Advertisement

Theoretical and Applied Climatology

, Volume 132, Issue 1–2, pp 621–637 | Cite as

Statistical evaluation of the performance of gridded monthly precipitation products from reanalysis data, satellite estimates, and merged analyses over China

  • Xueliang Deng
  • Suping Nie
  • Weitao Deng
  • Weihua Cao
Original Paper

Abstract

In this study, we compared the following four different gridded monthly precipitation products: the National Centers for Environmental Prediction version 2 (NCEP-2) reanalysis data, the satellite-based Climate Prediction Center Morphing technique (CMORPH) data, the merged satellite-gauge Global Precipitation Climatology Project (GPCP) data, and the merged satellite-gauge-model data from the Beijing Climate Center Merged Estimation of Precipitation (BMEP). We evaluated the performances of these products using monthly precipitation observations spanning the period of January 2003 to December 2013 from a dense, national, rain gauge network in China. Our assessment involved several statistical techniques, including spatial pattern, temporal variation, bias, root-mean-square error (RMSE), and correlation coefficient (CC) analysis. The results show that NCEP-2, GPCP, and BMEP generally overestimate monthly precipitation at the national scale and CMORPH underestimates it. However, all of the datasets successfully characterized the northwest to southeast increase in the monthly precipitation over China. Because they include precipitation gauge information from the Global Telecommunication System (GTS) network, GPCP and BMEP have much smaller biases, lower RMSEs, and higher CCs than NCEP-2 and CMORPH. When the seasonal and regional variations are considered, NCEP-2 has a larger error over southern China during the summer. CMORPH poorly reproduces the magnitude of the precipitation over southeastern China and the temporal correlation over western and northwestern China during all seasons. BMEP has a lower RMSE and higher CC than GPCP over eastern and southern China, where the station network is dense. In contrast, BMEP has a lower CC than GPCP over western and northwestern China, where the gauge network is relatively sparse.

Notes

Acknowledgements

This work was jointly supported by the National Natural Science Foundation of China (Grant Nos. 41275076, 40905046, 41305057, and 41175086), the Beijing Natural Science Foundation (Grant No. 8144046), the Anhui Research Project in the Public Interest (Grant No. 1604f0804003), and the China Special Fund for Meteorological Research in the Public Interest (Grant No. GYHY201406039).

References

  1. Adler RF, Negri AJ (1988) A satellite infrared technique to estimate tropical convective and stratiform rainfall. J Appl Meteorol 27(1):30–51CrossRefGoogle Scholar
  2. Adler RF et al (2003) The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167CrossRefGoogle Scholar
  3. Annamalai H, Slingo JM, Sperber KR, Hodges K (1999) The mean evolution and variability of the Asian summer monsoon: comparison of ECMWF and NCEP-NCAR reanalyses. Mon Wea Rev 127(6):1157–1186CrossRefGoogle Scholar
  4. Arkin P, Xie P (1994) The Global Precipitation Climatology Project: first algorithm intercomparison project. Bull. Amer. Meteor. Soc. 75:401–419CrossRefGoogle Scholar
  5. Berg W, L’Ecuyer T, Haynes JM (2010) The distribution of rainfall over oceans from spaceborne radars. J. Appl. Meteor. Climatol. 49(3):535–543CrossRefGoogle Scholar
  6. Bolvin DT, Adler RF, Huffman GJ, Nelkin EJ, Poutiainen JP (2009) Comparison of GPCP monthly and daily precipitation estimates with high-latitude gauge observations. J Appl Meteor Climatol 48:1843–1857CrossRefGoogle Scholar
  7. Bromwich DH, Nicolas JP, Monaghan AJ (2011) An assessment of precipitation changes over Antarctica and the Southern Ocean since 1989 in contemporary global reanalyses. J Clim 24:4189–4209CrossRefGoogle Scholar
  8. Cavazos T, Hewitson BC (2005) Performance of NCEP-NCAR reanalysis variables in statistical downscaling of daily precipitation. Clim Res 28(2):95–107Google Scholar
  9. China Meteorological Administration 2008 National Meteorological Information Center data documentation for China Ground Climate Dataset. Online is available: http://data.cma.cn/site/index.html
  10. Ebert EE, Janowiak JE, Kidd C (2007) Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc. 88(1):47–64CrossRefGoogle Scholar
  11. Grody NC, Weng F (2008) Microwave emission and scattering from deserts: theory compared with satellite measurements. IEEE Trans Geosci Remote Sens 46(2):361–375CrossRefGoogle Scholar
  12. Habib E, Haile AT, Tian Y, Joyce RJ (2012) Evaluation of the high-resolution CMORPH satellite rainfall product using dense rain gauge observations and radar-based estimates. J Hydrometeorol 13(6):1784–1798CrossRefGoogle Scholar
  13. Hodges KI, Lee RW, Bengtsson L (2011) A comparison of extratropical cyclones in recent reanalyses ERA-Interim, NASA MERRA, NCEP CFSR, and JRA-25. J Clim 24(18):4888–4906CrossRefGoogle Scholar
  14. Hsu KL, Gao X, Sorooshian S, Gupta HV (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol 36(9):1176–1190CrossRefGoogle Scholar
  15. Huffman GJ, Klepp C (2011) Fifth workshop of the international precipitation working group. Bull. Amer. Meteor. Soc. 92:ES54–ES57CrossRefGoogle Scholar
  16. Huffman GJ, Adler RF, Arkin P, Chang A, Ferraro R, Gruber A, Janowiak J, McNab A, Rudolf B, Schneider U (1997) The Global Precipitation Climatology Project (GPCP) combined precipitation dataset. Bull. Amer. Meteor. Soc. 78:5–20CrossRefGoogle Scholar
  17. Huffman GJ, Adler RF, Morrissey MM, Bolvin DT, Curtis S, Joyce R, McGavock B, Susskind J (2001) Global precipitation at one-degree daily resolution from multisatellite observations. J Hydrometeorol 2:36–50CrossRefGoogle Scholar
  18. Huffman GJ et al (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55CrossRefGoogle Scholar
  19. Janowiak JE, Gruber A, Kondragunta CR, Livezey RE, Huffman GJ (1998) A comparison of the NCEP-NCAR reanalysis precipitation and the GPCP rain gauge-satellite combined dataset with observational error considerations. J Clim 11:2960–2979CrossRefGoogle Scholar
  20. Jiang ZH, Lu Y, Ding Y (2013) Analysis of the high-resolution merged precipitation products over China based on the temporal and spatial structure score indices. Acta Meteorol Sin 71(5):891–900Google Scholar
  21. Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimation from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503CrossRefGoogle Scholar
  22. Juarez RI, Li WH, Fu R, Fernandes K, Cardoso AD (2009) Comparison of precipitation datasets over the tropical South American and African continents. J Hydrometeorol 10:289–299CrossRefGoogle Scholar
  23. Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc. 77:437–471CrossRefGoogle Scholar
  24. Kanamitsu M, Ebisuzaki W, Woollen J, Yang S, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP-DOE AMIP-II reanalysis (R-2). Bull Amer Meteor Soc 83:1631–1643CrossRefGoogle Scholar
  25. Kidd C, Bauer P, Turk J, Huffman GJ, Joyce R, Hsu KL, Braithwaite D (2012) Intercomparison of high-resolution precipitation products over northwest Europe. J Hydrometeorol 13(1):67–83CrossRefGoogle Scholar
  26. Krajewski WF, Ciach GJ, McCollum JR, Bacotiu C (2000) Initial validation of the Global Precipitation Climatology Project over the United States. J Appl Meteorol 39:1071–1087CrossRefGoogle Scholar
  27. Kumar A, Zhang L, Wang W (2013) Sea surface temperature-precipitation relationship in different reanalyses. Mon. Wea. Rev. 141:1118–1123CrossRefGoogle Scholar
  28. Kummerow C, Giglio L (1994) A passive microwave technique for estimating rainfall and vertical structure information from space. Part II: applications to SSM/I data. J Appl Meteorol 33(1):19–34CrossRefGoogle Scholar
  29. Lorenz C, Kunstmann H (2012) The hydrological cycle in three state-of-the-art reanalyses: intercomparison and performance analysis. J Hydrometeorol 13:1397–1420CrossRefGoogle Scholar
  30. Ma L, Zhang T, Frauenfeld O, 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:D09105. doi: 10.1029/2008JD011178 Google Scholar
  31. Naumann G, Barbosa P, Carrao H, Singleton A, Vogt J (2012) Monitoring drought conditions and their uncertainties in Africa using TRMM data. J. Appl. Meteor. Climatol. 51:1867–1874CrossRefGoogle Scholar
  32. Nie S, Luo Y, Zhu J (2008) Trends and scales of observed soil moisture variations in China. Adv Atmos Sci 25(1):43–58CrossRefGoogle Scholar
  33. Nie S, Luo Y, Li W, Wu T, Shi X, Wang Z (2012) Quality control and analysis of global gauge-based daily precipitation dataset from 1980 to 2009. Adv Atmos Sci 3(1):45–53Google Scholar
  34. Nie S, Luo Y, Wu T, Shi X, Wang Z (2015) A merging scheme for constructing daily precipitation analyses based on objective bias-correction and error estimation techniques. J Geophys Res Atmos 120. doi: 10.1002/2015JD023347
  35. Nie S et al (2016) A strategy for merging objective estimates of global daily precipitation from gauge observations, satellite estimates, and numerical predictions. Adv Atmos Sci 33(7):889–904CrossRefGoogle Scholar
  36. Onogi K et al (2007) The JRA-25 reanalysis. J Meteor Soc Japan 85:369–432CrossRefGoogle Scholar
  37. Peña-Arancibia JL, Van Dijk AI, Renzullo LJ, Mulligan M (2013) Evaluation of precipitation estimation accuracy in reanalyses, satellite products, and an ensemble method for regions in Australia and South and East Asia. J Hydrometeorol 14(4):1323–1333CrossRefGoogle Scholar
  38. Pfeifroth U, Mueller R, Ahrens B (2013) Evaluation of satellite-based and reanalysis precipitation data in the tropical Pacific. J. Appl. Meteor. Climatol. 52:634–644CrossRefGoogle Scholar
  39. Ploshay JJ, Lau N (2010) Simulation of the diurnal cycle in tropical rainfall and circulation during boreal summer with a high-resolution GCM. Mon. Wea. Rev. 138:3434–3453CrossRefGoogle Scholar
  40. Schlosser CA, Houser PR (2007) Assessing a satellite-era perspective of the global water cycle. J Clim 20:1316–1338CrossRefGoogle Scholar
  41. Serreze MC, Hurst CM (2000) Representation of mean Arctic precipitation from NCEP-NCAR and ERA reanalyses. J Clim 13:182–201CrossRefGoogle Scholar
  42. Shen Y, Xiong A, Wang Y, Xie P (2010) Performance of high-resolution satellite precipitation products over China. J Geophys Res Atmos 115:355–365CrossRefGoogle Scholar
  43. Shin DB, Kim JH, Park HJ (2011) Agreement between monthly precipitation estimates from TRMM satellite, NCEP reanalysis, and merged gauge-satellite analysis. J Geophys Res Atmos 116:971–978CrossRefGoogle Scholar
  44. Silva VB, Kousky VE, Shi W, Higgins RW (2007) An improved gridded historical daily precipitation analysis for Brazil. J Hydrometeorol 8:847–861CrossRefGoogle Scholar
  45. Sohn BJ, Han HJ, Seo EK (2010) Validation of satellite-based high-resolution rainfall products over the Korean Peninsula using data from a dense rain gauge network. J Appl Meteorol 49(4):701–714CrossRefGoogle Scholar
  46. Uppala SM et al (2005) The ERA-40 re-analysis. Quart J Roy Meteor Soc 131:2961–3012CrossRefGoogle Scholar
  47. Voisin N, Wood AW, Lettenmaier DP (2008) Evaluation of precipitation products for global hydrological prediction. J Hydrometeorol 9(3):388–407CrossRefGoogle Scholar
  48. Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc. 78:2539–2558CrossRefGoogle Scholar
  49. Xu S, Niu Z, Shen Y, Kuang D (2014) A research into the characters of CMORPH remote sensing precipitation error in China. Remote Sensing Technology and Application 29(2):189–194Google Scholar
  50. Yin X, Gruber A, Arkin P (2004) Comparison of the GPCP and CMAP merged gauge-satellite monthly precipitation products for the period 1979–2001. J Hydrometeorol 5(6):1207–1222CrossRefGoogle Scholar
  51. Yu R, Li W, Zhang X, Liu Y, Yu Y, Liu H, Zhou T (2000) Climatic features related to eastern China rainfalls in the NCAR CCM3. Adv Atmos Sci 17(4):503–518CrossRefGoogle Scholar
  52. 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
  53. Zhao T, Fu C (2006) Comparison of products from ERA-40, NCEP-2, and CRU with station data for summer precipitation over China. Adv Atmos Sci 23:593–604CrossRefGoogle Scholar
  54. Zhou T, Yu R, Chen H, Dai A, Pan Y (2008) Summer precipitation frequency, intensity, and diurnal cycle over China: a comparison of satellite data with rain gauge observations. J Clim 21(16):3997–4010CrossRefGoogle Scholar
  55. Zi Y, Xu YL, Fu YF (2007) Climatological comparison studies between GPCP and rain gauges precipitations in China. Acta Meteorol Sin 65(1):63–74Google Scholar

Copyright information

© Springer-Verlag Wien 2017

Authors and Affiliations

  • Xueliang Deng
    • 1
  • Suping Nie
    • 2
  • Weitao Deng
    • 3
  • Weihua Cao
    • 4
  1. 1.Key Laboratory of Atmospheric Science and Satellite Remote SensingAnhui Institute of MeteorologyHefeiChina
  2. 2.National Climate CenterChina Meteorological AdministrationBeijingChina
  3. 3.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)Nanjing University of Information Science and TechnologyNanjingChina
  4. 4.Institute of Urban MeteorologyChina Meteorological AdministrationBeijingChina

Personalised recommendations