Theoretical and Applied Climatology

, Volume 129, Issue 3–4, pp 711–727 | Cite as

Evaluation of TMPA 3B42 Precipitation Estimates during the Passage of Tropical Cyclones over New Caledonia

Original Paper


This study evaluates the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 version 7 (V7) estimates of tropical cyclone (TC) rainfall over New Caledonia using the island rain gauge observations as the ground-truth reference. Several statistical measures and techniques are utilised to characterise the difference and similarity between TMPA and the gauge observations. The results show that TMPA has skill in representing the observed rainfall during the passage of TCs. TMPA overestimates light rainfall events and underestimates moderate to higher rainfall events. The skill deteriorates with increasing elevation, as underestimation by TMPA is greater at higher altitudes. The ability of TMPA also varies with TC intensity and distance from the TC centre, whereby it is more skilful for less intense TCs (category 1-2) and near the TC centre than in the outer rainbands. The ability of TMPA varies from case to case but a better performance is shown for TCs with a higher average rainfall. Finally, case studies of TC Vania (2011), TC Innis (2009), and TC Erica (2003) show that TMPA has the ability to represent the spatial distribution of the observed rainfall, but it tends to underestimate the higher rainfall events.


Root Mean Square Error Tropical Cyclone Tropical Rainfall Measure Mission Rain Rate Rainfall Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Arkin P, Janowiak JE, Ebert EE (2006) Pilot Evaluation of High Resolution Precipitation Products (PEHRPP): A Contribution to GPM Planning. Fifth Global Precipitation Mission (GPM) Planning Workshop, Tokyo, Japan, JAXAGoogle Scholar
  2. Australian Government Bureau of Meteorology (2003) Tropical Cyclone Erica. Accessed 10 November 2014
  3. Chang LT-C, McAneney J, Cheung KKW (2013) Case Study of TRMM Satellite Rainfall Estimation for Landfalling Tropical Cyclones: Issues and Challenges. Trop Cyclone Res Rev 2:109–123Google Scholar
  4. Chen S, Hong Y, Cao Q, Kirstetter P-E, Gourley JJ, Qi Y, Zhang J, Howard K, Hu J, Wang J (2013b) Performance evaluation of radar and satellite rainfalls for Typhoon Morakot over Taiwan: Are remote-sensing products ready for gauge denial scenario of extreme events? J Hydrol 506:4–13. doi: 10.1016/j.jhydrol.2012.12.026 CrossRefGoogle Scholar
  5. Chen Y, Ebert EE, Walsh KJE, Davidson NE (2013a) Evaluation of TMPA 3B42 daily precipitation estimates of tropical cyclone rainfall over Australia. J Geophys Res Atmos 118:11966–11978. doi: 10.1002/2013JD020319 CrossRefGoogle Scholar
  6. Chen Y, Ebert EE, Walsh KJE, Davidson NE (2013c) Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAIN data. J Geophys Res Atmos 118:2184–2196. doi: 10.1002/jgrd.50250 CrossRefGoogle Scholar
  7. Dare R (2013) Seasonal tropical cyclone rain volumes over Australia. J Clim 26:5958–5964. doi: 10.1175/JCLI-D-12-00778.1 CrossRefGoogle Scholar
  8. Dowdy AJ, Qi L, Jones D, Ramsay H, Fawcett R, Kuleshov Y (2012) Tropical cyclone climatology of the south Pacific Ocean and its relationship to El Niño–Southern Oscillation. J Clim 25:6108–6122. doi: 10.1175/JCLI-D-11-00647.1 CrossRefGoogle Scholar
  9. Ebert E (2007) Methods for verifying satellite precipitation Estimates. In: Levizzani V, Bauer P, Turk FJ (eds) Measuring precipitation from space, vol 28. Advances In Global Change Research, Springer, ​Dordrecht, pp. 345–356. doi: 10.1007/978-1-4020-5835-6_27
  10. Ebert EE, Janowiak JE, Kidd C (2007) Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull Am Meteorol Soc 88:47–64. doi: 10.1175/BAMS-88-1-47 CrossRefGoogle Scholar
  11. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall, New YorkCrossRefGoogle Scholar
  12. Ensor LA, Robeson SM (2008) Statistical characteristics of daily precipitation: comparisons of gridded and point datasets. J Appl Meteor Climatol 47:2468–2476. doi: 10.1175/2008JAMC1757.1 CrossRefGoogle Scholar
  13. Habib E, Henschke A, Adler RF (2009) Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA. Atmos Res 94:373–388. doi: 10.1016/j.atmosres.2009.06.015 CrossRefGoogle Scholar
  14. Habib EH, Ea M, Aduvala AV (2008) Effect of local errors of tipping-bucket rain gauges on rainfall-runoff simulations. J Hydrol Eng 13:488–496. doi: 10.1061/(ASCE)1084-0699(2008)13:6(488) CrossRefGoogle Scholar
  15. Hou AY, Kakar RK, Neeck S, Aa A, Kummerow CD, Kojima M, Oki R, Nakamura K, Iguchi T (2014) The global precipitation measurement mission. Bull Am Meteorol Soc 95:701–722. doi: 10.1175/BAMS-D-13-00164.1 CrossRefGoogle Scholar
  16. Huffman GJ, Bolvin DT (2014) TRMM and other data precipitation data set documentation. Global Change Master Directory, NASA. Accessed 10 Januay 2015
  17. Huffman GJ, Bolvin DT, Braithwaite D, Hsu K, Joyce R, Xie P (2014) NASA Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG) Algorithm Theoretical Basis Document (ATBD) Version 4.4. Accessed 17 May 2015
  18. Huffman GJ, Bolvin DT, Nelkin EJ (2015) Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation. Accessed 17 May 2015
  19. 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 8:38–55. doi: 10.1175/JHM560.1 CrossRefGoogle Scholar
  20. Janowiak JE, Joyce RJ, Yarosh Y (2001) A real-time global half-hourly pixel-resolution infrared dataset and its applications. Bull Am Meteorol Soc 82:205–217. doi: 10.1175/1520-0477(2001)082<0205:ARTGHH>2.3.CO;2 CrossRefGoogle Scholar
  21. Jiang H, Zipser EJ (2010) Contribution of tropical cyclones to the global precipitation from eight seasons of TRMM data: regional, seasonal, and interannual variations. J Clim 23:1526–1543. doi: 10.1175/2009JCLI3303.1 CrossRefGoogle Scholar
  22. Joyce RJ, Janowiak JE, Pa A, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503. doi: 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2 CrossRefGoogle Scholar
  23. Jury M, Heron S, Spillman C, Anthony K, Dexter P, Sivakumar M (2010) Climate, Carbon and Coral Reefs. World Meteorological Organisation. Accessed 16 February 2015
  24. Knapp KR, Kruk MC, Levinson DH, Diamond HJ, Neumann CJ (2010) The International Best Track Archive for Climate Stewardship (IBTrACS). Bull Am Meteorol Soc 91:363–376. doi: 10.1175/2009BAMS2755.1 CrossRefGoogle Scholar
  25. Kummerow C, Hong Y, Olson WS, Yang S, Adler RF, McCollum J, Ferraro R, Petty G, Shin D-B, Wilheit TT (2001) The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. J Appl Meteorol 40:1801–1820. doi: 10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2 CrossRefGoogle Scholar
  26. Lau KM, Zhou YP, Wu HT (2008) Have tropical cyclones been feeding more extreme rainfall? J Geophys Res Atmos 113:1–12. doi: 10.1029/2008JD009963 CrossRefGoogle Scholar
  27. Li M, Shao Q (2010) An improved statistical approach to merge satellite rainfall estimates and raingauge data. J Hydrol 385:51–64. doi: 10.1016/j.jhydrol.2010.01.023 CrossRefGoogle Scholar
  28. Lonfat M, Marks FD, Chen SS (2004) Precipitation distribution in tropical cyclones using the Tropical Rainfall Measuring Mission (TRMM) microwave imager: a global perspective. Mon Wea Rev 132:1645–1660. doi: 10.1175/1520-0493(2004)132<1645:PDITCU>2.0.CO;2 CrossRefGoogle Scholar
  29. Mitra AK, Bohra AK, Rajeevan MN, Krishnamurti TN (2009) Daily Indian precipitation analysis formed from a merge of rain-gauge data with the TRMM TMPA satellite-derived rainfall estimates. J Meteor Soc Japan 87A:265–279. doi: 10.2151/jmsj.87A.265 CrossRefGoogle Scholar
  30. Nešpor V, Sevruk B (1999) Estimation of wind-induced error of rainfall gauge measurements using a numerical simulation. J Atmos Ocean Technol 16:450–464. doi: 10.1175/1520-0426(1999)016<0450:EOWIEO>2.0.CO;2 CrossRefGoogle Scholar
  31. Nogueira RC, Keim BD (2010) Annual volume and area variations in tropical cyclone rainfall over the eastern United States. J Clim 23:4363–4374. doi: 10.1175/2010JCLI3443.1 CrossRefGoogle Scholar
  32. Prakash S, Mahesh C, Gairola RM, Pal PK (2012) Comparison of high-resolution TRMM-based precipitation products during tropical cyclones in the North Indian Ocean. Nat Hazards 61:689–701. doi: 10.1007/s11069-011-0055-7 CrossRefGoogle Scholar
  33. Regional Specialised Meteorological Centre Fiji (2003)Tropical Cyclone summary 2002–2003 Season. Accessed 10 November 2014
  34. Renzullo LJ, Chappell A, Raupach T, Dyce P, Ming L, Sahao Q (2011) An assessment of statistically blended satellite-gauge precipitation data for daily rainfall analysis in Australia. Proc 34th International Symposium on Remote Sensing of Environment, Sydney, Australia, 10–15 April:4Google Scholar
  35. Roe GH (2005) Orographic precipitation. Annu Rev Earth Planet Sci 33(1):645–671. doi: 10.1146/ CrossRefGoogle Scholar
  36. Rudolf B (1993) Management and analysis of precipitation data on a routine basis. In: Proceedings of International Symposium on Precipitation and Evaporation, Slovak Hydrometeorology Institution, Bratislava, 1993.Google Scholar
  37. Scheel MLM, Rohrer M, Huggel C, Santos Villar D, Silvestre E, Huffman GJ (2011) Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) performance in the Central Andes region and its dependency on spatial and temporal resolution. Hydrol Earth Syst Sci 7:8545–8586. doi: 10.5194/hessd-7-8545-2010 CrossRefGoogle Scholar
  38. Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. 23rd ACM national conference:517–524. doi:10.1145/800186.810616Google Scholar
  39. Simpson RH (1974) The hurricane disaster—potential scale. Weatherwise 27:169–186. doi: 10.1080/00431672.1974.9931702 CrossRefGoogle Scholar
  40. Sinclair MR (1994) A diagnostic model for estimating orographic precipitation. J Appl Meteorol 33(10):1163–1175. doi: 10.1175/1520-0450(1994)033<1163:ADMFEO>2.0.CO;2 CrossRefGoogle Scholar
  41. Smith RB, Schafer P, Kirshbaum DJ, Regina E (2009) Orographic precipitation in the tropics: experiments in Dominica. J Atmos Sci 66(6):1698–1716. doi: 10.1175/2008JAS2920.1 CrossRefGoogle Scholar
  42. Sorooshian S, Hsu KL, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull Am Meteorol Soc 81:2035–2046. doi: 10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2 CrossRefGoogle Scholar
  43. Terry JP, Ra K, Wotling G (2008) Features of tropical cyclone-induced flood peaks on Grande Terre, New Caledonia. Water Environ J 22:177–183. doi: 10.1111/j.1747-6593.2007.00098.x CrossRefGoogle Scholar
  44. Turk FJ, Mehta A (2007) Toward improvements in short-time scale satellite-derived precipitation estimates using blended satellite techniques. In: Levizzani V, Bauer P, Turk FJ (eds) Measuring precipitation from space, vol 28. Advances In Global Change Research, Springer, Dordrecht, pp. 281–290. doi: 10.1007/978-1-4020-5835-6_22
  45. Turk FJ, Miller SD (2005) Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques. Geosci Remote Sens IEEE Trans 43(5):1059–1069. doi: 10.1109/TGRS.2004.841627 CrossRefGoogle Scholar
  46. United Nations Office for the Coordination of Humanitarian Affairs (2003) Cyclone raises dengue threat in New Caledonia Accessed 15 November 2014
  47. Vila DA, de Goncalves LGG, Toll DL, Rozante JR (2009) Statistical evaluation of combined daily gauge observations and rainfall satellite estimates over continental South America. J Hydrometeorol 10:533–543. doi: 10.1175/2008JHM1048.1
  48. Wang J, Fisher BL, Wolff DB (2008) Estimating rain rates from tipping-bucket rain gauge measurements. J Atmos Ocean Technol 25:43–56. doi: 10.1175/2007JTECHA895.1 CrossRefGoogle Scholar
  49. Weng F, Zhao L, Ferraro RR, Poe G, Li X, Grody NC (2003) Advanced microwave sounding unit cloud and precipitation algorithms. Radio Science 38:n/a-n/a. doi:10.1029/2002RS002679Google Scholar
  50. Wilheit TT (1986) Some comments on passive microwave measurement of rain. Bull Am Meteorol Soc 67:1226–1232. doi: 10.1175/1520-0477(1986)067<1226:SCOPMM>2.0.CO;2 CrossRefGoogle Scholar
  51. Wilheit TT (2003) The TRMM Measurement Concept. Meteorol Monogr 29:197. doi: 10.1175/0065-9401(2003)029<0197:CTTMC>2.0.CO;2 CrossRefGoogle Scholar
  52. Wilks DS (2011) Statistical methods in the atmospheric sciences, vol 100. International Geophysics Series, 3 edn., Academic PressGoogle Scholar
  53. Yu Z, Yu H, Chen P, Qian C, Yue C (2009) Verification of tropical cyclone-related satellite precipitation estimates in mainland China. J Appl Meteor Climatol 48:2227–2241. doi: 10.1175/2009JAMC2143.1 CrossRefGoogle Scholar
  54. Zhao L, Weng F (2002) Retrieval of ice cloud parameters using the advanced microwave sounding unit. J Appl Meteorol 41:384–395. doi: 10.1175/1520-0450(2002)041<0384:ROICPU>2.0.CO;2 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Anil Deo
    • 1
  • Kevin J. E. Walsh
    • 1
  • Alexandre Peltier
    • 2
  1. 1.School of Earth SciencesThe University of MelbourneMelbourneAustralia
  2. 2.Meteo-FranceNoumea CedexFrance

Personalised recommendations