Plateau Meteorology

Plateau Meteorology >

2020 , Vol. 39 >Issue 1: 185 - 195

DOI: https://doi.org/10.7522/j.issn.1000-0534.2019.00016

Analysis of the Accuracy of TRMM 3B42 Rainfall Data in the Upper and Middle Reaches of Taohe River

  • Lizhen CHENG ,
  • Meixue YANG ,
  • Xuejia WANG ,
  • Guoning WAN ,
  • Zhaochen LIU
Expand
  • <sup>1.</sup>State Key Laboratory of Cryospheric Science, Northwest Institute of Eco?Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, China;<sup>2.</sup>University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2018-10-29

  Online published: 2020-02-28

Fold

Abstract

TRMM satellite products play an important role in analyzing the temporal and spatial changes of precipitation, and have become an important source of hydrometeorological research in no data or less data areas. Since 1997, TRMM products have already been applied in plenty of researches with its robust advantages of high spatial and temporal resolution, wide coverage, long time series (real?time update). The Version 7 TRMM 3B42 products were evulated from January 2006 to December 2016 over the upper and middle of Taohe River Basin in different time scale. The results showed that: (1) More than 60% of the rainfall events were captured by the TRMM 3B42 satellite in the study area and the FAR was only 12%, which indicated that the TRMM satellite has a better detection ability for rainfall events. While, FBI was 0.88, suggesting that TRMM products was slightly underestimated the observation. In addition, it showed good performance for detecting the occurrence of rain events with average equitable threat score (ETS) of 0.31. Comparison of the occurrence frequency and rainfall contribution rate of TRMM daily precipitation and measured data at each rain level, TRMM performed best on the moderate rain level, which was significantly consistent with the observation. (2) The value of CC at daily scale average was 0.64 and the differences between TRMM and observation precipitation were narrow, which indicated that the accuracy of TRMM was high at daily scale. Comparative analyses with daily, TRMM data at monthly scale exhibited the best performance with CC of 0.96, which is close to the “ruth value”. In the monthly error distribution, BIAS in warm and humid season were smaller than that in cold and dry season, and the effect was good. While, RMSE in cold and dry season were smaller than that in warm and humid season owing to the concentration of precipitation in summer and autumn. (3) The spatial distribution based on TRMM and actual precipitation indicated that there was a decreasing trend from southwest to northeast in this area, and TRMM data slightly overestimated the annual data. In addition, the changes in summer and autumn were consistent with that in annual precipitation. TRMM products has an advantage in continuous space distribution and the accuracy was better. Therefore, TRMM precipitation products is liable in the study area.

Key words： Taohe River; TRMM; precipitation; temporal change; accuracy evaluation

Cite this article

Lizhen CHENG , Meixue YANG , Xuejia WANG , Guoning WAN , Zhaochen LIU . Analysis of the Accuracy of TRMM 3B42 Rainfall Data in the Upper and Middle Reaches of Taohe River[J]. Plateau Meteorology, 2020 , 39(1) : 185 -195 . DOI: 10.7522/j.issn.1000-0534.2019.00016

References

[1]Aghakouchak A, Nasrollahi N, Habib E, 2009. Accounting for uncertainties of the TRMM satellite estimates[J]. Remote Sensing, 1(3): 606-619. DOI: 10.3390/rs1030606.
[2]Beyer M, Wallner M, Bahlmann L, al et, 2016. Rainfall characteristics and their implications for rain?fed agriculture: a case study in the upper Zambezi River Basin[J]. Hydrological Sciences Journal, 61(2): 1-53.
[3]Cai Y C, Jin C J, Wang A Z, al et, 2015. Spatio?temporal analysis of the accuracy of tropical multi?satellite precipitation analysis 3B42 precipitation data in mid? high latitudes of China[J]. PLoS One, 10(4): e0120026. DOI: 10.1371/journal.pone.0120026.
[4]Darand M, Amanollahi J, Zandkarimi S, 2017. Evaluation of the performance of TRMM Multi?satellite Precipitation Analysis (TMPA) estimation over Iran[J]. Atmospheric Research, 190: 121-127.
[5]Deblauwe V, Droissart V, Bose R, al et, 2016. Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics[J]. Global Ecology and Biogeography, 25(4): 443-454. DOI: 10.1111/geb.12426.
[6]Fensterseifer C, Allasia D G, Paz A R, 2016. Assessment of the TRMM 3B42 precipitation product in southern Brazil[J]. Journal of the American Water Resources Association, 52(2): 367-375.
[7]Hrachowitz M, Weiler M, 2011. Uncertainty of precipitation estimates caused by sparse gauging networks in a small, mountainous watershed [J]. Journal of Hydrologic Engineering, 16(5): 460-471.
[8]Hu Z Y, Zhou Q M, Chen X, al et, 2017.Variations and changes of annual precipitation in central Asia over the last century[J]. International Journal of Climatology, 37: 157-170.
[9]Hu Q F, Yang D W, Wang Y T, al et, 2013.Accuracy and spatiotemporal variation of high resolution satellite rainfall estimate over the Ganjiang River basin[J]. Science China: Technological Sciences, 56(4): 853-865. DOI: 10.1007/s11431-013-5176-7.
[10]Huffman G J, Bolvin D T, Nelkin E J, al et, 2007.The TRMM multi? satellite precipitation analysis (TMPA): Quasi?global, multi? year, combined?sensor precipitation estimates at fine scales[J]. Journal of Hydrometeorology, 8: 38-55.
[11]Islam N, Uyeda H, 2005. Comparison of TRMM3B42products with surface rainfall over Bangladesh[C]//Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS'05. IEEE, 6: 4112-4115.
[12]Jiang S H, Ren L L, Hong Y, al et, 2012.Comprehensive evaluation of multi?satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method[J]. Journal of Hydrology, 452: 213-225.
[13]Kummerow C, Barnes W, Kozu T, al et, 1998. The tropical rainfall measuring mission (TRMM) sensor package[J]. Journal of Atmospheric and Oceanic Technology, 15(3): 809-817.
[14]Li L, Hong Y, Wang J H, al et, 2009. Evaluation of the real?time TRMM?based multi?satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa[J]. Natural Hazards, 50(1): 109-123. DOI: 10.1007/s11069-008-9324-5.
[15]Li X H, Zhang Q, Xu C Y, 2012. Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang Lake basin[J]. Journal of Hydrology, 426 (7): 28-38.
[16]Liu J Z, Zhu A X, Duan Z, 2012. Evaluation of TRMM 3B42 Precipitation product using rain gauge data in Meichuan Watershed, Poyang Lake Basin, China [J]. Journal of Resources and Ecology, 3(4): 359-366. DOI: 10.584/j.issn.1674-764x.2012.04.009.
[17]Liu Z, 2015.Comparison of versions 6 and 7 3?hourly TRMM multi?satellite precipitation analysis (TMPA) research products[J]. Atmospheric Research, 163: 91-101.
[18]Pombo S, Oliveira R P D, 2015. Evaluation of extreme precipitation estimates from TRMM in Angola[J]. Journal of Hydrology, 523: 663-679.
[19]Prakash S, Mitra A K, Aghakouchak A, al et, 2015. Error characterization of TRMM multi?satellite precipitation analysis (TMPA?3B42) products over India for different seasons[J]. Journal of Hydrology, 529(3): 1302-1312.
[20]Sahoo A K, Sheffield J, Pan M, al et, 2015.Evaluation of the tropical rainfall measuring mission multi?satellite precipitation analysis (TMPA) for assessment of large?scale meteorological drought[J]. Remote Sensing of Environment, 159: 181-193.
[21]Scheel M L M, Rohrer M, Huggel C H, al et, 2011. Evaluation of TRMM multi?satellite precipitation analysis (TMPA) performance in the Central Andes region and its dependency on spatial and temporal resolution[J]. Hydrology and Earth System Sciences, 15(8): 2649-2663. DOI: 10.5194/hess-15-2649-2011.
[22]Simons G, Bastiaanssen W, Ngo L, al et, 2016. Integrating global satellite?derived data products as a pre?analysis for hydrological modelling studies: A case study for the Red River Basin[J]. Remote Sensing, 8(4): 279. DOI: 10.3390/rs8040279.
[23]Tekeli A E, Fouli H, 2016. Evaluation of TRMM satellite?based precipitation indexes for flood forecasting over Riyadh City, Saudi Arabia[J]. Journal of Hydrology, 541: 471-479.
[24]Tong K, Su F, Yang D, al et, 2014. Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau[J]. Journal of Hydrology, 519: 423-437.
[25]Varikoden H, Samah A A, Babu C A, 2010. Spatial and temporal characteristics of rain intensity in the peninsular Malaysia using TRMM rain rate[J]. Journal of Hydrology, 387(3/4): 312-319.
[26]Wang X J, Pang G J, Yang M X, al et, 2018. Precipitation changes in the Qilian Mountains associated with the shifts of regional atmospheric water vapour during 1960-2014[J]. International Journal of Climatology, 38(12): 4355-4368.
[27]Yong B, Chen B, Gourley J J, al et, 2014. Intercomparison of the Version?6 and Version?7 TMPA precipitation products over high and low latitudes basins with independent gauge networks: Is the newer version better in both real?time and post?real?time analysis for water resources and hydrologic extremes?[J]. Journal of Hydrology, 508: 77-87.
[28]蔡研聪, 金昌杰, 王安志, 等, 2014. 中高纬度地区TRMM卫星降雨数据的精度评价[J]. 应用生态学报, 25(11): 3296-3306. DOI: 10.13287/j.1001-9332.2014.0190.
[29]陈桂英, 赵振国, 1998. 短期气候预测评估方法和业务初估[J]. 应用气象学报, 9(2): 178-185.
[30]陈亚宁, 李稚, 范煜婷, 等, 2014. 西北干旱区气候变化对水文水资源影响研究进展[J]. 地理学报, 69(9): 1295-1304. DOI: 10.11821/dlxb201409005.
[31]傅云飞, 刘奇, 自勇, 等, 2008. 基于TRMM卫星探测的夏季青藏高原降水和潜热分析[J].高原山地气象研究, 28 (1): 8-18.
[32]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.
[33]李常斌, 杨林山, 杨文瑾, 等, 2014. 洮河流域土地利用/土地覆被变化及其驱动机制研究[J]. 地理科学, 34(7): 848-855. DOI: 10.13249/j.cnki.sgs.2014.07.016.
[34]李芳洲, 李江南, 2017. 基于TRMM卫星探测的南海及周边地区春夏季降水日变化特征[J]. 热带地理, 37(5): 728-737. DOI: 10.13284/j.cnki.rddl.002985.
[35]李剑锋, 佘文婧, 江善虎, 等, 2014. TRMM卫星降水数据在老哈河流域的精度评估[J].水资源与水工程学报, 25 (5): 89-97. DOI: 10.11705/j.issn.1672-643X.2014.05.020.
[36]李蒙, 秦天玲, 刘少华, 等, 2017. 怒江上游TRMM 3B42 V7 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.
[37]李麒崙, 张万昌, 易路, 等, 2018. GPM与TRMM降水数据在中国大陆的精度评估与对比[J]. 水科学进展, 29(3): 303-313. DOI: 10.14042/j.cnki.32.1309.2018.03.001.
[38]李琼, 杨梅学, 万国宁, 等, 2016. TRMM3B43降水数据在黄河源区的适用性评价[J]. 冰川冻土, 38(3): 620-633. DOI: 10.7522/j.issn.1000-0240.2016.0069.
[39]林彤, 郑有飞, 李特, 等, 2018. 基于卫星资料的中国西北地区冰云特征分析[J]. 高原气象, 37(4): 1051-1060. DOI: 10.7522/j.issn.1000-0534.2017.00088．
[40]刘俊峰, 陈仁升, 韩春坛, 等, 2010.多卫星遥感降水数据精度评价[J].水科学进展, 21(3): 343-348. DOI: 10.14042/j.cnki.32. 1309.2010.03.002.
[41]刘少华, 严登华, 王浩, 等, 2016. 中国大陆流域分区TRMM降水质量评价[J]. 水科学进展, 27(5): 639-651. DOI: 10. 14042 /j. cnki.32.1309.2016.05.001.
[42]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: 10.11849/zrzyxb.2013.08.014.
[43]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.
[44]毛江玉, 吴国雄, 2012. 基于TRMM卫星资料揭示的亚洲季风区夏季降雨日变化[J]. 中国科学（地球科学）, 42 (4): 564-576.
[45]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.
[46]钱正安, 蔡英, 宋敏红, 等, 2018. 中国西北旱区暴雨水汽输送研究进展[J]. 高原气象, 37(3): 577-590. DOI: 10.7522/j.issn. 1000-0534.2018.00032.
[47]唐国强, 李哲, 薛显武, 等, 2015. 赣江流域TRMM遥感降水对地面站点观测的可替代性[J]. 水科学进展, 26(3): 340-346. DOI: 10.14042 /j. cnki.32.1309.2015.03.005.
[48]王佳伶, 陈华, 许崇育, 等, 2016. TRMM卫星降雨数据的精度及径流模拟评估[J]. 水资源研究, 5(5): 434-445.
[49]王莺, 张强, 王劲松, 等, 2017. 基于分布式水文模型(SWAT)的土地利用和气候变化对洮河流域水文影响特征[J]. 中国沙漠, 37(1): 175-185. DOI: 10.7522/j.issn.1000-694X.2015.00189.
[50]王兆礼, 钟睿达, 赖成光, 等, 2017. TRMM卫星降水反演数据在珠江流域的适用性研究——以东江和北江为例[J].水科学进展, 28(2): 174-182. DOI: 10.14042 /j. cnki. 32. 1309. 2017. 02. 002.
[51]谢红霞, 刘旭星, 隋兵, 等, 2017. TRMM降雨数据在湖南省长株潭地区的适用性[J]. 水土保持通报, 37(3): 295-301. DOI: 10.13961/j.cnki.stbctb.2017.03.051.
[52]薛童, 管兆勇, 徐建军, 等, 2017. ATMS和CrIS卫星资料同化对青藏高原天气预报的影响[J]. 高原气象, 36( 4) : 912-929. DOI: 10.7522 /j.issn.1000-0534.2016.00087．
[53]张济世, 康尔泗, 蓝永超, 等, 2003. 50a来洮河流域降水径流变化趋势分析[J]. 冰川冻土, 25(1): 77-82.
[54]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．
[55]张晓晓, 张钰, 徐浩杰, 2013. 1960-2010年洮河流域径流变化趋势及影响因素[J]. 兰州大学学报(自然科学版), 49(1): 38-42.
[56]赵庆云, 傅朝, 刘新伟, 等, 2017. 西北东部暖区大暴雨中尺度系统演变特征[J]. 高原气象, 36(3): 697-704. DOI: 10.7522/j.issn.1000-0534.2016.00140．
Options
Outlines

/

〈 〉

The system is designed and developed by Beijing magtec Technology Development Co., Ltd. with technical support: support@magtech.com.cn