Title

Multivariate Nonparametric Resampling Scheme for Generation of Daily Weather Variables

Document Type

Article

Journal/Book Title/Conference

Stochastic Hydrology and Hydraulics

Volume

11

Issue

1

Publisher

Springer

Publication Date

2-1-1997

First Page

65

Last Page

93

DOI

10.1007/BF02428426

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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