| Autor: |
Shweta Panjwani, S. Naresh Kumar, Laxmi Ahuja |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Algorithms for Intelligent Systems ISBN: 9789811601668 |
| DOI: |
10.1007/978-981-16-0167-5_9 |
| Popis: |
Global climate models (GCMs) are common source of developing scenarios. These scenario data (temperatures) have cold or hot bias that can be corrected using different bias correction methods. To reduce the uncertainties in impact assessment studies, suitable correction method must be used to correct the climate data. This study compared the scaling and empirical quantile mapping bias correction method for the observed IMD temperature (maximum and minimum) data for 35 years (1971–2005). Both the methods are able to correct temperature near to the observed data. But temperatures corrected by quantile method possess much similar trend to the observed data across 35 years with low RMSE values. So, quantile mapping can perform better than scaling method. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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