不同降水卫星数据反演降水量精度评价——以雅鲁藏布江流域为例
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摘要
利用多种定量指标和分类指标,评估PERSIANN-CDR(Precipitation Estimation from Remotely Sensed Information using Artificial Neural Netw orks-Climate Data Record)和TRM M 3B42 V7 (Tropical Rainfall M easuring M ission 3B42 V7)两种降水卫星产品在雅鲁藏布江流域的反演精度,并首次在雅鲁藏布江流域使用降水量体积分类指标对卫星数据的适用性进行评价。结果表明:(1)降水卫星数据的偏差主要表现在微量和重度降水的偏差上,两种降水卫星数据总是高估了弱降水,低估了强降水,PERSIANN降水卫星要比TRM M降水卫星对降水数据偏离程度小;(2) PERSIANN-CDR降水卫星数据与地面实测数据的相关系数为0. 663,偏差为0. 845,TRMM 3B42 V7降水卫星数据与地面实测数据的相关系数为0. 666,偏差为0. 579。只考虑定量指标的评价体系,两个降水卫星数据的精度差异相对较小;(3) PERSIANN-CDR卫星数据在各站点的各分类指标数值范围均比TRMM 3B42 V7卫星数据的指标数值范围大,PERSIANN-CDR卫星数据对降水事件和降水量的反演精度要高于TRMM 3B42 V7卫星数据。考虑降水量分类指标的评价指标体系比单纯使用传统定量指标评价降水卫星数据更能有效地反映出降水卫星对资料稀缺的高寒地区地面降水特征的捕捉能力。
Precipitation plays a very important role in the hydrological cycle and energy balance in many basins.In the Qinghai-Tibet Plateau,good quality precipitation data are unavailable and not easily accessible. Satellite precipitation data are being extensively used for alternative data sources for the ungauged basin. The uncertainties of satellite products over a certain region are largely unknown for systematic and random error. Therefore,satellite products need extensive validation before using in water resources research and streamflowsimulation. Based on daily precipitation data of meteorological stations in the Yarlung Zangbo River Basin during 1998—2015,the accuracy was assessed and compared between PERSIANN-CDR(Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) and TRMM3 b42 V7(Tropical Rainfall Measuring Mission 3 B42 V7) satellite precipitation products against quantitative indices and classification indices.The volumetric classification indices including VHI(Volumetric Hit Index,VHI),VFAR(Volumetric False Alarm Ratio,VFAR),and VMI(Volumetric Miss Index,VMI) are used to evaluate precipitation products in the Yarlung Zongbo River Basin for the first time. The results showthat:(1) The deviation of light and heavy precipitation is the main expression for the deviation of satellite products,and the satellite data always overestimate precipitation in areas where precipitation is small and underestimates precipitation in areas where precipitation is great. The deviation of PERSIANNT is smaller than the deviation of TRMM.(2) The correlation coefficient of PERSIANN precipitation satellite data is 0. 663,and the deviation is 0. 845. The correlation coefficient of TRMMprecipitation satellite data is 0. 666,and the deviation is 0. 579. The accuracy for PERSIANN and TRMMsatellite data are similar only by using quantitative indices.(3) Overall,the values of POD,VHI,FAR,VFAR,and VMI are 0. 830,0. 927,0. 54,0. 33,and 0. 073 respectively for PERSIANN. The values of POD,VHI,FAR,VFAR,and VMI are 0. 674,0. 833,0. 50,0. 31,and 0. 167 respectively for TRMM. PERSIANN satellite has better performance than TRMMsatellite on each site against classification indices. The evaluation system considering volumetric indices provides additional information beyond the traditional quantitative indices that can be useful in evaluating satellite products in plateau alpine region. This study is very meaningful and it could provide useful information for water resource management,water allocations and hydrologic simulation in the Yarlung Zangbo River Basin.
Precipitation plays a very important role in the hydrological cycle and energy balance in many basins.In the Qinghai-Tibet Plateau,good quality precipitation data are unavailable and not easily accessible. Satellite precipitation data are being extensively used for alternative data sources for the ungauged basin. The uncertainties of satellite products over a certain region are largely unknown for systematic and random error. Therefore,satellite products need extensive validation before using in water resources research and streamflowsimulation. Based on daily precipitation data of meteorological stations in the Yarlung Zangbo River Basin during 1998—2015,the accuracy was assessed and compared between PERSIANN-CDR(Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) and TRMM3 b42 V7(Tropical Rainfall Measuring Mission 3 B42 V7) satellite precipitation products against quantitative indices and classification indices.The volumetric classification indices including VHI(Volumetric Hit Index,VHI),VFAR(Volumetric False Alarm Ratio,VFAR),and VMI(Volumetric Miss Index,VMI) are used to evaluate precipitation products in the Yarlung Zongbo River Basin for the first time. The results showthat:(1) The deviation of light and heavy precipitation is the main expression for the deviation of satellite products,and the satellite data always overestimate precipitation in areas where precipitation is small and underestimates precipitation in areas where precipitation is great. The deviation of PERSIANNT is smaller than the deviation of TRMM.(2) The correlation coefficient of PERSIANN precipitation satellite data is 0. 663,and the deviation is 0. 845. The correlation coefficient of TRMMprecipitation satellite data is 0. 666,and the deviation is 0. 579. The accuracy for PERSIANN and TRMMsatellite data are similar only by using quantitative indices.(3) Overall,the values of POD,VHI,FAR,VFAR,and VMI are 0. 830,0. 927,0. 54,0. 33,and 0. 073 respectively for PERSIANN. The values of POD,VHI,FAR,VFAR,and VMI are 0. 674,0. 833,0. 50,0. 31,and 0. 167 respectively for TRMM. PERSIANN satellite has better performance than TRMMsatellite on each site against classification indices. The evaluation system considering volumetric indices provides additional information beyond the traditional quantitative indices that can be useful in evaluating satellite products in plateau alpine region. This study is very meaningful and it could provide useful information for water resource management,water allocations and hydrologic simulation in the Yarlung Zangbo River Basin.
引文
Aghakouchak A,M ehran A,2013.Extended contingency table:Performance metrics for satellite observations and climate model simulations[J].Water Resources Research,49(10):7144-7149.DOI:10.1002/w rcr.20498,2013.
Alijanian M,Rakhshandehroo G R,M ishra A K,et al,2017.Evaluation of satellite rainfall climatology using CM ORPH,PERSIANN‐CDR,PERSIANN,TRMM,MSWEP over Iran[J].International Journal of Climatology,37(14):4896-4914.DOI:10.1002/joc.5131.
DinkuT,Ceccato P,Lemma M,et al,2007.Validation of satellite rainfall products over East Africa's complex topography[J].International Journal of Remote Sensing,28(7):1503-1526.DOI:10.1080/01431160600954688.
HongY,2004.Precipitation estimation from remotely sensed information using Artificial Neural Netw ork-Cloud Classification System[J].Journal of Applied Meteorology,36(9):1176-1190.DOI:10.1175/1520-0450(1997)036.
Hong Y,Hsu K L,M oradkhani H,et al,2006.Uncertainty quantification of satellite precipitation estimation and M onte Carlo assessment of the error propagation into hydrologic response[J].Water Resources Research,42(8):2643-2645.DOI:10.1029/2005WR004398,2006.
Hu Q,Yang D,Li Z,et al,2014.M ulti-scale evaluation of six highresolution satellite monthly rainfall estimates over a humid region in China w ith dense rain gauges[J].International Journal of Remote Sensing,35(4):1272-1294.DOI:10.1175/JAM 2173.1.
Kummerow C,Barnes W,Kozu T,et al,1998.The Tropical Rainfall M easuring M ission(TRM M)sensor package[J].Journal of atmospheric and oceanic technology,15(3):809-817.DOI:10.1175/1520-0426(1998)015.
Levizzani V,Laviola S,Cattani E,2011.Detection and measurement of snow fall from space[J].Remote Sensing,3(1):145-166.DOI:10.3390/rs3010145.
SmithT M,Arkin P A,Bates J J,et al,2006.Estimating bias of satellite-based precipitation estimates[J].Journal of Hydrometeorology,7(5):841-856.DOI:10.1175/JHM 524.1.
Sorooshian S,Hsu K L,Gao X,et al,2000.Evaluation of PERSIANNsystem satellite-based estimates of tropical rainfall[J].Bulletin of the American M eteorological Society,81(9):2035-2046.DOI:10.1175/1520-0477(2000)081.
曾昭昭,王晓峰,任亮,2017.基于GWR模型的陕西秦巴山区TRM M降水数据降尺度研究[J].干旱区地理,40(1):26-36.DOI:10.13826/j.cnki.cn65-1103/x.2017.01.004
杜灵通,田庆久,黄彦,等,2012.基于TRMM数据的山东省干旱监测及其可靠性检验[J].农业工程学报,28(2):121-126.DOI::10.3969/j.issn.1002-6819.2012.02.022.
黄浠,王中根,桑燕芳,等,2016.雅鲁藏布江流域不同源降水数据质量对比研究[J].地理科学进展,35(3):339-348.DOI:10.18306/dlkxjz.2016.03.008.
计晓龙,吴昊旻,黄安宁,等,2017.青藏高原夏季降水日变化特征分析[J].高原气象,36(5):1188-1200.DOI:10.7522/j.issn.1000-0534.2016.00119.
江善虎,任立良,雍斌,等,2014.TRMM卫星降水数据在洣水流域径流模拟中的应用[J].水科学进展,25(5):641-649.DOI:10.14042/j.cnki.32.1309.2014.05.006.
江志红,黄群,李庆祥,2008.近50年中国降水序列均一性检验与订正研究[J].气候与环境研究,13(1):67-74.
李蒙,秦天玲,刘少华,等,2017.怒江上游TRMM 3B42V7降水产品资料时空验证及降水特征分析[J].高原气象,36(4):950-959.DOI:10.7522/j.issn.1000-0534.2016.00071.
廖荣伟,张冬斌,沈艳,2015.6种卫星降水产品在中国区域的精度特征评估[J].气象,41(8):970-979.DOI:10.13826/j.cnki.cn65-1103/x.2017.01.004.
林厚博,游庆龙,焦洋,等,2016.青藏高原及附近水汽输送对其夏季降水影响的分析[J].高原气象,35(2):309-317.DOI:10.7522/j.issn.1000-0534.2014.00146.
刘建军,陈葆德,2017.基于CloudSat卫星资料的青藏高原云系发生频率及其结构[J].高原气象,36(3):632-642.DOI:10.7522/j.issn.1000-0534.2017.00028.
刘天仇,1999.雅鲁藏布江水文特征[J].地理学报,54(S1):157-164.
吕洋,杨胜天,蔡明勇,等,2013.TRMM卫星降水数据在雅鲁藏布江流域的适用性分析[J].自然资源学报,28(8):1414-1425.DOI:1000-3037(2013)08-1414-12.
齐文文,张百平,庞宇,等,2013.基于TRMM数据的青藏高原降水的空间和季节分布特征[J].地理科学,33(8):999-1005.DOI:10.13249/j.cnki.sgs.2013.08.017.
孙乐强,郝振纯,王加虎,等,2014.TMPA卫星降水数据的评估与校正[J].水利学报,45(10):1135-1146.DOI:10.13243/j.cnki.slxb.2014.10.001.
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.DOI:10.11873/j.issn.1004-0323.2015.4.0607.
谢欣汝,游庆龙,保云涛,等,2018.基于多源数据的青藏高原夏季降水与水汽输送的联系[J].高原气象,37(1):78-92.DOI:10.7522/j.issn.1000-0534.2017.00030.
许时光,牛铮,沈艳,等,2014.CMORPH卫星降水数据在中国区域的误差特征研究[J].遥感技术与应用,29(2):189-194.DOI:10.11873/j.issn.1004-0323.2014.2.0189.
许时光,牛铮,沈艳,等,2015.CMORPH对青藏高原地区夏季降水的模拟精度研究与修正[J].遥感信息,30(1):71-76.DOI:10.3969/j.issn.1000-3177.2015.01.012.
杨志刚,卓玛,路红亚,等,2014.1961-2010年西藏雅鲁藏布江流域降水量变化特征及其对径流的影响分析[J].冰川冻土,36(1):166-172.DOI:10.7522/j.issn.1000-0240.2014.0021.
张立生,孙建华,赵思雄,等,2007.长江中游暖切变型暴雨的分析研究[J].气候与环境研究,12(2):165-180.DOI:10.3969/j.issn.1006-9585.2007.02.006.
张蒙,黄安宁,计晓龙,等,2016.卫星反演降水资料在青藏高原地区的适用性分析[J].高原气象,35(1):34-42.DOI:10.7522/j.issn.1000-0534.2014,00152.
Alijanian M,Rakhshandehroo G R,M ishra A K,et al,2017.Evaluation of satellite rainfall climatology using CM ORPH,PERSIANN‐CDR,PERSIANN,TRMM,MSWEP over Iran[J].International Journal of Climatology,37(14):4896-4914.DOI:10.1002/joc.5131.
DinkuT,Ceccato P,Lemma M,et al,2007.Validation of satellite rainfall products over East Africa's complex topography[J].International Journal of Remote Sensing,28(7):1503-1526.DOI:10.1080/01431160600954688.
HongY,2004.Precipitation estimation from remotely sensed information using Artificial Neural Netw ork-Cloud Classification System[J].Journal of Applied Meteorology,36(9):1176-1190.DOI:10.1175/1520-0450(1997)036.
Hong Y,Hsu K L,M oradkhani H,et al,2006.Uncertainty quantification of satellite precipitation estimation and M onte Carlo assessment of the error propagation into hydrologic response[J].Water Resources Research,42(8):2643-2645.DOI:10.1029/2005WR004398,2006.
Hu Q,Yang D,Li Z,et al,2014.M ulti-scale evaluation of six highresolution satellite monthly rainfall estimates over a humid region in China w ith dense rain gauges[J].International Journal of Remote Sensing,35(4):1272-1294.DOI:10.1175/JAM 2173.1.
Kummerow C,Barnes W,Kozu T,et al,1998.The Tropical Rainfall M easuring M ission(TRM M)sensor package[J].Journal of atmospheric and oceanic technology,15(3):809-817.DOI:10.1175/1520-0426(1998)015.
Levizzani V,Laviola S,Cattani E,2011.Detection and measurement of snow fall from space[J].Remote Sensing,3(1):145-166.DOI:10.3390/rs3010145.
SmithT M,Arkin P A,Bates J J,et al,2006.Estimating bias of satellite-based precipitation estimates[J].Journal of Hydrometeorology,7(5):841-856.DOI:10.1175/JHM 524.1.
Sorooshian S,Hsu K L,Gao X,et al,2000.Evaluation of PERSIANNsystem satellite-based estimates of tropical rainfall[J].Bulletin of the American M eteorological Society,81(9):2035-2046.DOI:10.1175/1520-0477(2000)081.
曾昭昭,王晓峰,任亮,2017.基于GWR模型的陕西秦巴山区TRM M降水数据降尺度研究[J].干旱区地理,40(1):26-36.DOI:10.13826/j.cnki.cn65-1103/x.2017.01.004
杜灵通,田庆久,黄彦,等,2012.基于TRMM数据的山东省干旱监测及其可靠性检验[J].农业工程学报,28(2):121-126.DOI::10.3969/j.issn.1002-6819.2012.02.022.
黄浠,王中根,桑燕芳,等,2016.雅鲁藏布江流域不同源降水数据质量对比研究[J].地理科学进展,35(3):339-348.DOI:10.18306/dlkxjz.2016.03.008.
计晓龙,吴昊旻,黄安宁,等,2017.青藏高原夏季降水日变化特征分析[J].高原气象,36(5):1188-1200.DOI:10.7522/j.issn.1000-0534.2016.00119.
江善虎,任立良,雍斌,等,2014.TRMM卫星降水数据在洣水流域径流模拟中的应用[J].水科学进展,25(5):641-649.DOI:10.14042/j.cnki.32.1309.2014.05.006.
江志红,黄群,李庆祥,2008.近50年中国降水序列均一性检验与订正研究[J].气候与环境研究,13(1):67-74.
李蒙,秦天玲,刘少华,等,2017.怒江上游TRMM 3B42V7降水产品资料时空验证及降水特征分析[J].高原气象,36(4):950-959.DOI:10.7522/j.issn.1000-0534.2016.00071.
廖荣伟,张冬斌,沈艳,2015.6种卫星降水产品在中国区域的精度特征评估[J].气象,41(8):970-979.DOI:10.13826/j.cnki.cn65-1103/x.2017.01.004.
林厚博,游庆龙,焦洋,等,2016.青藏高原及附近水汽输送对其夏季降水影响的分析[J].高原气象,35(2):309-317.DOI:10.7522/j.issn.1000-0534.2014.00146.
刘建军,陈葆德,2017.基于CloudSat卫星资料的青藏高原云系发生频率及其结构[J].高原气象,36(3):632-642.DOI:10.7522/j.issn.1000-0534.2017.00028.
刘天仇,1999.雅鲁藏布江水文特征[J].地理学报,54(S1):157-164.
吕洋,杨胜天,蔡明勇,等,2013.TRMM卫星降水数据在雅鲁藏布江流域的适用性分析[J].自然资源学报,28(8):1414-1425.DOI:1000-3037(2013)08-1414-12.
齐文文,张百平,庞宇,等,2013.基于TRMM数据的青藏高原降水的空间和季节分布特征[J].地理科学,33(8):999-1005.DOI:10.13249/j.cnki.sgs.2013.08.017.
孙乐强,郝振纯,王加虎,等,2014.TMPA卫星降水数据的评估与校正[J].水利学报,45(10):1135-1146.DOI:10.13243/j.cnki.slxb.2014.10.001.
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.DOI:10.11873/j.issn.1004-0323.2015.4.0607.
谢欣汝,游庆龙,保云涛,等,2018.基于多源数据的青藏高原夏季降水与水汽输送的联系[J].高原气象,37(1):78-92.DOI:10.7522/j.issn.1000-0534.2017.00030.
许时光,牛铮,沈艳,等,2014.CMORPH卫星降水数据在中国区域的误差特征研究[J].遥感技术与应用,29(2):189-194.DOI:10.11873/j.issn.1004-0323.2014.2.0189.
许时光,牛铮,沈艳,等,2015.CMORPH对青藏高原地区夏季降水的模拟精度研究与修正[J].遥感信息,30(1):71-76.DOI:10.3969/j.issn.1000-3177.2015.01.012.
杨志刚,卓玛,路红亚,等,2014.1961-2010年西藏雅鲁藏布江流域降水量变化特征及其对径流的影响分析[J].冰川冻土,36(1):166-172.DOI:10.7522/j.issn.1000-0240.2014.0021.
张立生,孙建华,赵思雄,等,2007.长江中游暖切变型暴雨的分析研究[J].气候与环境研究,12(2):165-180.DOI:10.3969/j.issn.1006-9585.2007.02.006.
张蒙,黄安宁,计晓龙,等,2016.卫星反演降水资料在青藏高原地区的适用性分析[J].高原气象,35(1):34-42.DOI:10.7522/j.issn.1000-0534.2014,00152.
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