The Ground-Truth Problem for Satellite Estimates of Rain Rate

Gerald R. North Climate System Research Program, Texas A&M University, College Station, Texas

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Juan B. Valdés Climate System Research Program, Texas A&M University, College Station, Texas

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Eunho Ha Climate System Research Program, Texas A&M University, College Station, Texas

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Samuel S. P. Shen Climate System Research Program, Texas A&M University, College Station, Texas

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Abstract

In this paper a scheme is proposed to use a point raingage to compare contemporaneous measurements of rain rate from a single-field-of-view estimate based on a satellite remote sensor such as a microwave radiometer. Even in the ideal case the measurements are different because one is at a point and the other is an area average over the field of view. Also the point gauge will be located randomly inside the field of view on different overpasses. A space-time spectral formalism is combined with a simple stochastic rain field to find the mean-square deviations between the two systems. It is found that by combining about 60 visits of the satellite to the ground-truth site, the expected error can be reduced to about 10% of the standard deviation of the fluctuations of the systems alone. This seems to be a useful level of tolerance in terms of isolating and evaluating typical biases that might be contaminating retrieval algorithms.

Abstract

In this paper a scheme is proposed to use a point raingage to compare contemporaneous measurements of rain rate from a single-field-of-view estimate based on a satellite remote sensor such as a microwave radiometer. Even in the ideal case the measurements are different because one is at a point and the other is an area average over the field of view. Also the point gauge will be located randomly inside the field of view on different overpasses. A space-time spectral formalism is combined with a simple stochastic rain field to find the mean-square deviations between the two systems. It is found that by combining about 60 visits of the satellite to the ground-truth site, the expected error can be reduced to about 10% of the standard deviation of the fluctuations of the systems alone. This seems to be a useful level of tolerance in terms of isolating and evaluating typical biases that might be contaminating retrieval algorithms.

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