Application of bias correction in climate prediction
The issue of climate change and its effects on many aspects of the environment become more challenges for society. The emission and concentration of carbon dioxide and greenhouse gases give impact to the increase in temperature, and thus leading to global warming. It is important and desirable to an...
| Main Author: | |
|---|---|
| Format: | Undergraduates Project Papers |
| Language: | English English English English |
| Published: |
2017
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/21012/ http://umpir.ump.edu.my/id/eprint/21012/ http://umpir.ump.edu.my/id/eprint/21012/1/Application%20of%20bias%20correction%20in%20climate%20prediction-Table%20of%20contents.pdf http://umpir.ump.edu.my/id/eprint/21012/7/Application%20of%20bias%20correction%20in%20climate%20prediction-Abstract.pdf http://umpir.ump.edu.my/id/eprint/21012/13/Application%20of%20bias%20correction%20in%20climate%20prediction-Chapter%201.pdf http://umpir.ump.edu.my/id/eprint/21012/19/Application%20of%20bias%20correction%20in%20climate%20prediction-References.pdf |
| Summary: | The issue of climate change and its effects on many aspects of the environment become more challenges for society. The emission and concentration of carbon dioxide and greenhouse gases give impact to the increase in temperature, and thus leading to global warming. It is important and desirable to analyze and predict the changes of climatic variables especially for rainfall. However, the accuracy in the climate simulation is becomes significant to ensure the reliability of the projection results. Thus, the bias correction (BC) methods were suggested to imply to treat the gaps between observed and simulated results. This study is focus on analysis the prediction patterns of rainfall in Lubuk Paku and Temerloh in Pahang state based on the historical rainfall. The rainfall pattern can be estimate the future climate change, general circulation models (GCMs) are applied. Therefore, Statistical Downscaling Model (SDSM) is applied in order to convert the coarse spatial resolution of the GCMs output into a fine resolution. However, there are biases in SDSM result and GCMs result. Therefore, two bias correction methods which are Linear Scaling (LS) and Local Intensity Scaling (LOCI) are applied to reduce the bias of those model and the performance of those methods are being compared. |
|---|