Downscaling techniques in climate modeling

Srinivasa Raju, K and Nagesh Kumar, D (2017) Downscaling techniques in climate modeling. [Book Chapter]

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Abstract

Describes downscaling techniques where GCM outputs are interpolated to the scale of hydrological modeling or local scale requirement. Statistical downscaling techniques that facilitate statistical relationships that metamorphose large-scale atmospheric variables/predictors simulated by GCMs to local scale variables/predictand are discussed in detail. Techniques include Linear and Non-linear regression, Artificial Neural Networks, Statistical Downscaling Model (SDSM), Change Factor, Least-Square, and Standard Support Vector Machines. Detailed discussion about Artificial Neural Networks that includes information about preprocessing, weights, epoch, activation function, training, learning rate, momentum factor, weight updation procedures, and challenges are also presented. SDSM, combination of regression and conditional weather generator techniques, Change Factor, and Support Vector Machine are also briefed. Nested Bias Correction technique which addresses bias across prespecified multiple timescales is also part of this chapter. Reader is expected to understand various statistical downscaling techniques by studying this chapter.

Item Type:	Book Chapter
Publication:	Springer Climate
Series.:	Springer Climate
Publisher:	Springer
Additional Information:	The copyright for this article belongs to the Springer Nature Singapore Pte Ltd.
Keywords:	Artificial neural networks; Change factor; Downscaling; Nested bias correction; SDSM; Support vector machine
Department/Centre:	Division of Mechanical Sciences > Centre for Earth Sciences Division of Mechanical Sciences > Divecha Centre for Climate Change Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research Division of Mechanical Sciences > Civil Engineering
Date Deposited:	14 Aug 2022 06:10
Last Modified:	14 Aug 2022 06:10
URI:	https://eprints.iisc.ac.in/id/eprint/75745

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