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
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.
Recommended Citation
Rajagopalan, B., Lall, U., Tarboton, D.G. et al. Stochastic Hydrol Hydraul (1997) 11: 65. https://doi.org/10.1007/BF02428426
