A k‐nearest‐neighbor simulator for daily precipitation and other weather variables
Balaji Rajagopalan,Upmanu Lall +1 more
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In this paper, 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.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).read more
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Resampling of regional climate model output for the simulation of extreme river flows
Robert Leander,T. Adri Buishand +1 more
TL;DR: In this paper, the authors investigated whether resampling of the output from a regional climate model (RCM) can provide realistic long-duration sequences of precipitation and temperature for the simulation of extreme river flows.
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Stochastic generation of annual, monthly and daily climate data: A review
R. Srikanthan,Thomas A. McMahon +1 more
TL;DR: In this paper, a review of the state-of-the-art models for the generation of rainfall and other climate data is presented, including traditional time series models and more complex models which take account of the pseudo-cycles in the data.
Journal ArticleDOI
Multisite simulation of daily precipitation and temperature in the Rhine Basin by nearest‐neighbor resampling
T. Adri Buishand,T. Brandsma +1 more
TL;DR: In this paper, the authors extended the method of nearest-neighbor resampling to simultaneous simulation of daily precipitation and temperature at multiple locations over a large area (25 stations in the German part of the Rhine basin).
Journal ArticleDOI
Techniques for estimating uncertainty in climate change scenarios and impact studies
TL;DR: In this article, the authors provide an overview of uncertainty analysis, including its sources and how it propagates, and some tentative recommenda- tions on strategies for achieving the goal of reliably quantifying uncertainty in global climate change.
Journal ArticleDOI
A technique for generating regional climate scenarios using a nearest‐neighbor algorithm
TL;DR: In this article, a K-nearest neighbor (K-nn) resampling scheme is presented that simulates daily weather variables, and consequently seasonal climate and spatial and temporal dependencies, at multiple stations in a given region.
References
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TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.
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A Process-Based Soil Erosion Model for USDA-Water Erosion Prediction Project Technology
TL;DR: In this paper, a model was developed for estimating soil erosion by water on hillslopes for use in new USDA erosion prediction technology. Detachment, transport, and deposition processes were represented.