Title page for ETD etd-08192004-224424


Type of Document Master's Thesis
Author Potta, Suchita
Author's Email Address spotta1@lsu.edu
URN etd-08192004-224424
Title Application of Stochastic Downscaling Techniques to Global Climate Model Data for Regional Climate Prediction
Degree Master of Science in Civil Engineering (M.S.C.E.)
Department Civil & Environmental Engineering
Advisory Committee
Advisor Name Title
Vijay P Singh Committee Chair
Donald D Adrian Committee Member
Vibhas Aravamuthan Committee Member
Keywords
  • green house gas integration
  • stochastic downscaling
  • control integration
  • green house gas plus sulphate integration
  • global warming
Date of Defense 2004-07-29
Availability unrestricted
Abstract
Global warming is the most important issue of the present day that affects the climate drastically. This research was carried out to find out the effects of Global warming on Louisiana in future on a very finer spatial and temporal scale. For this purpose spatial downscaling technique is used, where finer resolution climate information is derived from a coarser resolution Global Climate Model (GCM) output. Empirical/statistical downscaling method is used in which sub grid scale changes are calculated as a function of large scale climate. For this purpose a stochastic weather generator and two Global models are considered. The two global models are CCCma (Canadian Center for Climate Modeling and Analysis) and CSIRO (Australia's Commonwealth Scientific and Industrial Research Organization). The stochastic weather generator used is Climate Generator (CLIGEN). The global monthly means are calculated until the year 2090 from the available daily data of CCCma and CSIRO and the units are converted according to that used in CLIGEN. The monthly means of the parameter files of CLIGEN are replaced with the Global monthly means, and the other statistical parameters such as standard deviation, skewness, etc are changed accordingly and weather is generated using the CLIGEN until the year 2090 for Louisiana. Statistical analysis is performed for the climate generated using the two Global models and comparisons are made between the results of the two models. Also time series plots are drawn for the generated climate of the two models taking one year as a representative year.
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