{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/147843"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/147843","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"STATISTICAL MODELLING AND ANALYSIS FOR REGIONAL CLIMATE CHANGE","abstract":"General circulation models (GCMs) are often used to inform climate change information, however, due to their coarse spatial resolutions, downscaling approaches are often relied to transit the coarser-scale GCM outputs to higher resolutions. Multivariate multisite weather generators (MMWGs) are appealing tools, as they allow for simulations of multiple realizations of climate change scenarios consistent with local-scale weather characteristics as well as large-scale climate change signal informed by GCMs. This thesis developed a new MMWG which integrates a single WG with a post-processing technique to rebuild the observed inter-site, inter-variable correlations, and temporal structures in the simulations. The proposed MMWG was then adapted and extended to a multivariate multisite statistical downscaling (MMSD) approach for regional climate change study. Along with these statistical modeling approaches, observational investigation of the climate change signal in the gauge-based precipitation observations was also carried out. The proposed approaches could add value to modeling of regional climate change.","abstract_html":"General circulation models (GCMs) are often used to inform climate change information, however, due to their coarse spatial resolutions, downscaling approaches are often relied to transit the coarser-scale GCM outputs to higher resolutions. Multivariate multisite weather generators (MMWGs) are appealing tools, as they allow for simulations of multiple realizations of climate change scenarios consistent with local-scale weather characteristics as well as large-scale climate change signal informed by GCMs. This thesis developed a new MMWG which integrates a single WG with a post-processing technique to rebuild the observed inter-site, inter-variable correlations, and temporal structures in the simulations. The proposed MMWG was then adapted and extended to a multivariate multisite statistical downscaling (MMSD) approach for regional climate change study. Along with these statistical modeling approaches, observational investigation of the climate change signal in the gauge-based precipitation observations was also carried out. The proposed approaches could add value to modeling of regional climate change.","abstract_has_math":false,"creators":["LI XIN"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05-22","date_published":"2018-05-22","updated_at":"2026-07-24T03:31:38Z","subjects":["Weather Generator, Statistical Downscaling, Rainfall Disaggregation, Wet and Dry Spells, Rainfall Extremes, Singapore"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["LI XIN"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2018-05-22"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://scholarbank.nus.edu.sg/handle/10635/147843"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Weather Generator, Statistical Downscaling, Rainfall Disaggregation, Wet and Dry Spells, Rainfall Extremes, Singapore"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarbank.nus.edu.sg/bitstreams/7b60b0c8-ef3a-4992-ba84-ddb37e597287/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["General circulation models (GCMs) are often used to inform climate change information, however, due to their coarse spatial resolutions, downscaling approaches are often relied to transit the coarser-scale GCM outputs to higher resolutions. 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