Discrepancy between Gauges and Satellite Estimates of Rainfall in Equatorial Africa

Jeffrey R. McCollum NOAA/NESDIS Office of Research and Applications, Camp Springs, Maryland

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Arnold Gruber NOAA/NESDIS Office of Research and Applications, Camp Springs, Maryland

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Mamoudou B. Ba NOAA/NESDIS Office of Research and Applications, Camp Springs, Maryland

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Abstract

The Global Precipitation Climatology Project (GPCP) satellite estimates have approximately twice the magnitude of estimates produced from the rain gauges used by the GPCP in central equatorial Africa. Different possible explanations are identified and investigated. The first is that there may not be enough GPCP rain gauges in the area to provide accurate estimates of rainfall for comparisons with satellite estimates. A comparison of the time-averaged GPCP rain gauge estimate with a long-term (over 40 yr) climatology indicates that the GPCP gauge estimates are similar to long-term rainfall averages, suggesting that the GPCP rain gauge analysis is not underestimating rainfall. Two other possible explanations related to the physical properties of the air masses in this region are studied. Evidence from the literature and from estimates of the effective radii of cloud droplets suggests that there may be an abundance of aerosols in central Africa, resulting in an abundance of cloud condensation nuclei, small drops, and inefficient rain processes. The second explanation is that convective clouds forming under dry conditions generally have cloud bases considerably higher than those of clouds forming in moist environments. This leads to an increase in the evaporation rate of the falling rain, resulting in less precipitation reaching the ground. Analysis of the moisture distributions from both the National Centers for Environmental Prediction numerical weather prediction model reanalysis data and the National Aeronautics and Space Administration Water Vapor Project global moisture dataset reveals that the lower troposphere in this region of Africa is relatively dry, which suggests that cloud bases are high.

* Current affiliation: Department of Meteorology, University of Maryland at College Park, College Park, Maryland.

Corresponding author address: Jeffrey R. McCollum, UCAR Visiting Scientist, NOAA/NESDIS Office of Research and Applications, WWB, Room 601, 5200 Auth Road, Camp Springs, MD 20746-4304.

jmccollum@nesdis.noaa.gov

Abstract

The Global Precipitation Climatology Project (GPCP) satellite estimates have approximately twice the magnitude of estimates produced from the rain gauges used by the GPCP in central equatorial Africa. Different possible explanations are identified and investigated. The first is that there may not be enough GPCP rain gauges in the area to provide accurate estimates of rainfall for comparisons with satellite estimates. A comparison of the time-averaged GPCP rain gauge estimate with a long-term (over 40 yr) climatology indicates that the GPCP gauge estimates are similar to long-term rainfall averages, suggesting that the GPCP rain gauge analysis is not underestimating rainfall. Two other possible explanations related to the physical properties of the air masses in this region are studied. Evidence from the literature and from estimates of the effective radii of cloud droplets suggests that there may be an abundance of aerosols in central Africa, resulting in an abundance of cloud condensation nuclei, small drops, and inefficient rain processes. The second explanation is that convective clouds forming under dry conditions generally have cloud bases considerably higher than those of clouds forming in moist environments. This leads to an increase in the evaporation rate of the falling rain, resulting in less precipitation reaching the ground. Analysis of the moisture distributions from both the National Centers for Environmental Prediction numerical weather prediction model reanalysis data and the National Aeronautics and Space Administration Water Vapor Project global moisture dataset reveals that the lower troposphere in this region of Africa is relatively dry, which suggests that cloud bases are high.

* Current affiliation: Department of Meteorology, University of Maryland at College Park, College Park, Maryland.

Corresponding author address: Jeffrey R. McCollum, UCAR Visiting Scientist, NOAA/NESDIS Office of Research and Applications, WWB, Room 601, 5200 Auth Road, Camp Springs, MD 20746-4304.

jmccollum@nesdis.noaa.gov

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  • Andreae, M. O., and Coauthors, 1992: Ozone and Aitken nuclei over equatorial Africa: Airborne observations during DECAFE 88. J. Geophys. Res.,97, 6137–6148.

  • Arkin, P. A., and B. N. Meisner, 1987: The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982–84. Mon. Wea. Rev.,115, 51–74.

  • Ba, M. B., D. Rosenfeld, and A. Gruber, 1998: AVHRR multispectral derived cloud parameters: Relationships to microwave scattering signature and to cloud-to-ground lightning. Preprints, Ninth Conf. on Satellite Meteorology and Oceanography, Paris, France, Amer. Meteor. Soc., 408–411.

  • Christian, H. J., K. T. Driscoll, S. J. Goodman, R. J. Blakeslee, D. A. Mach, and D. E. Buechler, 1996: The Optical Transient Detector (OTD). Proc. 10th Int. Conf. on Atmospheric Electricity, Osaka, Japan, International Commission of Atmospheric Electricity and Society of Atmospheric Electricity of Japan, 368–371.

  • Cotton, W., and R. A. Anthes, 1989: Storm and Cloud Dynamics. Academic Press, 883 pp.

  • Désalmand, F., J. Baudet, and R. Serpolay, 1982: Influence of rainfall on the seasonal variations of cloud condensation nuclei concentrations in a sub-equatorial climate. J. Atmos. Sci.,39, 2076–2082.

  • Ferraro, R. R., 1997: Special Sensor Microwave Imager derived global rainfall estimates for climatological applications. J. Geophys. Res.,102, 16 715–16 735.

  • Fontan, J., A. Druilhet, B. Benech, and R. Lyra, 1992: The DECAFE experiments: Overview and meteorology. J. Geophys. Res.,97, 6123–6136.

  • Groisman, P. Y., and D. R. Legates, 1994: The accuracy of United States precipitation data. Bull. Amer. Meteor. Soc.,75, 215–227.

  • Higgins, R. W., K. C. Mo, and S. D. Schubert, 1996: The moisture budget of the central United States in spring as evaluated in the NCEP/NCAR and the NASA/DAO reanalyses. Mon. Wea. Rev.,124, 939–963.

  • Huffman, G. J., 1997: Estimation of root-mean-square random error for finite samples of estimated precipitation. J. Appl. Meteor.,36, 1191–1201.

  • Huffman, G. J., R. F. Adler, B. Rudolf, U. Schneider, and P. R. Keehn, 1995: Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information. J. Climate,8, 1284–1295.

  • Huffman, G. J., and Coauthors, 1997: The global precipitation climatology product (GPCP) combined precipitation dataset. Bull. Amer. Meteor. Soc.,78, 5–20.

  • Johnson, D. B., 1980: The influence of cloud-base temperature and pressure on droplet concentration. J. Atmos. Sci.,39, 2076–2082.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc.,77, 437–471.

  • Kaufman, Y. J., and T. Nakajima, 1993: Effect of Amazon smoke on cloud microphysics and albedo—Analysis from satellite imagery. J. Appl. Meteor.,32, 729–744.

  • Kessler, E., 1967: On the continuity of water substance. Tech. Memo. IERTM-NSSL 33, U.S. Department of Commerce Environmental Science Services Administration, 125 pp.

  • Mo, K. C., and R. W. Higgins, 1996: Large-scale atmospheric moisture transport as evaluated in the NCEP/NCAR and the NASA/DAO reanalyses. J. Climate,9, 1531–1545.

  • Morrissey, M. L., J. A. Maliekal, J. S. Greene, and J. Wang, 1995: The uncertainty in simple spatial averages using rain gauge networks. Water Resour. Res.,31, 2011–2017.

  • Nakajima, T., and M. D. King, 1990: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory. J. Atmos. Sci.,47, 1878–1893.

  • Nicholson, S. E, 1993: An overview of African rainfall fluctuations of the last decade. J. Climate,6, 1463–1466.

  • Randel, D. L., T. H. Vonder Haar, M. A. Ringerud, G. L. Stephens, T. J. Greenwald, and C. L. Combs, 1996: A new global water vapor dataset. Bull. Amer. Meteor. Soc.,77, 1233–1246.

  • Rosenfeld, D., and Y. Mintz, 1988: Evaporation of rain falling from convective clouds as derived from radar measurements. J. Appl. Meteor.,27, 209–215.

  • Rosenfeld, D., and G. Gutman, 1994: Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data. Atmos. Res.,34, 259–283.

  • Rudolf, B., 1993: Management and analysis of precipitation data on a routine basis. Proc. Int. WMO/IAHS/ETH Symp. on Precipitation and Evaporation, Bratislava, Slovakia, Slovak Hydrometeor. Inst., 69–76.

  • Rudolf, B., H. Hauschild, W. Rueth, and U. Schneider, 1994: Terrestrial precipitation analysis: Operational method and required density of point measurements. NATO ASII/26, Global Precipitations and Climate Change, M. Desbois and F. Désalmand, Eds., Springer Verlag, 173–186.

  • Sellers, W. D., 1965: Physical Climatology. University of Chicago Press, 272 pp.

  • Trenberth, K. E., and C. J. Guillemot, 1995: Evaluation of the global atmospheric moisture budget as seen from analyses. J. Climate,8, 2255–2272.

  • Wilheit, T., A. Chang, and L. Chiu, 1991: Retrieval of monthly rainfall indices from microwave radiometric measurements using probability distribution function. J. Atmos. Oceanic Technol.,8, 118–136.

  • WCRP, 1986: Report on the workshop on global large-scale precipitation datasets for the World Climate Research Program. WCRP-111, WMO/TD-No. 94, 45 pp. [Available from the World Meteorological Organization, P. O. Box 2300, CH-1211 Geneva 2, Switzerland.].

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