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Use of stochastic weathergenerators for precipitation downscaling

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  • Daniel S. Wilks

Abstract

Downscaling coarse‐resolution model representations of climate is a disaggregation problem, in which any number of small‐scale weather sequences can be associated with a given set of large‐scale values. Because of this intrinsic indeterminacy it is natural and logically consistent for downscaling methods to include explicitly random elements. Weather generators are stochastic models for (usually) daily weather time series, which can be used for climate‐change downscaling through appropriate adjustments to their parameters. Two main approaches for such parametric adjustments have been developed, namely changes in the daily weather generator parameters based on imposed or assumed changes in the corresponding monthly statistics, and day‐by‐day changes to the generator parameters that are controlled by daily variations in simulated atmospheric circulation. This paper reviews and compares these two methods for weather‐generator‐based downscaling, focusing on the downscaling of precipitation. WIREs Clim Change 2010 1 898–907 DOI: 10.1002/wcc.85 This article is categorized under: Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change Assessing Impacts of Climate Change > Representing Uncertainty

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  • Daniel S. Wilks, 2010. "Use of stochastic weathergenerators for precipitation downscaling," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 1(6), pages 898-907, November.
    • Handle: RePEc:wly:wirecc:v:1:y:2010:i:6:p:898-907
    DOI: 10.1002/wcc.85
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    1. Reyhaneh Rahimi & Hassan Tavakol-Davani & Mohsen Nasseri, 2021. "An Uncertainty-Based Regional Comparative Analysis on the Performance of Different Bias Correction Methods in Statistical Downscaling of Precipitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2503-2518, June.

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