Explanation and Optimizing Multi‐Model Blending Algorithm Using Random Variables Theory
Abstract In this study, we modeled the multi‐model blending process using random variables and explicitly derived the distribution of the blended forecast error under the assumption of normally distributed errors. Utilizing this error distribution, we regained the scalar version of the Best Linear U...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-02-01
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| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024GL111622 |
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| _version_ | 1850032923651080192 |
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| author | Yu Wang Yong Cao Yue Shen Ruixia Zhao Xiaoqing Zeng Li Yao Kan Dai |
| author_facet | Yu Wang Yong Cao Yue Shen Ruixia Zhao Xiaoqing Zeng Li Yao Kan Dai |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | Abstract In this study, we modeled the multi‐model blending process using random variables and explicitly derived the distribution of the blended forecast error under the assumption of normally distributed errors. Utilizing this error distribution, we regained the scalar version of the Best Linear Unbiased Estimator. Notably, the model yielded negative weights, which contradict traditional assumptions that weights should be positive. To address this, we modified the algorithm to set negative weights to zero and compared the performance with the original algorithm. Our findings indicate that setting negative weights to zero results in a slight improvement in blending performance compared to using negative weights directly. This improvement may be attributed to modeling the actual forecast error as a normally distributed unified parameter and applying a consistent correlation coefficient across the annual data set. |
| format | Article |
| id | doaj-art-e474b630aac04df683fa95aed8bb0f3c |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-e474b630aac04df683fa95aed8bb0f3c2025-08-20T02:58:26ZengWileyGeophysical Research Letters0094-82761944-80072025-02-01524n/an/a10.1029/2024GL111622Explanation and Optimizing Multi‐Model Blending Algorithm Using Random Variables TheoryYu Wang0Yong Cao1Yue Shen2Ruixia Zhao3Xiaoqing Zeng4Li Yao5Kan Dai6National Meteorological Center of China Meteorological Administration Beijing PR. ChinaNational Meteorological Center of China Meteorological Administration Beijing PR. ChinaChina Meteorological Administration Training Center Beijing PR. ChinaNational Meteorological Center of China Meteorological Administration Beijing PR. ChinaNational Meteorological Center of China Meteorological Administration Beijing PR. ChinaNational Meteorological Center of China Meteorological Administration Beijing PR. ChinaNational Meteorological Center of China Meteorological Administration Beijing PR. ChinaAbstract In this study, we modeled the multi‐model blending process using random variables and explicitly derived the distribution of the blended forecast error under the assumption of normally distributed errors. Utilizing this error distribution, we regained the scalar version of the Best Linear Unbiased Estimator. Notably, the model yielded negative weights, which contradict traditional assumptions that weights should be positive. To address this, we modified the algorithm to set negative weights to zero and compared the performance with the original algorithm. Our findings indicate that setting negative weights to zero results in a slight improvement in blending performance compared to using negative weights directly. This improvement may be attributed to modeling the actual forecast error as a normally distributed unified parameter and applying a consistent correlation coefficient across the annual data set.https://doi.org/10.1029/2024GL111622optimal blendingmulti‐model blendingrandom variablesstochastic processesuncertainty quantification |
| spellingShingle | Yu Wang Yong Cao Yue Shen Ruixia Zhao Xiaoqing Zeng Li Yao Kan Dai Explanation and Optimizing Multi‐Model Blending Algorithm Using Random Variables Theory Geophysical Research Letters optimal blending multi‐model blending random variables stochastic processes uncertainty quantification |
| title | Explanation and Optimizing Multi‐Model Blending Algorithm Using Random Variables Theory |
| title_full | Explanation and Optimizing Multi‐Model Blending Algorithm Using Random Variables Theory |
| title_fullStr | Explanation and Optimizing Multi‐Model Blending Algorithm Using Random Variables Theory |
| title_full_unstemmed | Explanation and Optimizing Multi‐Model Blending Algorithm Using Random Variables Theory |
| title_short | Explanation and Optimizing Multi‐Model Blending Algorithm Using Random Variables Theory |
| title_sort | explanation and optimizing multi model blending algorithm using random variables theory |
| topic | optimal blending multi‐model blending random variables stochastic processes uncertainty quantification |
| url | https://doi.org/10.1029/2024GL111622 |
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