Multivariate nonparametric resampling scheme for generation of daily weather variables |
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| Authors: | B Rajagopalan U Lall D G Tarboton D S Bowles |
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| Institution: | (1) Lamont-Doherty Earth Observatory, Columbia University, Rt. 9W, POB 1000, 10964 Palisades, NY, USA;(2) Utah Water Research Lab., Utah State University, 84322-8200 Logan, UT, USA |
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| Abstract: | A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the
original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought
of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate
conditional probability density function. This improves on the classical Bootstrap technique by generating values that have
not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation
is generated from the nonparametric wet/dry spell model as described in Lall et al. 1995]. A vector of other variables (solar
radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated
by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest.
An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided. |
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| Keywords: | Nonparametric Monte Carlo precipitation weather |
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