Editorial Type:
Article
Article Type:
Research Article

Conditional Bias in Radar Rainfall Estimation

Grzegorz J. Ciach Environmental Verification and Analysis Center, University of Oklahoma, Norman, Oklahoma

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Mark L. Morrissey Environmental Verification and Analysis Center, University of Oklahoma, Norman, Oklahoma

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Witold F. Krajewski Iowa Institute of Hydraulic Research, The University of Iowa, Iowa City, Iowa

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Print Publication:
01 Nov 2000
DOI:
https://doi.org/10.1175/1520-0450(2000)039<1941:CBIRRE>2.0.CO;2
Page(s):
1941–1946
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Abstract

The goal of this study is to improve understanding of the optimization criteria for radar rainfall (RR) products. Conditional bias (CB) is formally defined and discussed. The CB is defined as the difference between a given rain rate and the conditional average of its estimates. A simple analytical model is used to study the behavior of CB and its effect on the relationship between the estimates and the truth. This study shows the measurement errors of near-surface radar reflectivity and the natural reflectivity–rainfall rate variability can affect CB. This RR estimation error component is also compared with the commonly used mean-square error (MSE). A dilemma between the minimization of these two errors is demonstrated. Removing CB from the estimates significantly increases MSE, but minimizing MSE results in a large CB that manifests itself in underestimation of strong rainfalls.

Corresponding author address: Grzegorz J. Ciach, Environmental Verification and Analysis Center, University of Oklahoma, Norman, OK 73069.

gciach@ou.edu

Abstract

The goal of this study is to improve understanding of the optimization criteria for radar rainfall (RR) products. Conditional bias (CB) is formally defined and discussed. The CB is defined as the difference between a given rain rate and the conditional average of its estimates. A simple analytical model is used to study the behavior of CB and its effect on the relationship between the estimates and the truth. This study shows the measurement errors of near-surface radar reflectivity and the natural reflectivity–rainfall rate variability can affect CB. This RR estimation error component is also compared with the commonly used mean-square error (MSE). A dilemma between the minimization of these two errors is demonstrated. Removing CB from the estimates significantly increases MSE, but minimizing MSE results in a large CB that manifests itself in underestimation of strong rainfalls.

Corresponding author address: Grzegorz J. Ciach, Environmental Verification and Analysis Center, University of Oklahoma, Norman, OK 73069.

gciach@ou.edu

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Journal of Applied Meteorology and Climatology

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