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Comparison of Two Satellite-based Rainfall Algorithms Using Pacific Atoll Raingage Data

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  • a Planetary Geosciences Division, School of Ocean and Earth Science and Technology, University of Hawaii, Honolulu, Hawaii
  • | b Geography Department, University of Delaware, Newark, Delaware
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Abstract

Rainfall estimates for two simple satellite-based rainfall algorithms are verified over the tropical Pacific using a new method that incorporates sparsely distributed raingages. The resulting linear regression relationship between monthly areal rainfall and the highly reflective cloud index agrees with earlier results. However, the GOES precipitation index (GPI), which was calibrated using radar rainfall data obtained from the eastern tropical Atlantic, produces biased areas rainfall estimates over most of the tropical Pacific. However, its precision is greater than the highly reflective cloud index, perhaps due to the GPI's larger spatial dimensions. With the incorporation of calibration coefficients determined in this study, the GPI will produce unbiased estimates of areal rainfall for the tropical Pacific region.

Abstract

Rainfall estimates for two simple satellite-based rainfall algorithms are verified over the tropical Pacific using a new method that incorporates sparsely distributed raingages. The resulting linear regression relationship between monthly areal rainfall and the highly reflective cloud index agrees with earlier results. However, the GOES precipitation index (GPI), which was calibrated using radar rainfall data obtained from the eastern tropical Atlantic, produces biased areas rainfall estimates over most of the tropical Pacific. However, its precision is greater than the highly reflective cloud index, perhaps due to the GPI's larger spatial dimensions. With the incorporation of calibration coefficients determined in this study, the GPI will produce unbiased estimates of areal rainfall for the tropical Pacific region.

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