TRMM卫星降水数据在天山山区的校正方法研究
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摘要
本文基于全球降水观测(Global Precipitation Measurement,GPM)及天山山区40个气象站点数据,提出了一种新的热带降水测量任务(Tropical Rainfall Measuring Mission,TRMM)卫星数据误差校正方法(MERGE),并与广泛使用的地理差异分析(Geographical Different Analysis,GDA)方法进行对比,通过交叉验证来评价两种方法对山区年/月降水的校正效果。结果表明,MERGE方法不仅能提高TRMM的空间分辨率(0.05°),且在年(平均Bias降低了21%)、月(平均Bias降低了37%)尺度上的校正结果都优于GDA方法;校正后TRMM降水的误差在不同月份存在很大差异,即在降水较多的雨季(4—10月)误差较大(MAE>3mm),而在干季(11月至次年3月)误差较小(MAE<3mm)。MERGE方法不仅能有效改善高海拔地区的降水误差(平均误差降低了59%),且对原始TRMM数据的依赖性较小(R2=0.47),即使在原始TRMM数据表现不佳的局部区域,也能有效降低其误差。
Based on Global Precipitation Measurement(GPM)and 40 weather stations in Tianshan Mountains,a new error correction method(MERGE)is proposed and compared with the widely used geographical different analysis(GDA)method through cross-validation.The results indicate that both GDA and MERGE methods can improve the accuracy of Tropical Rainfall Measuring Mission(TRMM)significantly.The MERGE method not only improve the spatial resolution of TRMM,but also has higher accuracy than GDA method on annual and monthly scales.The error of corrected TRMM data varies greatly in different months.It has larger error(MAE>3 mm)in the rainy season(April-October),but less error(MAE<3 mm)in the dry season(November-March).Besides,the MERGE method can effectively improve the error in high altitude area and less depends on the original TRMM data,with the R2=0.47.So,even in local areas where the TRMM data have poor performance,this method can also reduce the error effectively.
Based on Global Precipitation Measurement(GPM)and 40 weather stations in Tianshan Mountains,a new error correction method(MERGE)is proposed and compared with the widely used geographical different analysis(GDA)method through cross-validation.The results indicate that both GDA and MERGE methods can improve the accuracy of Tropical Rainfall Measuring Mission(TRMM)significantly.The MERGE method not only improve the spatial resolution of TRMM,but also has higher accuracy than GDA method on annual and monthly scales.The error of corrected TRMM data varies greatly in different months.It has larger error(MAE>3 mm)in the rainy season(April-October),but less error(MAE<3 mm)in the dry season(November-March).Besides,the MERGE method can effectively improve the error in high altitude area and less depends on the original TRMM data,with the R2=0.47.So,even in local areas where the TRMM data have poor performance,this method can also reduce the error effectively.
引文
成璐,沈润平,师春香,等,2014.CMORPH和TRMM 3B42降水估计产品的评估检验[J].气象,40(11):1372-1379.
范彬彬,罗格平,张弛,等,2013.新疆夏季降水时空分布的适用性评估[J].地理研究,32(9):1602-1612.
李瑞雪,2010.中国天山山区气候变化的时空分布特征[D].兰州:西北师范大学.
骆三,苗峻峰,牛涛,等,2011.TRMM测雨产品3B42与台站资料在中国区域的对比分析[J].气象,37(9):1081-1090.
隋玉正,崔林丽,史军,等,2016.一种多源全球海洋降水资料融合方法[J].热带海洋学报,35(2):50-56.
唐国强,万玮,曾子悦,等,2015.全球降水测量(GPM)计划及其最新进展综述[J].遥感技术与应用,30(4):607-615.
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[J].应用气象学报,24(5):544-553.
张涛,李宝林,何元庆,等,2015.基于TRMM订正数据的横断山区降水时空分布特征[J].自然资源学报,30(2):260-270.
张正勇,何新林,刘琳,等,2015.中国天山山区降水空间分布模拟及成因分析[J].水科学进展,26(4):500-508.
周胜男,罗亚丽,汪会,2015.青藏高原、中国东部及北美副热带地区夏季降水系统发生频次的TRMM资料分析[J].气象,41(1):1-16.
Ceccherini G,Ameztoy I,Hernández C P R,et al,2015.High-resolution precipitation datasets in South America and West Africa based on satellite-derived rainfall,enhanced vegetation index and digital elevation model[J].Remote Sens,7(5):6454-6488.
Cheema M J M,Bastiaanssen W G M,2012.Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin[J].Int J Remote Sens,33(8):2603-2627.
Condom T,Rau P,Espinoza J C,2011.Correction of TRMM 3B43monthly precipitation data over the mountainous areas of Peru during the period 1998-2007[J].Hydrol Process,25(12):1924-1933.
Duan Z,Bastiaanssen W G M,2013.First results from Version 7TRMM 3B43precipitation product in combination with a new downscaling-calibration procedure[J].Remote Sens Environ,131:1-13.
Fang J,Du J,Xu W,et al,2013.Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area[J].Adv Water Resour,61:42-50.
Harmsen E W,Mesa S E G,Cabassa E,et al,2008.Satellite sub-pixel rainfall variability[J].Int J Syst Appl Eng Dev,2(3):91-100.
Huang C C,Zheng X G,Tait A,et al,2014.On using smoothing spline and residual correction to fuse rain gauge observations and remote sensing data[J].J Hydrol,508:410-417.
Huffman G J,Bolvin D T,Nelkin E J,2015.Day 1for the integrated multi-satellite retrievals for GPM(IMERG)data sets.NASAGoddard Space Flight Center and Science System Application.
Ji X,Chen Y F,2012.Characterizing spatial patterns of precipitation based on corrected TRMM 3B43data over the mid Tianshan Mountains of China[J].J Mt Sci,9(5):628-645.
Jia S F,Zhu W B,LüA F,et al,2011.A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEMin the Qaidam Basin of China[J].Remote Sens Environ,115(12):3069-3079.
Joyce R J,Janowiak J E,Arkin P A,et al,2004.CMORPH:a method that produces global precipitation estimates from passive microwave and Infrared data at high spatial and temporal resolution[J].J Hydrometeor,5(3):487-503.
Krajewski W F,Ciach G J,Habib E,2003.An analysis of small-scale rainfall variability in different climatic regimes[J].Hydrol Sci,48(2):151-162.
Mantas V M,Liu Z,Caro C,et al,2015.Validation of TRMM multisatellite precipitation analysis(TMPA)products in the Peruvian Andes[J].Atmos Res,163:132-145.
Ning S W,Ishidaira H,Udmale P,et al,2015.Remote sensing based analysis of recent variations in water resources and vegetation of a semi-arid region[J].Water,7(11):6039-6055.
Tang G Q,Zeng Z Y,Long D,et al,2016.Statistical and hydrological comparisons between TRMM and GPM Level-3products over a midlatitude basin:is Day-1IMERG a good successor for TMPA3B42V7?[J].J Hydrometeor,17(1):121-137.
Vila D A,de Goncalves L G G,Toll D L,et al,2009.Statistical evaluation of combined daily gauge observations and rainfall satellite estimates over continental South America[J].J Hydrometeor,10(2):533-542.
Wanders N,Pan M,Wood E F,2015.Correction of real-time satellite precipitation with multi-sensor satellite observations of land surface variables[J].Remote Sens Environ,160:206-221.
Willmott C J,Robeson S M,Matsuura K,et al.Assessment of three dimensionless measures of model performance[J].Environ Modell Soft,73(c):167-174.
Xu S G,Wu C Y,Wang L,et al,2015.A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics[J].Remote Sens Environ,162:119-140.
范彬彬,罗格平,张弛,等,2013.新疆夏季降水时空分布的适用性评估[J].地理研究,32(9):1602-1612.
李瑞雪,2010.中国天山山区气候变化的时空分布特征[D].兰州:西北师范大学.
骆三,苗峻峰,牛涛,等,2011.TRMM测雨产品3B42与台站资料在中国区域的对比分析[J].气象,37(9):1081-1090.
隋玉正,崔林丽,史军,等,2016.一种多源全球海洋降水资料融合方法[J].热带海洋学报,35(2):50-56.
唐国强,万玮,曾子悦,等,2015.全球降水测量(GPM)计划及其最新进展综述[J].遥感技术与应用,30(4):607-615.
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[J].应用气象学报,24(5):544-553.
张涛,李宝林,何元庆,等,2015.基于TRMM订正数据的横断山区降水时空分布特征[J].自然资源学报,30(2):260-270.
张正勇,何新林,刘琳,等,2015.中国天山山区降水空间分布模拟及成因分析[J].水科学进展,26(4):500-508.
周胜男,罗亚丽,汪会,2015.青藏高原、中国东部及北美副热带地区夏季降水系统发生频次的TRMM资料分析[J].气象,41(1):1-16.
Ceccherini G,Ameztoy I,Hernández C P R,et al,2015.High-resolution precipitation datasets in South America and West Africa based on satellite-derived rainfall,enhanced vegetation index and digital elevation model[J].Remote Sens,7(5):6454-6488.
Cheema M J M,Bastiaanssen W G M,2012.Local calibration of remotely sensed rainfall from the TRMM satellite for different periods and spatial scales in the Indus Basin[J].Int J Remote Sens,33(8):2603-2627.
Condom T,Rau P,Espinoza J C,2011.Correction of TRMM 3B43monthly precipitation data over the mountainous areas of Peru during the period 1998-2007[J].Hydrol Process,25(12):1924-1933.
Duan Z,Bastiaanssen W G M,2013.First results from Version 7TRMM 3B43precipitation product in combination with a new downscaling-calibration procedure[J].Remote Sens Environ,131:1-13.
Fang J,Du J,Xu W,et al,2013.Spatial downscaling of TRMM precipitation data based on the orographical effect and meteorological conditions in a mountainous area[J].Adv Water Resour,61:42-50.
Harmsen E W,Mesa S E G,Cabassa E,et al,2008.Satellite sub-pixel rainfall variability[J].Int J Syst Appl Eng Dev,2(3):91-100.
Huang C C,Zheng X G,Tait A,et al,2014.On using smoothing spline and residual correction to fuse rain gauge observations and remote sensing data[J].J Hydrol,508:410-417.
Huffman G J,Bolvin D T,Nelkin E J,2015.Day 1for the integrated multi-satellite retrievals for GPM(IMERG)data sets.NASAGoddard Space Flight Center and Science System Application.
Ji X,Chen Y F,2012.Characterizing spatial patterns of precipitation based on corrected TRMM 3B43data over the mid Tianshan Mountains of China[J].J Mt Sci,9(5):628-645.
Jia S F,Zhu W B,LüA F,et al,2011.A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEMin the Qaidam Basin of China[J].Remote Sens Environ,115(12):3069-3079.
Joyce R J,Janowiak J E,Arkin P A,et al,2004.CMORPH:a method that produces global precipitation estimates from passive microwave and Infrared data at high spatial and temporal resolution[J].J Hydrometeor,5(3):487-503.
Krajewski W F,Ciach G J,Habib E,2003.An analysis of small-scale rainfall variability in different climatic regimes[J].Hydrol Sci,48(2):151-162.
Mantas V M,Liu Z,Caro C,et al,2015.Validation of TRMM multisatellite precipitation analysis(TMPA)products in the Peruvian Andes[J].Atmos Res,163:132-145.
Ning S W,Ishidaira H,Udmale P,et al,2015.Remote sensing based analysis of recent variations in water resources and vegetation of a semi-arid region[J].Water,7(11):6039-6055.
Tang G Q,Zeng Z Y,Long D,et al,2016.Statistical and hydrological comparisons between TRMM and GPM Level-3products over a midlatitude basin:is Day-1IMERG a good successor for TMPA3B42V7?[J].J Hydrometeor,17(1):121-137.
Vila D A,de Goncalves L G G,Toll D L,et al,2009.Statistical evaluation of combined daily gauge observations and rainfall satellite estimates over continental South America[J].J Hydrometeor,10(2):533-542.
Wanders N,Pan M,Wood E F,2015.Correction of real-time satellite precipitation with multi-sensor satellite observations of land surface variables[J].Remote Sens Environ,160:206-221.
Willmott C J,Robeson S M,Matsuura K,et al.Assessment of three dimensionless measures of model performance[J].Environ Modell Soft,73(c):167-174.
Xu S G,Wu C Y,Wang L,et al,2015.A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics[J].Remote Sens Environ,162:119-140.