Advertisement

Climate Dynamics

, Volume 51, Issue 9–10, pp 3311–3331 | Cite as

Evaluation of precipitation trends from high-resolution satellite precipitation products over Mainland China

  • Fengrui ChenEmail author
  • Yongqi Gao
Article

Abstract

Many studies have reported the excellent ability of high-resolution satellite precipitation products (0.25° or finer) to capture the spatial distribution of precipitation. However, it is not known whether the precipitation trends derived from them are reliable. For the first time, we have evaluated the annual and seasonal precipitation trends from two typical sources of high-resolution satellite-gauge products, TRMM 3B43 and PERSIANN-CDR, using rain gauge observations over China, and they were also compared with those from gauge-only products (0.25° and 0.5° precipitation products, hereafter called CN25 and CN50). The evaluation focused mainly on the magnitude, significance, sign, and relative order of the precipitation trends, and was conducted at gridded and regional scales. The following results were obtained: (1) at the gridded scale, neither satellite-gauge products precisely measure the magnitude of precipitation trends but they do reproduce their sign and relative order; regarding capturing the significance of trends, they exhibit relatively acceptable performance only over regions with a sufficient amount of significant precipitation trends; (2) at the regional scale, both satellite-gauge products generally provide reliable precipitation trends, although they do not reproduce the magnitude of trends in winter precipitation; and (3) overall, CN50 and TRMM 3B43 outperform others in reproducing all four aspects of the precipitation trends. Compared with CN25, PERSIANN-CDR performs better in determining the magnitude of precipitation trends but marginally worse in reproducing their sign and relative order; moreover, both of them are at a level in capturing the significance of precipitation trends.

Keywords

Precipitation trends TRMM PERSIANN Satellite-gauge precipitation data 

Notes

Acknowledgements

This study was supported by the Natural Science Foundation of China under Grant no. 41401503, the Innovative Research Team in University of Henan Provinces under Grant No. 16IRTSTHN012, the NordForsk-funded Project GREENICE (61841): Impacts of Sea-Ice and Snow-Cover Changes on Climate, Green Growth, and Society, and the China Scholarship Council.

References

  1. Adler RF, Huffman GJ, Chang A, Ferraro R, Xie PP, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167CrossRefGoogle Scholar
  2. Arriaga-Ramírez S, Cavazos T (2010) Regional trends of daily precipitation indices in northwest Mexico and southwest United States. J Geophys Res 115:1–10CrossRefGoogle Scholar
  3. Ashouri H, Hsu KL, Sorooshian S, Braithwaite DK, Knapp KR, Cecil LD, Nelson BR, Prat OP (2015) PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. B Am Meteorol Soc 96:69–83CrossRefGoogle Scholar
  4. Behrangi A, Stephens G, Adler RF, Huffman GJ, Lambrigtsen B, Lebsock M (2014) An update on the oceanic precipitation rate and its zonal distribution in light of advanced observations from space. J Clim 27:3957–3965CrossRefGoogle Scholar
  5. Boushaki FI, Hsu KL, Sorooshian S, Park GH, Mahani S, Shi W (2009) Bias adjustment of satellite precipitation estimation using ground-based measurement: a case study evaluation over the southwestern United States. J Hydrometeorol 10:1231–1242CrossRefGoogle Scholar
  6. Chappell A, Renzullo LJ, Raupach TH, Haylock M (2013) Evaluating geostatistical methods of blending satellite and gauge data to estimate near real-time daily rainfall for Australia. J Hydrol 493:105–114CrossRefGoogle Scholar
  7. Chen F, Li X (2016) Evaluation of IMERG and TRMM 3B43 monthly precipitation products over Mainland China. Remote Sen-Basel 8:1–18Google Scholar
  8. Chen S, Hong Y, Cao Q, Gourley JJ, Kirstetter PE, Yong B, Tian Y, Zhang Z, Shen Y, Hu J (2013a) Similarity and difference of the two successive V6 and V7 TRMM multisatellite precipitation analysis performance over China. J Geophys Res 118:1–15Google Scholar
  9. Chen S, Hong Y, Gourley JJ, Huffman GJ, Tian Y, Cao Q, Yong B, Kirstetter PE, Hu J, Hardy J (2013b) Evaluation of the successive V6 and V7 TRMM multisatellite precipitation analysis over the Continental United States. Water Resour Res 49:8174–8186CrossRefGoogle Scholar
  10. Chen F, Liu Y, Liu Q, Qin F (2015) A statistical method based on remote sensing for the estimation of air temperature in China. Int J Climatol 35:2131–2143CrossRefGoogle Scholar
  11. de Barros Soares D, Lee H, Loikith PC, Barkhordarian A, Mechoso CR (2016) Can significant trends be detected in surface air temperature and precipitation over South America in recent decades? Int J Climatol 37:1483–1493CrossRefGoogle Scholar
  12. Dee D, Uppala S, Simmons A, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M, Balsamo G, Bauer P (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteor Soc 137:553–597CrossRefGoogle Scholar
  13. Di Piazza A, Lo Conti F, Noto LV, Viola F, La Loggia G (2011) Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. Int J Appl Earth Obs 13:396–408CrossRefGoogle Scholar
  14. Duan W, He B, Takara K, Luo P, Hu M, Alias NE, Nover D (2015) Changes of precipitation amounts and extremes over Japan between 1901 and 2012 and their connection to climate indices. Clim Dyn 45:2273–2292CrossRefGoogle Scholar
  15. Duan Z, Liu J, Tuo Y, Chiogna G, Disse M (2016) Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Sci Total Environ 573:1536–1553CrossRefGoogle Scholar
  16. Ebert EE, Janowiak JE, Kidd C (2007) Comparison of near-real-time precipitation estimates from satellite observations and numerical models. B Am Meteorol Soc 88:47–64CrossRefGoogle Scholar
  17. Franchito SH, Rao VB, Vasques AC, Santo CM, Conforte JC (2009) Validation of TRMM precipitation radar monthly rainfall estimates over Brazil. J Geophys Res 114:1–9CrossRefGoogle Scholar
  18. Gao Y, Liu M (2013) Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau. Hydrol Earth Syst Sc 17:837–849CrossRefGoogle Scholar
  19. Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New YorkGoogle Scholar
  20. Grody NC, Weng F (2008) Microwave emission and scattering from deserts: theory compared with satellite measurements. IEEE T Geosci Remote 46:361–375CrossRefGoogle Scholar
  21. Harris I, Jones P, Osborn T, Lister D (2014) Updated high-resolution grids of monthly climatic observations—the CRU TS3. 10 Dataset. Int J Climatol 34:623–642CrossRefGoogle Scholar
  22. Haylock M, Hofstra N, Klein Tank A, Klok E, Jones P, New M (2008) A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J Geophys Res 113:1–12CrossRefGoogle Scholar
  23. Hengl T (2007) A Practical Guide to Geostatistical Mapping of Environmental Variables. Office for Official Publication of the European Communities, LuxembourgGoogle Scholar
  24. Hession SL, Moore N (2011) A spatial regression analysis of the influence of topography on monthly rainfall in East Africa. Int J Climatol 31:1440–1456CrossRefGoogle Scholar
  25. Hipel KW, McLeod AI (1994) Time series modelling of water resources and environmental systems. Elesvier, AmsterdamGoogle Scholar
  26. Hofer M, Marzeion B, Mölg T (2012) Comparing the skill of different reanalyses and their ensembles as predictors for daily air temperature on a glaciated mountain (Peru). Clim Dyn 39:1969–1980CrossRefGoogle Scholar
  27. Hong Y, Gochis D, Cheng J, Hsu K, Sorooshian S (2007) Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network. J Hydrometeorol 8:469–482CrossRefGoogle Scholar
  28. Houghton J, Meira Filho L, Callander B, Harris N, Kattenberg A, Maskell K (1996) Climate change. The IPCC second assessment report. Cambridge University Press, New YorkGoogle Scholar
  29. Hsu K, Gao X, Sorooshian S, Gupta HV (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol Clim 36:1176–1190CrossRefGoogle Scholar
  30. Huffman GJ, Bolvin DT (2013) TRMM and other data precipitation data set documentation. NASA Goddard Space Flight Center. https://pmm.nasa.gov/sites/default/files/document_files/ 3B42_3B43_doc_V7.pdf. Accessed 5 June 2017
  31. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55CrossRefGoogle Scholar
  32. Huffman GJ, Adler RF, Bolvin DT, Gu G (2009) Improving the global precipitation record: GPCP version 2.1. Geophys Res Lett 36:1–5CrossRefGoogle Scholar
  33. Hutchinson MF (1995) Interpolating mean rainfall using thin plate smoothing splines. Int J Geogr Inf Syst 9:385–403CrossRefGoogle Scholar
  34. Jain S, Kumar V, Saharia M (2013) Analysis of rainfall and temperature trends in northeast India. Int J Climatol 33:968–978CrossRefGoogle Scholar
  35. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J (1996) The NCEP/NCAR 40-year reanalysis project. B Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
  36. Kendall MG (1975) Rank correlation methods. Charles Griffin, LondonGoogle Scholar
  37. Kishore P, Jyothi S, Basha G, Rao S, Rajeevan M, Velicogna I, Sutterley TC (2016) Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends. Clim Dyn 46:541–556CrossRefGoogle Scholar
  38. Kumar S, Merwade V, Kinter JL, Niyogi D (2013) Evaluation of temperature and precipitation trends and long-term persistence in CMIP5 twentieth-century climate simulations. J Clim 26:4168–4185CrossRefGoogle Scholar
  39. Li J, Heap AD (2014) Spatial interpolation methods applied in the environmental sciences: a review. Environ Modell Softw 53:173–189CrossRefGoogle Scholar
  40. Lin R, Zhou T, Qian Y (2014) Evaluation of global monsoon precipitation changes based on five reanalysis datasets. J Clim 27:1271–1289CrossRefGoogle Scholar
  41. Liu Z (2016) Comparison of Integrated multisatellite retrievals for GPM (IMERG) and TRMM multisatellite precipitation analysis (TMPA) monthly precipitation products: initial results. J Hydrometeorol 17:777–790CrossRefGoogle Scholar
  42. Longobardi A, Villani P (2010) Trend analysis of annual and seasonal rainfall time series in the Mediterranean area. Int J Climatol 30:1538–1546Google Scholar
  43. Ma L, Zhang T, Frauenfeld OW, Ye B, Yang D, Qin D (2009) Evaluation of precipitation from the ERA-40, NCEP-1, and NCEP-2 reanalyses and CMAP-1, CMAP-2, and GPCP-2 with ground-based measurements in China. J Geophys Res 114:1–20Google Scholar
  44. Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259CrossRefGoogle Scholar
  45. Manzanas R, Amekudzi L, Preko K, Herrera S, Gutiérrez JM (2014) Precipitation variability and trends in Ghana: an intercomparison of observational and reanalysis products. Clim Dyn 124:805–819Google Scholar
  46. New M, Hulme M, Jones P (2000) Representing twentieth-century space–time climate variability. Part II: development of 1901–96 monthly grids of terrestrial surface climate. J Clim 13:2217–2238CrossRefGoogle Scholar
  47. NMIC (2012) Assessment Report of China’s Ground Precipitation 0.5°*0.5° Gridded Dataset (V2.0). National Meteorological Information Center. http://image.data.cma.cn/static/subject/doc/ SURF_CLI_CHN_PRE_MON_GRID_0.5_ASSESSMENT.pdf. Accessed 5 June 2017
  48. Romilly TG, Gebremichael M (2011) Evaluation of satellite rainfall estimates over Ethiopian river basins. Hydrol Earth Syst Sc 15:1505–1514CrossRefGoogle Scholar
  49. Schamm K, Ziese M, Becker A, Finger P, Meyer-Christoffer A, Schneider U, Schröder M, Stender P (2014) Global gridded precipitation over land: a description of the new GPCC first guess daily product. Earth Syst Sci Data 6:49–60CrossRefGoogle Scholar
  50. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389CrossRefGoogle Scholar
  51. Shen Y, Xiong A, Wang Y, Xie P (2010) Performance of high-resolution satellite precipitation products over China. J Geophys Res 115:1–17CrossRefGoogle Scholar
  52. Sorooshian S, Hsu KL, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. B Am Meteorol Soc 81:2035–2046CrossRefGoogle Scholar
  53. Tang G, Ma Y, Long D, Zhong L, Hong Y (2016) Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. J Hydrol 533:152–167CrossRefGoogle Scholar
  54. Vila DA, De Goncalves L, Toll DL, Rozante JR (2009) Statistical evaluation of combined daily gauge observations and rainfall satellite estimates over continental South America. J Hydrometeorol 10:533–543CrossRefGoogle Scholar
  55. Wagner PD, Fiener P, Wilken F, Kumar S, Schneider K (2012) Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J Hydrol 464:388–400CrossRefGoogle Scholar
  56. Wu J, Gao X (2013) A gridded daily observation dataset over China region and comparison with the other datasets. Chin J Geophys 56:1102–1111Google Scholar
  57. Wu J, Gao X, Giorgi F, Chen D (2017) Changes of effective temperature and cold/hot days in late decades over China based on a high resolution gridded observation dataset. Int J Climatol 37:788–800CrossRefGoogle Scholar
  58. Xie P, Arkin PA (1998) Global monthly precipitation estimates from satellite-observed outgoing longwave radiation. J Clim 11:137–164CrossRefGoogle Scholar
  59. Xie P, Xiong AY (2011) A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses. J Geophys Res 116:1–14CrossRefGoogle Scholar
  60. Xie P, Chen M, Yang S, Yatagai A, Hayasaka T, Fukushima Y, Liu C (2007) A gauge-based analysis of daily precipitation over East Asia. J Hydrometeorol 8:607–626CrossRefGoogle Scholar
  61. Xie P, Chen M, Shi W (2010) CPC unified gauge-based analysis of global daily precipitation. 24th Conf. on Hydrology, Atlanta, GA, Amer. Meteor. Soc. https://ams.confex.com/ams/90annual%20/techprogram/paper_163676.htm. Accessed 5 June 2017
  62. Xu Y, Gao X, Shen Y, Xu C, Shi Y, Giorgi F (2009) A daily temperature dataset over China and its application in validating a RCM simulation. Adv Atmos Sci 26:763–772CrossRefGoogle Scholar
  63. Xu Z, Fan K, Wang H (2015) Decadal variation of summer precipitation over China and associated atmospheric circulation after the late 1990s. J Clim 28:4086–4106CrossRefGoogle Scholar
  64. Yang Y, Luo Y (2014) Evaluating the performance of remote sensing precipitation products CMORPH, PERSIANN, and TMPA, in the arid region of northwest China. Theor appl climatol 118:429–445CrossRefGoogle Scholar
  65. Yatagai A, Arakawa O, Kamiguchi K, Kawamoto H, Nodzu MI, Hamada A (2009) A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges. SOLA 5:137–140CrossRefGoogle Scholar
  66. Ye JS (2014) Trend and variability of China’s summer precipitation during 1955–2008. Int J Climatol 34:559–566CrossRefGoogle Scholar
  67. Yin Z, Zhang X, Liu X, Colella M, Chen X (2008) An assessment of the biases of satellite rainfall estimates over the Tibetan Plateau and correction methods based on topographic analysis. J Hydrometeorol 9:301–326CrossRefGoogle Scholar
  68. Yong B, Liu D, Gourley JJ, Tian Y, Huffman GJ, Ren L, Hong Y (2015) Global view of real-time TRMM multisatellite precipitation analysis: implications for its successor global precipitation measurement mission. B Am Meteorol Soc 96:283–296CrossRefGoogle Scholar
  69. You Q, Min J, Zhang W, Pepin N, Kang S (2015) Comparison of multiple datasets with gridded precipitation observations over the Tibetan Plateau. Clim Dyn 45:791–806CrossRefGoogle Scholar
  70. Yu R, Zhou T, Xiong A, Zhu Y, Li J (2007) Diurnal variations of summer precipitation over contiguous China. Geophys Res Lett 34:1–4Google Scholar
  71. Yu L, Furevik T, Otterå OH, Gao Y (2015) Modulation of the Pacific Decadal Oscillation on the summer precipitation over East China: a comparison of observations to 600-years control run of Bergen Climate Model. Clim Dyn 44:475–494CrossRefGoogle Scholar
  72. Zhai P, Zhang X, Wan H, Pan X (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18:1096–1108CrossRefGoogle Scholar
  73. Zhu Y, Wang H, Ma J, Wang T, Sun J (2015) Contribution of the phase transition of Pacific Decadal Oscillation to the late 1990s’ shift in East China summer rainfall. J Geophys Res 120:8817–8827CrossRefGoogle Scholar
  74. Zhu Q, Xuan W, Liu L, Xu P (2016) Evaluation and hydrological application of precipitation estimates derived from PERSIANN-CDR, TRMM 3B42V7, and NCEP-CFSR over humid regions in China. Hydrol Process 30:3061–3083CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Collaborative Innovation Center of Yellow River CivilizationHenan UniversityKaifengPeople’s Republic of China
  2. 2.Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of EducationHenan UniversityKaifengPeople’s Republic of China
  3. 3.Nansen Environmental and Remote Sensing Center/Bjerknes Center for Climate ResearchBergenNorway
  4. 4.Nansen-Zhu International Research Center, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingPeople’s Republic of China

Personalised recommendations