Calibrating Weather Forecastingby Functional Data Analysis
碩士 === 國立中興大學 === 統計學研究所 === 105 === Recently, weather forecasts become accurate as technology advances. However, high-resolution long-term forecasts are still challenging due to the difficulty of measurement certain atmospheric parameters (e.g. soil moisture) and the limitation of computation resou...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/10048299315896008397 |
| Summary: | 碩士 === 國立中興大學 === 統計學研究所 === 105 === Recently, weather forecasts become accurate as technology advances. However,
high-resolution long-term forecasts are still challenging due to the difficulty
of measurement certain atmospheric parameters (e.g. soil moisture) and the limitation
of computation resource. In this article we focus on the calibration of longterm
weather forecasts by historical weather observations and predictions. The
atmospheric parameters are treated as continuous spatial-temporal functions, and
function-on-function linear regression models are utilized. Our experiment on temperature
data in Taiwan shows that our achieves better calibration results compared
to state-of-the art approach.
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