Downscaling for climate change impact study
The idea of statistical downscaling is to translate the information we get from the Global Climate Models (GCM) to local and regional scale. The data we get from GCM provided data is very large, approximately 100-300km in size. This data is not going to be useful to planners that works on a more loc...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/77935 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The idea of statistical downscaling is to translate the information we get from the Global Climate Models (GCM) to local and regional scale. The data we get from GCM provided data is very large, approximately 100-300km in size. This data is not going to be useful to planners that works on a more local scale. Using statistical downscaling model (SDSM), we can get the needed data. Most SDSM begin with comparing GCM output in the past with particular observation during the same period. By comparing modelled projection and actual climate observation, researchers can see the relationship between global and regional climate patterns, therefore describe this pattern using statistics. The next step to this statistic is to apply this statistical relation to future climate projection. Assuming that the statistical relationship observed in the past will continue to hold in the future. In this project, SDSM software was used to perform statistical downscaling of precipitation data for Bangladesh, Gobi Desert and Singapore. Using NCEP and GCM daily predictor variables, 4 different types of graph consisting of mean rainfall, maximum rainfall, variance and wet days percentage were generated and compared it to the observed data of each region. |
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