Skip to main content

Global Precipitation Monitoring

  • Chapter
  • First Online:
Satellite-based Applications on Climate Change
  • 1931 Accesses

Abstract

Satellite observations play a vital role in the global monitoring of precipitation because they fill in large data voids where conventional measurements such as surface rain gauges and weather radars are primarily restricted to populated land regions. Geostationary satellites, containing visible and infrared sensors, provide the most continuous observations from space; they can infer surface precipitation through relationships between cloud properties and precipitation rate. Passive microwave sensors, which operate primarily on low Earth-orbiting satellites, provide a more direct measurement of rainfall and global coverage; however, they observe the Earth less frequently than the geostationary satellites. This chapter summarizes the strengths and weaknesses of the various satellite retrieval algorithms, then describes emerging blended precipitation products that merge different satellite measurements to achieve the best possible rainfall product. Examples of the utility of such data are also provided.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adler RF, Negri AJ (1988) A satellite infrared technique to estimate tropical convective and stratiform rainfall. J Appl Meteorol 27:30–51

    Article  Google Scholar 

  • Adler RF, Kidd C, Petty G, Morrissey M, Goodman HM (2001) Intercomparison of global precipitation products: the third precipitation intercomparison project (PIP-3). Bull Am Meteorol Soc 82:1377–1396

    Article  Google Scholar 

  • Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin E (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J Hydrometeorol 4:1147–1167

    Article  Google Scholar 

  • Aonashi K, Awaka J, Hirose M, Kozu T, Kubota T, Liu G, Shige S, Kida S, Seto S, Takahashi N, Takayabu Y (2009) GSMaP passive microwave precipitation retrieval algorithm: algorithm description and validation. J Meteorol Soc Jpn 87:119–136

    Article  Google Scholar 

  • Arkin PA, Meisner BN (1987) The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982-84. Mon Weather Rev 115:51–74

    Article  Google Scholar 

  • Arkin PA, Xie PP (1994) The global precipitation climatology project: first algorithm intercomparison project. Bull Am Meteorol Soc 75:401–419

    Article  Google Scholar 

  • Ba MB, Gruber A (2001) GOES multispectral rainfall algorithm (GMSRA). J Appl Meteorol 40:1500–1514

    Article  Google Scholar 

  • Barrett EC, Martin DW (1981) The use of satellite data in rainfall monitoring. Academic, London

    Google Scholar 

  • Barrett EC, Dodge J, Goodman HM, Janowiak J, Kidd C, Smith EA (1994) The first WetNet precipitation intercomparison project. Remote Sens Rev 11:49–60

    Article  Google Scholar 

  • Dodge J, Goodman HM (1994) The WetNet project. Remote Sens Rev 11:5–21

    Article  Google Scholar 

  • Ebert EE, Manton MJ, Arkin PA, Allam RJ, Holpin CE, Gruber A (1996) Results from the GPCP algorithm intercomparison programme. Bull Am Meteorol Soc 77:2875–2887

    Google Scholar 

  • Ebert EE, Manton MJ (1998) Performance of satellite rainfall estimation algorithms during TOGA COARE. J Atmos Sci 55:1537–1557

    Article  Google Scholar 

  • Ferraro RR, Marks GF (1995) The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements. J Atmos Ocean Technol 12:755–770

    Article  Google Scholar 

  • Gopolan K, Wang NY, Liu C, Ferraro R (2010) Version 7 of the TRMM 2A12 land precipitation algorithm. J Atmos Ocean Technol 27:1343–1354

    Article  Google Scholar 

  • Griffith CG, Woodley WL, Grube P, Martin DW, Stout J, Sikdar DN (1978) Rain estimation from geosynchronous satellite imagery – visible and infrared studies. Mon Weather Rev 106:1153–1171

    Google Scholar 

  • Grody NC (1991) Classification of snow cover and precipitation using the special sensor microwave/imager (SSM/I). J Geophys Res 96:7423–7435

    Article  Google Scholar 

  • Hirose M, Oki R, Short D, Nakamura K (2009) Regional characteristics of scale-based precipitation systems from ten years of TRMM PR data. J Meteorol Soc Jpn 87:353–368

    Article  Google Scholar 

  • Hsu K, Gao X, Sorooshian S, Gupta HV (1997) Precipitation estimation from remotely sensed information using artificial neural networks. J Appl Meteorol 36:1176–1190

    Article  Google Scholar 

  • Huffman GJ, Adler RF, Arkin PA, 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 Am Meteorol Soc 78:5–20

    Article  Google Scholar 

  • 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 2:36–50

    Article  Google Scholar 

  • Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Hong Y, Stocker E, Wolff D (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55

    Article  Google Scholar 

  • Iguchi T, Kozu T, Meneghini R, Awaka J, Okamoto K (2000) Rain-profiling algorithm for the TRMM precipitation radar. J Appl Meteorol 39:2038–2052

    Article  Google Scholar 

  • Iguchi T, Oki R, Smith EA, Furuhama Y (2002) Global precipitation measurement program and the development of dual-frequency precipitation radar. J Comm Res Lab 49:37–45

    Google Scholar 

  • Joyce RJ, Janowiak JE, Arkin PA, 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

    Article  Google Scholar 

  • Kidder SQ, Vonder Haar TH (1995) Satellite meteorology: an introduction. Academic, New York

    Google Scholar 

  • Kniveton DR, Motta BC, Goodman HM, Smith M, LaFontaine FJ (1994) The first WetNet precipitation intercomparison project: generation of results. Remote Sens Rev 11:243–302

    Article  Google Scholar 

  • Kummerow C, Olson WS, Giglio L (1996) A simplified scheme for obtaining precipitation and vertical hydrometeor profile from passive microwave. IEEE Trans Geosci Remote Sens 34:1213–1232

    Article  Google Scholar 

  • Kummerow C, Barnes W, Kozu T, Shiue J, Simpson J (1998) The tropical rainfall measuring mission (TRMM) sensor package. J Atmos Ocean Technol 15:809–817

    Article  Google Scholar 

  • Kummerow C, Hong Y, Olson WS, Yang S, Adler RF, McCollum J, Ferraro R, Petty G, Shin D, Wilheit TT (2001) The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J Appl Meteorol 40:1801–1819

    Article  Google Scholar 

  • L’Ecuyer TS, Stephens GL (2002) An estimation-based precipitation retrieval algorithm for attenuating radars. J Appl Meteorol 41:272–285

    Article  Google Scholar 

  • Liu G (2008) Deriving snow cloud characteristics from CloudSat observations. J Geophys Res 113. doi:10.1029/2007JD0009766

  • Lovejoy S, Austin GL (1979) The delineation of rain areas from visible and IR satellite data from GATE and mid-latitudes. Atmos-Ocean 17:77–92

    Article  Google Scholar 

  • Matrosov S (2007) Potential for attenuation-based estimates of rainfall rate from CloudSat. Geophys Res Lett 34:L05817. doi:10.1029/2006GL029161

    Article  Google Scholar 

  • McCollum JR, Ferraro RR (2003) The next generation of NOAA/NESDIS SSM/I, TMI and AMSR-E microwave land rainfall algorithms. J Geophys Res 108:8382–8404

    Article  Google Scholar 

  • Olson WS (1989) Physical retrieval of rainfall rates over the ocean by multispectral microwave radiometry: application to tropical cyclones. J Geophys Res 94:2267–2280

    Article  Google Scholar 

  • Sapiano MRP, Arkin PA (2009) An inter-comparison and validation of high resolution satellite precipitation estimates with three-hourly gauge data. J Hydrometeorol 10:149–166

    Article  Google Scholar 

  • Scofield RA (1987) The NESDIS operational convective precipitation technique. Mon Weather Rev 115:1773–1792

    Article  Google Scholar 

  • Scofield RA, Kuligowski RJ (2003) Status and outlook of operational satellite precipitation algorithms for extreme-precipitation events. Weather Forecast 18:1037–1051

    Article  Google Scholar 

  • Smith EA, Xiang X, Mugnai A, Tripoli GJ (1994) Design of an inversion-based precipitation profile retrieval algorithm using an explicit cloud model for initial guess microphysics. Meteorol Atmos Phys 54:53–78

    Article  Google Scholar 

  • Smith EA et al (1998) Results of WetNet PIP-2 project. J Atmos Sci 55:148–1536

    Google Scholar 

  • Smith EA et al (2007) International global precipitation measurement (GPM) program and mission: an overview. In: Levizzani V, Bauer P, Turk J (eds) Measuring precipitation from space, EURAINSAT and the future, vol 28, Advances in global change research. Springer, Dordrecht, pp 611–653

    Chapter  Google Scholar 

  • Sorooshian S, Hsu K, 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

    Article  Google Scholar 

  • Spencer RW, Goodman HM, Hood RE (1989) Precipitation retrieval over land and ocean with the SSM/I: identification and characteristics of the scattering signal. J Atmos Ocean Technol 6:254–273

    Article  Google Scholar 

  • Stephens G et al (2002) The CloudSat mission and the ATrain. Bull Am Meteorol Soc 83:1771–1790

    Article  Google Scholar 

  • Turk FJ, Miller SD (2005) Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques. IEEE Trans Geosci Remote Sens 43:1059–1069

    Article  Google Scholar 

  • Vila D, Ferraro RR, Joyce R (2007) Evaluation and improvement of AMSU precipitation retrievals. J Geophys Res 112:D20119. doi:10.1029/2007JD008617

    Article  Google Scholar 

  • Wang N, Liu C, Ferraro R, Wolff D, Zipser E, Kummerow C (2009) The TRMM 2A12 land precipitation product – status and future plans. J Meteorol Soc Jpn 87:237–253

    Article  Google Scholar 

  • Weng F, Zhao L, Poe G, Ferraro R, Li X, Grody N (2003) AMSU cloud and precipitation algorithms. Radio Sci 338:8068–8079

    Google Scholar 

  • Wilheit TT, Chang A, Rao M, Rodgers E, Theon J (1977) A satellite technique for quantitatively mapping rainfall rates over the oceans. J Appl Meteorol 16:551–560

    Article  Google Scholar 

  • Wilheit TT, Kummerow C, Ferraro R (2003) Rainfall algorithms for AMSR-E. IEEE Trans Geosci Remote Sens 41:204–214

    Article  Google Scholar 

  • Woodley WL, Sancho R (1971) A first step towards rainfall estimation from satellite cloud photographs. Weather 26:279–289

    Article  Google Scholar 

  • Xie P, Arkin PA (1997) Global pentad precipitation analysis based on gauge observations, satellite estimates and model outputs. In: Extended abstracts, American Geophysical Union 1997 fall meeting, AGU, San Francisco, 1997

    Google Scholar 

  • Xie P, Janowiak JE, Arkin PA, Adler R, Gruber A, Ferraro R, Huffman G, Curtis S (2003) GPCP pentad precipitation analyses: an experimental dataset based on gauge observations and satellite estimates. J Climate 16:2197–2214

    Google Scholar 

  • Zhao L, Weng F (2002) Retrieval of ice cloud parameters using the AMSU. J Appl Meteorol 41:384–395

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank our colleagues, H. Meng, M. Sapiano, D. Vila, and N. Wang for their contributions to this chapter. Additionally, we would like to recognize the Naval Research Laboratory in Monterey, CA, and the Climate Prediction Center in Camp Springs, M.D., for use of their imagery obtained from their web sites.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ralph Ferraro .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Ferraro, R., Smith, T. (2013). Global Precipitation Monitoring. In: Qu, J., Powell, A., Sivakumar, M. (eds) Satellite-based Applications on Climate Change. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5872-8_6

Download citation

Publish with us

Policies and ethics