Numerical Studies on Forecast Error Correction of GRAPES Model with Variational Approach
To implement deterministic short-range numerical weather forecast error correction, this study develops a novel approach using the variational method and historical data. Based on time-dependency characteristic of nonsystematic forecast error, variational approach is adopted to establish the mapping...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2019/2856289 |
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| _version_ | 1849308565476474880 |
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| author | Dengxin He Zhimin Zhou Zhaoping Kang Lin Liu |
| author_facet | Dengxin He Zhimin Zhou Zhaoping Kang Lin Liu |
| author_sort | Dengxin He |
| collection | DOAJ |
| description | To implement deterministic short-range numerical weather forecast error correction, this study develops a novel approach using the variational method and historical data. Based on time-dependency characteristic of nonsystematic forecast error, variational approach is adopted to establish the mapping relation between nonsystematic error series and the prior period nonsystematic error series, so as to estimate nonsystematic error in the future and revise the forecast under the premise of the revision for forecast systematic forecast error. According to the hindcast daily data of geopotential height on 500 hPa generated by GRAPES model on January and July from 2002 to 2010, preliminary analysis is carried out on characteristics of forecast error in East Asia. Further estimation and forecast correction test are conducted for nonsystematic error. The result shows that the nonsystematic forecast error in the GRAPES model has obvious characteristic of state dependency. Nonsystematic forecast error changes along season and the state of weather and accounts for great proportion in total forecast error. Nonsystematic forecast error estimated by variational approach is relatively close to the real forecast error. After nonsystematic correction, the corrected 24 h and 48 h forecast of majority samples has a smaller RMSE. Further study on temperature shows a similar result, even comparing to the observational upper air MICAPS data. |
| format | Article |
| id | doaj-art-043b15c4e9b64a478e7b231a5f4d8159 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-043b15c4e9b64a478e7b231a5f4d81592025-08-20T03:54:25ZengWileyAdvances in Meteorology1687-93091687-93172019-01-01201910.1155/2019/28562892856289Numerical Studies on Forecast Error Correction of GRAPES Model with Variational ApproachDengxin He0Zhimin Zhou1Zhaoping Kang2Lin Liu3Institute of Heavy Rain, China Meteorological Administration, Wuhan 430074, ChinaInstitute of Heavy Rain, China Meteorological Administration, Wuhan 430074, ChinaInstitute of Heavy Rain, China Meteorological Administration, Wuhan 430074, ChinaInstitute of Heavy Rain, China Meteorological Administration, Wuhan 430074, ChinaTo implement deterministic short-range numerical weather forecast error correction, this study develops a novel approach using the variational method and historical data. Based on time-dependency characteristic of nonsystematic forecast error, variational approach is adopted to establish the mapping relation between nonsystematic error series and the prior period nonsystematic error series, so as to estimate nonsystematic error in the future and revise the forecast under the premise of the revision for forecast systematic forecast error. According to the hindcast daily data of geopotential height on 500 hPa generated by GRAPES model on January and July from 2002 to 2010, preliminary analysis is carried out on characteristics of forecast error in East Asia. Further estimation and forecast correction test are conducted for nonsystematic error. The result shows that the nonsystematic forecast error in the GRAPES model has obvious characteristic of state dependency. Nonsystematic forecast error changes along season and the state of weather and accounts for great proportion in total forecast error. Nonsystematic forecast error estimated by variational approach is relatively close to the real forecast error. After nonsystematic correction, the corrected 24 h and 48 h forecast of majority samples has a smaller RMSE. Further study on temperature shows a similar result, even comparing to the observational upper air MICAPS data.http://dx.doi.org/10.1155/2019/2856289 |
| spellingShingle | Dengxin He Zhimin Zhou Zhaoping Kang Lin Liu Numerical Studies on Forecast Error Correction of GRAPES Model with Variational Approach Advances in Meteorology |
| title | Numerical Studies on Forecast Error Correction of GRAPES Model with Variational Approach |
| title_full | Numerical Studies on Forecast Error Correction of GRAPES Model with Variational Approach |
| title_fullStr | Numerical Studies on Forecast Error Correction of GRAPES Model with Variational Approach |
| title_full_unstemmed | Numerical Studies on Forecast Error Correction of GRAPES Model with Variational Approach |
| title_short | Numerical Studies on Forecast Error Correction of GRAPES Model with Variational Approach |
| title_sort | numerical studies on forecast error correction of grapes model with variational approach |
| url | http://dx.doi.org/10.1155/2019/2856289 |
| work_keys_str_mv | AT dengxinhe numericalstudiesonforecasterrorcorrectionofgrapesmodelwithvariationalapproach AT zhiminzhou numericalstudiesonforecasterrorcorrectionofgrapesmodelwithvariationalapproach AT zhaopingkang numericalstudiesonforecasterrorcorrectionofgrapesmodelwithvariationalapproach AT linliu numericalstudiesonforecasterrorcorrectionofgrapesmodelwithvariationalapproach |