Surveys in Geophysics

, Volume 25, Issue 5–6, pp 511–537 | Cite as

Overview of Overland Satellite Rainfall Estimation for Hydro-Meteorological Applications

  • Emmanouil N. AnagnostouEmail author


This paper reviews current techniques on rainfall estimation from satellite sensor observations. The sensors considered in this study are the Precipitation Radar (PR) and radiometer (TMI) onboard TRMM (Tropical Rainfall Measuring Missio) satellite, the Special Sensor Microwave/Imager (SSM/I) onboard Defense Meteorological Satellite Program (DMSP) platforms, and infrared (IR) sensors onboard geostationary satellites. We present the physical basis and mathematical formulation of a newly developed combined radar-radiometer (PR/TMI) retrieval for TRMM and its application for overland rain estimation. Subsequently we discuss the current state-of-the-art in overland passive microwave (TMI and SSM/I) rain estimation techniques, and outstanding issues associated with the inverse problem. The significance of lightning information in advancing high-frequency rainfall estimation from passive microwave-calibrated IR retrieval techniques is discussed on the basis of newly developed techniques. Finally, current approaches are presented on merging the infrequent passive microwave-based rainfall estimates with the high-frequency, but lower accuracy, rainfall fields derived from proxy parameters (e.g., lightning and IR). The paper provides useful insights on satellite rainfall estimation and discusses issues and applications.


lightning precipitation prediction rainfall retrieval satellite observations soil moisture 


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  1. Adler, R.F., Negri, A.J. 1988‘A satellite infrared technique to estimate tropical convective and stratiform rainfall’J. Appl. Met.273051CrossRefGoogle Scholar
  2. Adler, R.F., Negri, A.J., Keehn, P.R., Hakkarinen, I.M. 1993Estimation of monthly rainfall over Japan and surrounding waters from a combination of low-orbit microwave and geosynchronous IR dataJ. Appl. Met.32335356CrossRefGoogle Scholar
  3. Adler, R.F., Huffman, J., Keehn, P.R. 1994‘Global tropical rain estimates from microwave adjusted geosynchronous IR data’Remote Sens. Rev.11125152Google Scholar
  4. Alexander, G.D., Weinman, J.A., Karyampudi, V.M., Olson, W.S., Lee, A.C.L 1999‘The effect of assimilaing rain rates derived from satellites and lightning on forecasts of the 1993 superstrom’Mon. Wea. Rev.12714331457CrossRefGoogle Scholar
  5. Anagnostou E.N., (2004), ‘Assessment of satellite rain retrieval error propagation in the prediction of land surface hydrologic variables, Kluwer Academic Publishers (in review).Google Scholar
  6. Anagnostou E.N., Morales C., (2002), ‘Rainfall estimation from TOGA radar observations during LBA field campaign’. J. Geoephys. Res. 107(D20), DOI: 10.1029/2001JD000377.Google Scholar
  7. Anagnostou, E.N., Negri, A.J., Adler, R.F. 1999‘A satellite infrared technique for diurnal rainfall variability studies’J. Geophys. Res.1043147731488CrossRefGoogle Scholar
  8. Anderson, E., Pailleux, J., Thepaut, J.-N., Eyre, J.R., McNally, A.P., Kelly, G.A., Courtier, P. 1994‘Use of cloud cleared radiances in three/four dimensional variational assimilation’Quart. J. Roy. Met. Soc.120627653CrossRefGoogle Scholar
  9. Arkin, P.A., Meisner, B.N. 1987‘The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982–1984’Mon. Wea. Rev.1155174CrossRefGoogle Scholar
  10. Ba, M.B., Gruber, A. 2001‘GOES Multispectral Rainfall Algorithm (GMSRA)’J. Appl. Met.4015001514CrossRefGoogle Scholar
  11. Barnstone, A.G. 1992Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke scoreWea. Forecasting7699709CrossRefGoogle Scholar
  12. Bindlish, R., Jackson, T.J., Wood, E., Gao, H., Starks, P., Bosch, D., Lakshmi, V. 2003‘Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States’Remote sens. Environ.85507515CrossRefGoogle Scholar
  13. Boccippio D.J., Petersen W.A., and Cecil, D. J.: in press, ‘The tropical convection spectrum: 1. Archetypical vertical structures’. J. Clim.Google Scholar
  14. Buishand, T.A., Shabalova, M.V., Brandsma, T. 2004‘On the choice of the temporal aggregation level for statistical downscaling of precipitation’J. Clim.17I8161827CrossRefGoogle Scholar
  15. Chang, D.-E., Weinman, J.A., Morales, C.A., Olson, W.S. 2001‘The effect of spaceborne microwave and ground-based continuous lightning measurements on forecasts of the 1998 Groundhog Day storm’Mon. Wea. Rev.12918091833CrossRefGoogle Scholar
  16. Chauhan, N.S. 1997Soil moisture estimation under a vegetation cover: combined active passive microwave remote sensing approach’Int. J. Remote Sens.1810791097CrossRefGoogle Scholar
  17. Chen, M., Zeng, X., Dickinson, R.E. 1998‘Adjustment of GCM precipitation intensity over the United States’J. Appl. Met.37876887CrossRefGoogle Scholar
  18. Chronis, T.G., Anagnostou, E.N., Dinku, T. 2004‘High-frequency estimation of thunderstorms via satellite infrared and a long-range lightning network in Europe’Quart J. Roy. Met. Soc.13015551575CrossRefGoogle Scholar
  19. Clark, M.P., Hay, L.E. 2004Use of medium-range numerical weather predicion model output to produce forecasts of streamflow, JHydromet.51532CrossRefGoogle Scholar
  20. Conner, M.D., Petty, G.R. 1998‘Validation and intercomparison of SSM/I rain-rates retrieval methods over the continental United States’J. Appl. Met.37679700CrossRefGoogle Scholar
  21. Dinku T., Anagnostou, E. N: in press, ‘Regional differences in overland rainfall estimation from PR-calibrated TMI algorithm’. J. Appl. Met.Google Scholar
  22. Ebert, E.E., Manton, M.J. 1998‘Performance of satellite rainfall estimation algorithms during TOGA COARE’J. Atmos. Sci.5515371557CrossRefGoogle Scholar
  23. Ebert, E.E., Manton, M.J., Arkin, P.A., Allam, R.J., Holpin, C.E., Gruber, A. 1996‘Results from the GPCP algorithm intercomparison programme’Bull. Am. Met. Soc.7728752888CrossRefGoogle Scholar
  24. Evans, K.F., Turk, J., Wong, T., Stephens, G.L. 1995‘A Bayesian approach to microwave precipitation profile retrieval’J. Appl. Met.34260278Google Scholar
  25. Famiglietti, J.S., Wood, E.F. 1991‘Evapotranspiration and runoff from large land areas: Land surface hydrology for atmospheric general circulation modelsWood, E.F. eds. Land Surface Atmospheric Interactions for Climate Modeling: Observations, Models, and AnalysisKluwer Academic PublishersNorwell Massachusetts179204Google Scholar
  26. Ferraro, R.R. 1997‘Special sensor microwave imager derived global rainfall estimates for climatological applications’J. Geophys. Res.1021671516735CrossRefGoogle Scholar
  27. Ferraro, R.R., Marks, G.F. 1995The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurementsJ. Atmos. Oceanic Technol.12755770CrossRefGoogle Scholar
  28. Ganguly, A.R., Bras, R. 2003‘Distributed quantitative precipitation forecasting using information from radar and numerical weather Prediction Models’J. Hydromet.411681180CrossRefGoogle Scholar
  29. Goodman, S.J., Buechler, D.E., Meyer, P.J. 1988‘Convective tendency images derived from a combination of lightning and satellite data’Wea. Forecasting3173188CrossRefGoogle Scholar
  30. Grandt, C. 1992‘Thunderstorm monitoring in South Africa and Europe by means of Very Low Frequency sferics’J. Geophys. Res.971821518226Google Scholar
  31. Grecu, M., Anagnostou, E.N. 2001‘Overland precipitation estimation from passive microwave observations’J. Appl. Met.4013671380CrossRefGoogle Scholar
  32. Grecu, M., Anagnostou, E.N., Adler, R.F. 2000‘Assessment of the use of lightning information in satellite infrared rainfall estimation’J. Hydromet.1211221CrossRefGoogle Scholar
  33. Grecu, M., Olson, W.S., Anagnostou, E.N. 2004‘Retrieval of precipitation profiles from multiresolution, multifrequency, active and passive microwave observations’J. Appl. Met.43562575CrossRefGoogle Scholar
  34. Guetter, A.K., Georgakakos, K.P., Tsonis, A.A. 1999Hydrologic applications of satellite data: 2Flow simulation and soil water estimates, J. Geophys. Res.1012652726538Google Scholar
  35. Haddad, Z.S., Im, E., Durden, S.L., Hensley, S. 1996‘Stochastic filtering of rain profiles using radar, surface-referenced radar, or combined radar–radiometer measurements’J. Appl. Met.35229242CrossRefGoogle Scholar
  36. Hou, A.Y., Ledvina, D.V., da Silva, A.M., Zhang, S.Q., Joiner, J., Atlas, R.M. 2000Assimilation of SSM/I-derived surface rainfall and total precipitable water for improving the GEOS analysis for climate studies, MonWea. Rev.128509537CrossRefGoogle Scholar
  37. Hsu, K., Gupta, H., Gao, X., Sorooshian, S. 1999‘Estimation of physical variables from multichannel remotely sensed imagery using a neural network: application to rainfall estimation’Water Resour. Res.3516051618CrossRefGoogle Scholar
  38. Huffman G.J., Adler R.F., Stocker E.F., Bolvin D.T., and Nelkin E.J., (2003), ‘Analysis of TRMM 3-hourly-multi-satellite percipitation estimates computed in both real and post-real time’. 12th AMS Conf. on Sat. Meteor. and Ocean., CD-ROM, 13–17 February, Long Beach, CA.Google Scholar
  39. Iguchi, T., Kozu, T., Meneghini, R., Awaka, J., Okamoto, K. 2000‘Rain-profiling algorithm for TRMM precipitation radar’J. Appl. Met.3920382052CrossRefGoogle Scholar
  40. Jackson, T.J., Schmugge, T.J. 1991‘Vegetation effects on the microwave emission of soils’Remote Sens. Environ.36203212CrossRefGoogle Scholar
  41. Joyce, R.J., Janowiak, J.E., Arkin, P.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. Hydromet.5487503CrossRefGoogle Scholar
  42. Kidd, C., Kniveton, D.R., Todd, M.C., Bellerby, T.J. 2003‘Satellite rainfall estimation using combined passive microwave and infrared algorithms’J. Hydromet.410881104CrossRefGoogle Scholar
  43. Kidder S.Q., Vonder Haar T.H., (1995), Satellite Meteorology: An Introduction, Academic Press.Google Scholar
  44. Koster, R, Suarez M., (1996), ‘Energy and water balance calculations in the MOSAIC LSM’. NASA Tech. Memo. l04606, Vol. 9.Google Scholar
  45. Kuligowski, J.R. 2002‘A self calibrating real time GOES rainfall algorithm for short-term rainfall estimates’J. Hydromet.3112130CrossRefGoogle Scholar
  46. Kummerow, C., Olson, W.S., Giglio, L. 1996‘A simplified scheme for obtaining precipitation and vertical hydrometeor profile from passive microwave’IEEE Trans. Geosci. Remote Sens.3412131232CrossRefGoogle Scholar
  47. Kummerow, C.,  et al. 2000‘The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit’J. Appl. Met.3919651982CrossRefGoogle Scholar
  48. Kummerow, C., Hong, Y., Olson, W.S., Yang, S., Adler, R.F., McCollum, J., Ferraro, R., Petty, G., Shin, D.B., Wilheit, T.T. 2001‘The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors’J. Appl. Met.4018011819CrossRefGoogle Scholar
  49. Le Dimet, F.-X., Talagrand, O. 1986‘Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects’Tellus3897110Google Scholar
  50. Lee, K.H., Anagnostou, E.N. 2004‘A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations’Remote Sen. Environ92112125CrossRefGoogle Scholar
  51. Lee, K.H., Burke, J.E., Shuttleworth, W.J., Harlow, R.C. 2002‘Influence of vegetation on SMOS mission retrievals’Hydrol. Earth Syst. Sci.6153166Google Scholar
  52. Leese, J., Jackson, T., Pitman, A., Dirmeyer, P. 2001‘GEWEX/BAHC International Workshop on soil moisture monitoring, analysis, and prediction for hydrometeorological and hydroclimatological applications’Bull. Am. Met. Soc.8214231430CrossRefGoogle Scholar
  53. Levizzani, V., Schmetz, J., Lutz, H.J., Kerkmann, J., Alberoni, P.P., Cervino, M. 2001‘Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation’Meteor. Appl.82341CrossRefGoogle Scholar
  54. Liang, X., Wood, E., Lettenmaier, D. 1996‘Surface and soil moisture parameterization of the VIC-2L model: Evaluation and modifications’Global Planet. Change13195206CrossRefGoogle Scholar
  55. Liao, L., Meneghini, R., Iguchi, T. 2001‘Comparisons of rain rate and reflectivity factor derived from the TRMM precipitation radar and the WSR-88D over the Melbourne, Florida site’J. Atmos. Oceanic Technol.1819591974CrossRefGoogle Scholar
  56. Luo L., et al., (2003), ‘Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains’. J. Geophys. Res. 108(D22): 8843, DOI: 10.1029/2002JD003246.Google Scholar
  57. Magono, C., Tazawa, S. 1966‘Design of “snow crystal sondes”’J. Atmos. Sci.23618625CrossRefGoogle Scholar
  58. Marzano, F.S., Palmacci, M., Cimini, D., Giuliani, G., Turk, F.J. 2004‘Multivariate statistical integration of satellite infrared and microwave radiometric measurements for rainfall retrieval at the geostationary scale’IEEE Trans. Geosci. Remote Sens.4210181032CrossRefGoogle Scholar
  59. McCollum J.R., Ferraro R.R., (2003), ‘Next generation of NOAA/NESDIS TMI, SSM/I, and AMSR-E microwave land rainfall algorithms’. J. Geophys. Res. 108: DOI: 10.1029/2001JD001512.Google Scholar
  60. Meneghini, R. 1978‘Rain-rate estimates for an attenuating radar’Radio Sci.13459470Google Scholar
  61. Meneghini, R., Iguchi, T., Kozu, T., Liao, L., Okamoto, K., Jones, J.A., Kwiatkowski, J. 2000‘Use of surface reference technique for path attenuation estimates from the TRMM precipitation radar’J. Appl. Met.3920532070CrossRefGoogle Scholar
  62. Mie, G. 1908‘Contribution to the optics of suspended media, specifically colloidal metal suspensions’Ann. Phys.25377445Google Scholar
  63. Mitchell, K.,  et al. 1999‘GCIP Land Data Assimilation System (LDAS) project now underway’GEWEX News936Google Scholar
  64. Mitchell K. et al., (2000), ‘Recent GCIP-sponsored advancements in coupled land-surface modeling and data assimilation in the NCEP Eta mesoscale model’. Preprints, 15th AMS Conf. on Hydrology, Long Beach, CA, Paper Pl.22.Google Scholar
  65. Morales, C., Anagnostou, E.N. 2003‘Extending the capabilities of high-frequency rainfall estimation from geostationary-based satellite infrared via a network of long-range lightning observations’J. Hydromet.4141159CrossRefGoogle Scholar
  66. Negri A.J., Xu L., Adler R.F., (2002), ‘A TRMM-calibrated infrared rainfall algorithm applied over Brazil’. J. Geophys. Res. 107(D20), 8048, DOI:10.1029/2000JD000265.Google Scholar
  67. Negri, A.J., Adler, R.F. 1993‘An intercomparison of three satellite infrared rainfall techniques over Japan and surrounding waters’J. Appl. Met.32357373CrossRefGoogle Scholar
  68. Olson, W.S. 1989‘Physical retrieval of rainfall rates over the ocean by multispectral microwave radiometry: Application to tropical cyclones,’ JGeophys. Res.9422672280Google Scholar
  69. Olson, W.S., Hong, Y., Kummerow, C.D., Turk, J. 2001‘A texture-polarization method for convective-stratiform precipitation area coverage from passive microwave radiometer data’J. Appl. Met.4015771591CrossRefGoogle Scholar
  70. Pandey, G.R., Cayan, D.R., Dettinger, M.D., Georgakakos, K.P. 2000‘A hybrid orographic plus statistical model for downscaling daily precipitation in Northern California’J. Hydromet.1491506CrossRefGoogle Scholar
  71. Petersen, W.A., Rutledge, S.A. 2001‘Regional variability in tropical convection: observations from TRMM’J. Clim.1435663586CrossRefGoogle Scholar
  72. Petty, G.W. 1995‘The status of satellite-based rainfall estimation over land’Remote Sens. Environ.51125137CrossRefGoogle Scholar
  73. Pierdicca, N., Marzano, F.S., d’ Auria, G., Basili, P., Ciotti, P., Mugnai, A. 1996‘Precipitation retrieval from spaceborne microwave radiometers based on maximum a posteriori probability estimation’IEEE Trans. Geosci. Remote Sens.34831846CrossRefGoogle Scholar
  74. Prabhakara, C., Iacovazzi, R.,Jr., Wienman, J.A., Dalu, G. 2000‘A TRMM microwave radiometer rain rate estimation method with convective stratiform discrimination’J. Meteorol. Soc. Jpn.78241258Google Scholar
  75. Robock A., et al., (2003), ‘Evaluation of the North American Land Data Assimilation System over the southern Great Plains during the warm season’. J. Geophys. Res. 108(D22): 8846, DOI: 10.1029/2002JD003245.Google Scholar
  76. Schaake J.C., et al., (2004), ‘An intercomparison of soil moisture fields in the North American Land Data Assimilation System (NLDAS)’. J. Geophys. Res. 109(D01S90), DOI: 10.1029/2002JD003309.Google Scholar
  77. Smith, E.A., Xiang, X., Mugnai, A., Tripoli, G.J. 1994‘Design of an inversion-based precipitation profile retrieval algorithm using an explicit cloud model for initial guess microphysics’Meteor. Atmos. Phys.545378CrossRefGoogle Scholar
  78. Smith, E.A., Turk, F.J., Farrar, M.R., Mugnai, A., Xiang, X.W. 1997‘Estimating 13.8-GHz path-integrated attenuation from 10.7-GHz brightness temperatures for the TRMM combined PR-TMI precipitation algorithm’J. Appl. Met.36365388CrossRefGoogle Scholar
  79. Spencer, R.W., Olson, W.S., Rongzhang, W., Martin, D.W., Weinman, J.A., Santek, D.A. 1983‘Heavy thunderstorms observed over land by the Nimbus 7 Scanning Multichannel Microwave Radiometer’J. Clim. Appl. Meteor.2210411046CrossRefGoogle Scholar
  80. Spencer, R.W., Goodman, H.M., Hood, R.E. 1989Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal’J. Atmos. Oceanic Technol.6254273CrossRefGoogle Scholar
  81. Tapiador, F.J., Kidd, C., Levizzani, V., Marzano, F.S. 2004A neural networks-based fusion technique to estimate half-hourly rainfall estimates at 0.1° resolution from satellite passive microwave and infrared dataJ. Appl. Met.43576594CrossRefGoogle Scholar
  82. Testud, J., Oury, S., Black, R.A., Amayenc, P., Dou, X. 2001‘The concept of “normalized” distribution to describe raindrop spectra: A tool for cloud physics and cloud remote sensing’J. Appl. Met.4011181140CrossRefGoogle Scholar
  83. Todd, M.C., Kidd, C., Kniveton, D., Bellerby, T.J. 2000‘A combined satellite infrared and passive microwave technique for estimation of small scale rainfall’J. Atmos. Oceanic Technol.18742754CrossRefGoogle Scholar
  84. Turk F.J., Ebert E.E., Oh J., Sohn B.J., Levizzani V., Smith E. A., Ferraro R., (2003), Validation of a global operational blended-satellite precipitation analysis at short time scales’. 12th AMS Conf. on Sat. Meteor. and Ocean., CD-ROM, 13–17 February, Long Beach, CA.Google Scholar
  85. Vicente G., A., Scofield, R.A., Menzel, W.P. 1998‘The operational GOES infrared rainfall estimation technique’Bull. Am. Meteor. Soc.7918831898CrossRefGoogle Scholar
  86. Wang, G.L., Eltahir, E.A.B. 2000‘Impact of rainfall sub-grid variability on modeling the biosphere–atmosphere system, JClim.1328872899CrossRefGoogle Scholar
  87. Weinman, J.A., Guetter, P.J. 1977‘Determination of rainfall distribution from microwave radiation: measured by the Nimbus 6 ESMR’J. Appl. Met.29561585Google Scholar
  88. Weinman, J.A., Meneghini, R., Nakamura, K. 1990‘Retrieval of precipitation profiles from airborne radar and passive radiometer measurements: Comparison with dual-frequency radar measurements’J. Appl. Met.29981993CrossRefGoogle Scholar
  89. Weng, F., Liu, Q. 2003‘Satellite data assimilation in numerical weather prediction models. Part I: Forward radiative transfer and Jacobian modeling in cloudy atmospheres’J. Atmos. Sci.6026332646CrossRefGoogle Scholar
  90. Widmann M., Bretherton C.S., Salathé, E.P., (2003), ‘Statistical precipitation downscaling over the Northwestern United States using numerically simulated precipitation as a predictor’. J. Clim. 799–816.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringUniversity of ConnecticutStorrsU.S.A

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