• Bell, T. L., and N. Reid, 1993: Detecting the diurnal cycle of rainfall using satellite observations. J. Appl. Meteor., 32, 311322.

  • Bell, T. L., and P. K. Kundu, 2000: Dependence of satellite sampling error on monthly averaged rain rates: Comparison of simple models and recent studies. J. Climate, 13, 449462.

    • Search Google Scholar
    • Export Citation
  • Bell, T. L., P. K. Kundu, and C. D. Kummerow, 2001: Sampling errors of SSM/I and TRMM rainfall averages: Comparison with error estimates from surface data. J. Appl. Meteor., 40, 938954.

    • Search Google Scholar
    • Export Citation
  • Fisher, B. L., 2004: Climatological validation of TRMM TMI and PR monthly rain products over Oklahoma. J. Appl. Meteor., 43, 519535.

  • Fisher, B. L., 2007: Statistical error decomposition of regional-scale climatological precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM). J. Appl. Meteor. Climatol., 46, 791813.

    • Search Google Scholar
    • Export Citation
  • Ha, E., and G. R. North, 1995: Model studies of the beam-filling error for rain-rate retrieval with microwave radiometers. J. Atmos. Oceanic Technol., 12, 268281.

    • Search Google Scholar
    • Export Citation
  • Haddad, Z., E. Im, S. L. Durden, and S. Henly, 1996: Stochastic filtering of rain profiles using radar, surface-referenced radar, or combined radar–radiometer measurements. J. Appl. Meteor., 35, 229242.

    • Search Google Scholar
    • Export Citation
  • Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and K. Okamoto, 2000: Rain-profiling algorithm for the TRMM precipitation radar. J. Appl. Meteor., 39, 20382052.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809817.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors, 2001: The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 18011820.

    • Search Google Scholar
    • Export Citation
  • Laughlin, C. R., 1981: On the effect of temporal sampling on the observation of mean rainfall. Precipitation Measurements from Space, D. Atlas and O. Thiele, Eds., NASA Publ., D59–D66.

    • Search Google Scholar
    • Export Citation
  • Liu, L., and A. Y. Hou, 2008: Evaluation of coincident passive microwave rainfall estimates using TRMM PR and ground measurements as references. J. Appl. Meteor. Climatol., 47, 31703187.

    • Search Google Scholar
    • Export Citation
  • McCollum, J. R., W. F. Krajewski, R. R. Ferraro, and M. B. Ba, 2002: Evaluation of biases of satellite rainfall estimation algorithms over the continental United States. J. Appl. Meteor., 41, 10651080.

    • Search Google Scholar
    • Export Citation
  • McConnell, A., and G. North, 1987: Sampling errors in satellite estimates of tropical rain. J. Geophys. Res., 92, 95679570.

  • Meneghini, R., T. Iguchi, T. Kozu, L. Liao, K. Okamoto, J. A. Jones, and J. Kwiatkowski, 2000: Use of the surface reference technique for path attenuation estimates from the TRMM Precipitation Radar. J. Appl. Meteor., 39, 20532070.

    • Search Google Scholar
    • Export Citation
  • Morrissey, M. L., and J. E. Janowiak, 1996: Sampling-induced conditional biases in satellite climate-scale rainfall estimates. J. Appl. Meteor., 35, 541548.

    • Search Google Scholar
    • Export Citation
  • Negri, A. J., T. L. Bell, and L. Xu, 2002: Sampling of the diurnal cycle of precipitation using TRMM. J. Atmos. Oceanic Technol., 19, 13331344.

    • Search Google Scholar
    • Export Citation
  • North, G. R., 1988: Survey of sampling problems for TRMM. Tropical Rainfall Measurements, J. S. Theon and N. Fugono, Eds., A. Deepak, 337–348.

    • Search Google Scholar
    • Export Citation
  • Oki, R., and A. Sumi, 1994: Sampling simulation of TRMM rainfall estimation using radar–AMeDAS composites. J. Appl. Meteor., 33, 15971608.

    • Search Google Scholar
    • Export Citation
  • Olson, W. S., and Coauthors, 2006: Precipitation and latent heating distributions from satellite passive microwave radiometry. Part I: Improved method and uncertainty estimates. J. Appl. Meteor. Climatol., 45, 702720.

    • Search Google Scholar
    • Export Citation
  • Qiu, S., P. Pellegrino, R. Ferraro, and L. Zhao, 2005: The improved AMSU rain-rate algorithm and its evaluation for a cool season event in the western United States. Wea. Forecasting, 20, 761774.

    • Search Google Scholar
    • Export Citation
  • Robertson, F. R., D. E. Fizjarrald, and C. D. Kummerow, 2003: Effects of uncertainty in TRMM precipitation radar path integrated attenuation on interannual variations of tropical oceanic rainfall. Geophys. Res. Lett., 30, 1180, doi:10.1029/2002GL016416.

    • Search Google Scholar
    • Export Citation
  • Salby, M. L., and P. Callaghan, 1997: Sampling error in climate properties derived from satellite measurements: Consequences of undersampled diurnal variability. J. Climate, 10, 1836.

    • Search Google Scholar
    • Export Citation
  • Shin, K., and G. R. North, 1988: Sampling error study for rainfall estimate by satellite using a stochastic model. J. Appl. Meteor., 27, 12181231.

    • Search Google Scholar
    • Export Citation
  • Shin, K., G. R. North, Y. Ahn, and P. A. Arkin, 1990: Times scales and variability of area-averaged tropical oceanic rainfall. Mon. Wea. Rev., 118, 15071516.

    • Search Google Scholar
    • Export Citation
  • Spencer, R. W., H. M. Goodman, and R. E. Hood, 1989: Precipitation retrieval over land and ocean with the SSM/I. Part I: Identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol., 6, 254273.

    • Search Google Scholar
    • Export Citation
  • Steiner, M., 1996: Uncertainty of estimates of monthly areal rainfall for temporally sparse remote observations. Water Resour. Res., 32, 373388.

    • Search Google Scholar
    • Export Citation
  • Steiner, M., T. L. Bell, Y. Zhang, and E. F. Wood, 2003: Comparison of two methods for estimating the sampling-related uncertainty of satellite rainfall averages based on a large radar dataset. J. Climate, 16, 37593778.

    • Search Google Scholar
    • Export Citation
  • Weng, F., L. Zhao, R. Ferraro, G. Poe, X. Li, and N. Grody, 2003: Advanced Microwave Sounding Unit cloud and precipitation algorithms. Radio Sci., 38, 80688079.

    • Search Google Scholar
    • Export Citation
  • Wilheit, T. T., 1988: Error analysis for the Tropical Rainfall Measuring Mission (TRMM). Tropical Rainfall Measurements, J. S. Theon and N. Fugono, Eds., A. Deepak, 377–385.

    • Search Google Scholar
    • Export Citation
  • Wolff, D. B., and B. L. Fisher, 2009: Assessing the relative performance of microwave-based satellite rain-rate retrievals using TRMM ground validation data. J. Appl. Meteor., 48, 10691099.

    • Search Google Scholar
    • Export Citation
  • Wolff, D. B., D. A. Marks, E. Amitai, D. S. Silberstein, B. L. Fisher, A. Tokay, J. Wang, and J. L. Pippitt, 2005: Ground validation for the Tropical Rainfall Measuring Mission (TRMM). J. Atmos. Oceanic Technol., 22, 365380.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 34 34 34
PDF Downloads 1 1 1

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI, AMSR-E, SSM/I, AMSU-B, and the TRMM PR

View More View Less
  • 1 Science Systems and Applications, Inc., Lanham, and NASA Goddard Space Flight Center, Greenbelt, Maryland
Restricted access

Abstract

Passive and active microwave rain sensors on board Earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscures the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different spaceborne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25° resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands, and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean, and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite, and the regional climatology. The most significant result from this study found that each of the satellites incurred negative long-term oceanic retrieval biases of 10%–30%.

Corresponding author address: Brad Fisher, Science Systems and Applications, Inc., Ste. 600, 10210 Greenbelt Rd., Lanham, MD 20706. E-mail: bradford.fisher@ssaihq.com

Abstract

Passive and active microwave rain sensors on board Earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscures the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different spaceborne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25° resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands, and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean, and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite, and the regional climatology. The most significant result from this study found that each of the satellites incurred negative long-term oceanic retrieval biases of 10%–30%.

Corresponding author address: Brad Fisher, Science Systems and Applications, Inc., Ste. 600, 10210 Greenbelt Rd., Lanham, MD 20706. E-mail: bradford.fisher@ssaihq.com
Save