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.
Similar content being viewed by others
References
Bruhn, J.A.; Fry, W.E.; Fick, G.W. 1980: Simulation of daily weather data using theoretical probability distributions, J. Appl. Meteorology 19(9), 1029–1036
Devroye, L. 1986: Non-Uniform Random Variate Generation. Springer-Verlag, New York
Jianping, D.; Simonoff, J. 1994: The construction and properties of boundary kernels for sparse multinomials, J. Comp. Graph. Statis. 3, 1–10
Efron, B. 1979: Bootstrap methods: Another look at the Jackknife, Ann. Stat. 7, 1–26
Hall, P.; Titterington, D.M. 1987: On smoothing sparse multinomial data, Australian J. Stat. 29(1), 19–37
Jones, W.; Rex, R.C.; Threadgill, D.E. 1972: A simulated environmental model of temperature, evaporation, rainfall, and soil moisture, Trans. of the ASAE, 366–372
Kunsch, H.R. 1989: The Jackknife and the Bootstrap for general stationary observations, Ann. Stat. 17, 1217–1241
Lall, U.; Rajagopalan, B.; Tarboton, D.G. 1995: A nonparametric wet/dry spell model for resampling daily precipitation, Water Resour. Res.
Lane, L.J.; Nearing, M.A. 1989: USDA — Water Erosion Prediction Project: Hill Slope Profile Model Documentation, NSERL Report No. 2, National Soil Erosion Research Laboratory, USDA-Agricultural Research Service, W. Lafayette, Indiana 47907
Liu, R.Y.; Singh, K. 1988: Using iid Bootstrap Inference for Some Non-iid Models, Preprint. Department of Statistics, Rutgers University
Nicks, A.D.; Harp, J.F. 1980: Stochastic generation of temperature and solar radiation data, J. Hydr. 48, 1–7
Rajagopalan, B.; Lall, U. 1995: A kernel estimator for discrete distributions, J. Nonparametric Stat. 4, 409–426
Rajagopalan, B.; Lall, U.; Tarboton, D.G. 1995: Evaluation of kernel density estimation methods for daily precipitation resampling, Water Resour. Res., Sep. 1996
Richardson, C.W. 1981: Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resour. Res. 17(1), 182–190
Rosenblatt, M. 1956: Remarks on some nonparametric estimates of a density function, Ann. Math. Stat. 27, 832–837
Sain, S.R.; Baggerly, K.A.; Scott, D.W. 1994: Cross-validation of multivariate densities, J. Amer. Stat. Assoc. 89(427), 807–817
Scott, D.W. 1992: Multivariate Density Estimation, John Wiley and Sons, Inc., New York
Sharma, A.; Tarboton, D.G.; Lall, U. 1995: Streamflow simulation: A nonparametric approach, Water Resour. Res., in press
Sheather, S.J.; Jones, M.C. 1991: A reliable data-based bandwidth selection method for kernel density estimation, J. Royal Stat. Soc. B53, 683–690
Silverman, B.W. 1986: Density Estimation for Statistics and Data Analysis. Chapman and Hall, New York
Wand, M.P.; Jones, M.C. 1994: Multivariate plug-in bandwidth selection, Comp. Stat, 9, 97–116
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Rajagopalan, B., Lall, U., Tarboton, D.G. et al. Multivariate nonparametric resampling scheme for generation of daily weather variables. Stochastic Hydrol Hydraul 11, 65–93 (1997). https://doi.org/10.1007/BF02428426
Issue Date:
DOI: https://doi.org/10.1007/BF02428426