碩士 / 國立中央大學 / 大氣物理研究所 / 100 / Abstract
The capability of various oceanic sub-surface variables in predicting tropical sea surface temperature (SST) is examined in this study. The oceanic sub-surface variables include temperature, salinity, and zonal velocity at various depths from the Global Ocean Data Assimilation System (GODAS), the Simple Ocean Data Assimilation Reanalysis (SODA) and the Ocean reanalysis (ORA-S3). To prevent the distortion of forecast skill by persistence, the tropic SST was first normalized using the persistence neutralization transformation to neutralize its persistence. Then, the forecast capability was estimated using three measures. The first and the second measures were the percentages of explained variance and the correlation coefficients from linear fits between the tropic SSTA and a given subsurface variable. These two measures were used primarily for quick screening purpose. The third one was correlation coefficients from cross-validation procedure. This measure was used to estimate the true forecast skill of each examined predictor.
Results from linear fits showed that percentages of explained variance and correlation coefficients can indeed speed up the screening of predictors. However, they also showed that variables at great depths had very high forecast skills, which were not reproduced in those of Cross-Validation. These artificial skills were suspected to be generated primarily due to the replacement of missing data with the corresponding climate mean in data assimilation stage. Therefore, to correctly select predictors with better forecast capability, the evaluation of forecast skill should be based on Cross-Validation results.
Results from Cross-Validation showed that variables from ORA-S3 tended to have better skills than those from GODAS and SODA. However, ORA-S3 also showed unusual high forecast skills for lead times about 1yr. Compared assimilation procedure among these three datasets, it appears that ORA-S3 assimilates the salinity in the isothermal layer and incorporate a new bias correction algorithm in assimilation process may be the main reasons responsible for these phenomena.
| Identifer | oai:union.ndltd.org:TW/100NCU05021027 |
| Date | January 2012 |
| Creators | Chun-ta Wu, 吳俊達 |
| Contributors | Yung-an Lee, Chun-ta Wu, 李永安, 吳俊達 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 51 |
Page generated in 0.0068 seconds