Master of Science in Civil Engineering (MSCE)
Civil and Environmental Engineering
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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Potta, Suchita, "Application of stochastic downscaling techniques to Global Climate Model data for regional climate prediction" (2004). LSU Master's Theses. 568.
Vijay P Singh