A k-nearest-neighhor simulator for daily precipitation and other weather variables

Publication Status is "Submitted" Or "In Press: 
LDEO Publication: 
Publication Type: 
Year of Publication: 
1999
Editor: 
Journal Title: 
Water Resources Research
Journal Date: 
Oct
Place Published: 
Tertiary Title: 
Volume: 
35
Issue: 
10
Pages: 
3089-3101
Section / Start page: 
Publisher: 
ISBN Number: 
0043-1397
ISSN Number: 
Edition: 
Short Title: 
Accession Number: 
ISI:000082832400014
LDEO Publication Number: 
Call Number: 
Abstract: 

A multivariate, nonparametric time series simulation method is provided to generate random sequences of daily weather variables that "honor" the statistical properties of the historical data of the same weather variables at the site. A vector of weather variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, average wind speed, and precipitation) on a day of interest is resampled from the historical data by conditioning on the vector of the same variables (feature vector) on the preceding day. The resampling is done from the k nearest neighbors in state space of the feature vector using a weight function. This approach is equivalent to a nonparametric approximation of a multivariate, lag 1 Markov process. It does not require prior assumptions as to the form of the joint probability density function of the variables. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, is provided. Results are compared with those from the application of a multivariate autoregressive model similar to that of Richardson [1981].

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240NQTimes Cited:60Cited References Count:29

DOI: