Evaluation of kernel density estimation methods for daily precipitation resampling |
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| Authors: | Balaji Rajagopalan Upmanu Lall and David G Tarboton |
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| Institution: | (1) Lamont-Doherty Earth Observatory of Columbia University, USA Palisades, NY;(2) Dept. of Civil & Environmental Engineering, Utah Water Res. Lab., Utah State University, 84322 Logan, UT, USA |
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| Abstract: | Kernel density estimators are useful building blocks for empirical statistical modeling of precipitation and other hydroclimatic
variables. Data driven estimates of the marginal probability density function of these variables (which may have discrete
or continuous arguments) provide a useful basis for Monte Carlo resampling and are also useful for posing and testing hypotheses
(e.g bimodality) as to the frequency distributions of the variable. In this paper, some issues related to the selection and
design of univariate kernel density estimators are reviewed. Some strategies for bandwidth and kernel selection are discussed
in an applied context and recommendations for parameter selection are offered. This paper complements the nonparametric wet/dry
spell resampling methodology presented in Lall et al. (1996). |
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