):712-718(in Chinese)
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刘晓阳,杨洪平,李建通等.2010.新一代天气雷达定量降水估测集成系统.气象,36(4):90-95.Liu X Y,Yang H P,Li J T,et al.2010.CINRAD radar quantitative precipitation estimation group system.Meteor Mon,36(4):90-95(in Chinese)
潘旸,沈艳,宇婧婧等.2012.基于最优插值方法分析的中国区域地面观测与卫星反演逐时降水融合试验.气象学报,70(6):1381-1389.Pan Y,Shen Y,Yu J J,et al.2012.Analysis of the combined gauge-satellite hourly precipitation over China based on the OI technique.Acta Meteor Sinica,70(6):1381-1389(in Chinese)
潘旸,沈艳,宇婧婧等.2015.基于贝叶斯融合方法的高分辨率地面-卫星-雷达三源降水融合试验.气象学报,73(1):177-186.Pan Y,Shen Y,Yu J J,et al.2015.An experiment of highresolution gauge-radar-satellite combined precipitation retrieval based on the Bayesian merging method.Acta Meteor Sinica,73(1):177-186(in Chinese)
任芝花,赵平,张强等.2010.适用于全国自动站小时降水资料的质量控制方法.气象,36(7):123-132.Ren Z H,Zhao P,Zhang Q,et al.2010.Quality control procedures for hourly precipitation data from automatic weather stations in China.Meteor Mon,36(7):123-132(in Chinese)
沈艳,冯明农,张洪政等.2010.我国逐日降水量格点化方法.应用气象学报,21(3):279-286.Shen Y,Feng M N,Zhang H Z,et al.2010.Interpolation methods of China daily precipitation data.J Appl Meteor Sci,21(3):279-286(in Chinese)
沈艳,潘旸,徐宾等.2012.最优插值法在对中国自动站降水量空间分析中的参数优化.成都信息工程学院学报,27(2):219-224.Shen Y,Pan Y,Xu B,et al.2012.Parameter improvements of hourly automatic weather stations precipitation analysis by optimal interpolation over China.J Chengdu Univ Inf Technol,27(2):219-224(in Chinese)
沈艳,潘旸,宇婧婧等.2013.中国区域小时降水量融合产品的质量评估.大气科学学报,36(1):37-46.Shen Y,Pan Y,Yu JJ,et al.2013.Quality assessment of hourly merged precipitation product over China.Trans Atmos Sci,36(1):37-46(in Chinese)
杨俊,吕伟涛,马颖等.2010.基于局部阈值插值的地基云自动检测方法.气象学报,68(6):1007-1017.Yang J,LüW T,Ma Y,et al.2010.An automatic ground based cloud detection method based on the local threshold interpolation.Acta Meteor Sinica,68(6):1007-1017(in Chinese)
叶金印,邱旭敏,黄勇等.2013.气象遥感图像及格点场重采样插值方法.计算机工程与应用,49(18):237-241.Ye J Y,Qiu XM,Huang Y,et al.2013.Resampling interpolation methods of meteorological remote sensing image and grid point field.Comput Eng Appl,49(18):237-241(in Chinese)
宇婧婧,沈艳,潘旸等.2013.概率密度匹配法对中国区域卫星降水资料的改进.应用气象学报,24(5):544-553.Yu J J,Shen Y,Pan Y,et al.2013.Improvement of satellite-based precipitation estimates over China based on probability density function matching method.J Appl Meteor Sci,24(5):544-553(in Chinese)
宇婧婧,沈艳,潘旸等.2015.中国区域逐日融合降水数据集与国际降水产品的对比评估.气象学报,73(2):394-410.Yu J J,Shen Y,Pan Y,et al.2015.Comparative assessment between the daily merged precipitation dataset over China and the world's popular counterparts.Acta Meteor Sinica,73(2):394-410(in Chinese)
袁松,程华,王东勇等.2012.模式再分析与实际探空资料的对比分析.气象科学,32(1):62-67.Yuan S,Cheng H,Wang DY,et al.2012.Comparative analysis on meteorological parameters of sounding data and model-generated sounding data.J Meteor Sci,32(1):62-67(in Chinese)
Adam J C,Lettenmaier D P.2003.Adjustment of global gridded precipitation for systematic bias.J Geophys Res,108(D9):4257,doi:10.1029/2002JD002499
Adam J C,Clark E A,Lettenmaier D P,et al.2006.Correction of global precipitation products for orographic effects.J Climate,19(1):15-38
Duan Q Y,Ajami N K,Gao X G,et al.2007.Multi-model ensemble hydrologic prediction using Bayesian model averaging.Adv Water Resour,30(5):1371-1386
Hong Y,Hsu K L,Sorooshian S,et al.2004.Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system.J Appl Meteor,43(12):1834-1853
Hong Y,Hsu K L,Moradkhani H,et al.2006.Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response.Water Resour Res,42(8):W08421,doi:10.1029/2005 WR004398
Huffman G J,Adler R F,Arkin P A,et al.1997.The global precipitation climatology project(GPCP)combined precipitation dataset.Bull Amer Meteor Soc,78(1):5-20
Huffman G J,Adler R F,Morrissey M M,et al.2001.Global precipitation at one-degree daily resolution from multisatellite observations.J Hydrometeorol,2(1):36-50
Huffman G J,Bolvin D T,Nelkin E J,et al.2007.The TRMMmultisatellite precipitation analysis(TMPA):Quasi-global,multiyear,combined-sensor precipitation estimates at fine scales.J Hydrometeorol,8(1):38-55
Joyce R J,Janowiak J E,Arkin P A,et al.2004.CMORPH:Amethod that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution.J Hydrometeorol,5(3):487-503
Lu N M,You R,Zhang W J.2004.A fusing technique with satellite precipitation estimate and raingauge data.Acta Meteor Sinica,18(2):141-146
Raftery A E,Gneiting T,Balabdaoui F,et al.2005.Using Bayesian model averaging to calibrate forecast ensembles.Mon Wea Rev,133(5):1155-1174
Seo D J.1998.Real-time estimation of rainfall fields using radar rainfall and rain gage data.J Hydrol,208(1-2):37-52
Seo D J,Breidenbach J P,Johnson E R.1999.Real-time estimation of mean field bias in radar rainfall data.J Hydrol,223(3-4):131-147
Seo D J,Breidenbach J P.2002.Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements.J Hydrometeorol,3(2):93-111
Shen Y,Xiong A Y,Wang Y,et al.2010.Performance of high-resolution satellite precipitation products over China.J Geophys Res,115(D2):D02114,doi:10.1029/2009JD012097
Shen Y,Zhao P,Pan Y,et al.2014.A high spatiotemporal gaugesatellite merged precipitation analysis over China.J Geophys Res,119(6):3063-3075,doi:10.1002/2013JD020686
Xie P P,Xiong A Y.2011.A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses.JGeophys Res,116(D21):D21106,doi:10.1029/2011JD016118
高晓荣,梁建茵,李春晖等.2013.多平台(雷达、卫星、雨量计)降水信息的融合技术初探.高原气象,32(2):549-555.Gao XR,Liang J Y,Li C H,et al.2013.Preliminary studies on merged techniques based on precipitation information from multiplatform(radar,satellite and rain gauge).Plateau Meteor,32(2):549-555(in Chinese)
刘晓阳,杨洪平,李建通等.2010.新一代天气雷达定量降水估测集成系统.气象,36(4):90-95.Liu X Y,Yang H P,Li J T,et al.2010.CINRAD radar quantitative precipitation estimation group system.Meteor Mon,36(4):90-95(in Chinese)
潘旸,沈艳,宇婧婧等.2012.基于最优插值方法分析的中国区域地面观测与卫星反演逐时降水融合试验.气象学报,70(6):1381-1389.Pan Y,Shen Y,Yu J J,et al.2012.Analysis of the combined gauge-satellite hourly precipitation over China based on the OI technique.Acta Meteor Sinica,70(6):1381-1389(in Chinese)
潘旸,沈艳,宇婧婧等.2015.基于贝叶斯融合方法的高分辨率地面-卫星-雷达三源降水融合试验.气象学报,73(1):177-186.Pan Y,Shen Y,Yu J J,et al.2015.An experiment of highresolution gauge-radar-satellite combined precipitation retrieval based on the Bayesian merging method.Acta Meteor Sinica,73(1):177-186(in Chinese)
任芝花,赵平,张强等.2010.适用于全国自动站小时降水资料的质量控制方法.气象,36(7):123-132.Ren Z H,Zhao P,Zhang Q,et al.2010.Quality control procedures for hourly precipitation data from automatic weather stations in China.Meteor Mon,36(7):123-132(in Chinese)
沈艳,冯明农,张洪政等.2010.我国逐日降水量格点化方法.应用气象学报,21(3):279-286.Shen Y,Feng M N,Zhang H Z,et al.2010.Interpolation methods of China daily precipitation data.J Appl Meteor Sci,21(3):279-286(in Chinese)
沈艳,潘旸,徐宾等.2012.最优插值法在对中国自动站降水量空间分析中的参数优化.成都信息工程学院学报,27(2):219-224.Shen Y,Pan Y,Xu B,et al.2012.Parameter improvements of hourly automatic weather stations precipitation analysis by optimal interpolation over China.J Chengdu Univ Inf Technol,27(2):219-224(in Chinese)
沈艳,潘旸,宇婧婧等.2013.中国区域小时降水量融合产品的质量评估.大气科学学报,36(1):37-46.Shen Y,Pan Y,Yu JJ,et al.2013.Quality assessment of hourly merged precipitation product over China.Trans Atmos Sci,36(1):37-46(in Chinese)
杨俊,吕伟涛,马颖等.2010.基于局部阈值插值的地基云自动检测方法.气象学报,68(6):1007-1017.Yang J,LüW T,Ma Y,et al.2010.An automatic ground based cloud detection method based on the local threshold interpolation.Acta Meteor Sinica,68(6):1007-1017(in Chinese)
叶金印,邱旭敏,黄勇等.2013.气象遥感图像及格点场重采样插值方法.计算机工程与应用,49(18):237-241.Ye J Y,Qiu XM,Huang Y,et al.2013.Resampling interpolation methods of meteorological remote sensing image and grid point field.Comput Eng Appl,49(18):237-241(in Chinese)
宇婧婧,沈艳,潘旸等.2013.概率密度匹配法对中国区域卫星降水资料的改进.应用气象学报,24(5):544-553.Yu J J,Shen Y,Pan Y,et al.2013.Improvement of satellite-based precipitation estimates over China based on probability density function matching method.J Appl Meteor Sci,24(5):544-553(in Chinese)
宇婧婧,沈艳,潘旸等.2015.中国区域逐日融合降水数据集与国际降水产品的对比评估.气象学报,73(2):394-410.Yu J J,Shen Y,Pan Y,et al.2015.Comparative assessment between the daily merged precipitation dataset over China and the world's popular counterparts.Acta Meteor Sinica,73(2):394-410(in Chinese)
袁松,程华,王东勇等.2012.模式再分析与实际探空资料的对比分析.气象科学,32(1):62-67.Yuan S,Cheng H,Wang DY,et al.2012.Comparative analysis on meteorological parameters of sounding data and model-generated sounding data.J Meteor Sci,32(1):62-67(in Chinese)
Adam J C,Lettenmaier D P.2003.Adjustment of global gridded precipitation for systematic bias.J Geophys Res,108(D9):4257,doi:10.1029/2002JD002499
Adam J C,Clark E A,Lettenmaier D P,et al.2006.Correction of global precipitation products for orographic effects.J Climate,19(1):15-38
Duan Q Y,Ajami N K,Gao X G,et al.2007.Multi-model ensemble hydrologic prediction using Bayesian model averaging.Adv Water Resour,30(5):1371-1386
Hong Y,Hsu K L,Sorooshian S,et al.2004.Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system.J Appl Meteor,43(12):1834-1853
Hong Y,Hsu K L,Moradkhani H,et al.2006.Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response.Water Resour Res,42(8):W08421,doi:10.1029/2005 WR004398
Huffman G J,Adler R F,Arkin P A,et al.1997.The global precipitation climatology project(GPCP)combined precipitation dataset.Bull Amer Meteor Soc,78(1):5-20
Huffman G J,Adler R F,Morrissey M M,et al.2001.Global precipitation at one-degree daily resolution from multisatellite observations.J Hydrometeorol,2(1):36-50
Huffman G J,Bolvin D T,Nelkin E J,et al.2007.The TRMMmultisatellite precipitation analysis(TMPA):Quasi-global,multiyear,combined-sensor precipitation estimates at fine scales.J Hydrometeorol,8(1):38-55
Joyce R J,Janowiak J E,Arkin P A,et al.2004.CMORPH:Amethod that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution.J Hydrometeorol,5(3):487-503
Lu N M,You R,Zhang W J.2004.A fusing technique with satellite precipitation estimate and raingauge data.Acta Meteor Sinica,18(2):141-146
Raftery A E,Gneiting T,Balabdaoui F,et al.2005.Using Bayesian model averaging to calibrate forecast ensembles.Mon Wea Rev,133(5):1155-1174
Seo D J.1998.Real-time estimation of rainfall fields using radar rainfall and rain gage data.J Hydrol,208(1-2):37-52
Seo D J,Breidenbach J P,Johnson E R.1999.Real-time estimation of mean field bias in radar rainfall data.J Hydrol,223(3-4):131-147
Seo D J,Breidenbach J P.2002.Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements.J Hydrometeorol,3(2):93-111
Shen Y,Xiong A Y,Wang Y,et al.2010.Performance of high-resolution satellite precipitation products over China.J Geophys Res,115(D2):D02114,doi:10.1029/2009JD012097
Shen Y,Zhao P,Pan Y,et al.2014.A high spatiotemporal gaugesatellite merged precipitation analysis over China.J Geophys Res,119(6):3063-3075,doi:10.1002/2013JD020686
Xie P P,Xiong A Y.2011.A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses.JGeophys Res,116(D21):D21106,doi:10.1029/2011JD016118