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Abstract:
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Quantile translation, or, quantile matching, is a popular method for downscaling between a variable of a climate model output and the corresponding weather variable at a monitoring station. The method assumes asynchronicity of climate and weather and boils down to translating the cdf's of the two variables. For purely random series, such as extremes, this reduces to applying a relationship between the quantile pairs to future model outputs, and obtaining prediction intervals. We extend this method to time-dependent variables. For daily temperature averages, we use low-order regression splines for trend and cycle fitting, and low-order autoregressive model for dependence. The cdf translation is defined for trend and cycle, and non-parametric quantile matching is applied for the remainder. For uncertainty quantification, we propose a parametric bootstrap. We present an application of infrastructure design and adaptation to climate change that examines the timing of freezing and thawing of soils and road beds. Our model predicts spring thaw dates and provides prediction intervals.
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