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Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting.

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Title: Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting.
Authors: Chen, Chaohui1 chenchaohui2001@163.com, Li, Xiang1, He, Hongrang1, Xiang, Jie1, Ma, Shenjia1
Source: SCIENCE CHINA Earth Sciences. Apr2018, Vol. 61 Issue 4, p462-472. 11p.
Subjects: Algorithm software, Breeding, Weather forecasting, Ecological disturbances, Atmospheric models
Abstract: We propose a method based on the local breeding of growing modes (LBGM) considering strong local weather characteristics for convection-allowing ensemble forecasting. The impact radius was introduced in the breeding of growing modes to develop the LBGM method. In the local breeding process, the ratio between the root mean square error (RMSE) of local space forecast at each grid point and that of the initial full-field forecast is computed to rescale perturbations. Preliminary evaluations of the method based on a nature run were performed in terms of three aspects: perturbation structure, spread, and the RMSE of the forecast. The experimental results confirm that the local adaptability of perturbation schemes improves after rescaling by the LBGM method. For perturbation physical variables and some near-surface meteorological elements, the LBGM method could increase the spread and reduce the RMSE of forecast, improving the performance of the ensemble forecast system. In addition, different from those existing methods of global orthogonalization approach, this new initial-condition perturbation method takes into full consideration the local characteristics of the convective-scale weather system, thus making convectionallowing ensemble forecast more accurate. [ABSTRACT FROM AUTHOR]
Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting.
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  Data: <searchLink fieldCode="JN" term="%22SCIENCE+CHINA+Earth+Sciences%22">SCIENCE CHINA Earth Sciences</searchLink>. Apr2018, Vol. 61 Issue 4, p462-472. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Algorithm+software%22">Algorithm software</searchLink><br /><searchLink fieldCode="DE" term="%22Breeding%22">Breeding</searchLink><br /><searchLink fieldCode="DE" term="%22Weather+forecasting%22">Weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Ecological+disturbances%22">Ecological disturbances</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink>
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  Data: We propose a method based on the local breeding of growing modes (LBGM) considering strong local weather characteristics for convection-allowing ensemble forecasting. The impact radius was introduced in the breeding of growing modes to develop the LBGM method. In the local breeding process, the ratio between the root mean square error (RMSE) of local space forecast at each grid point and that of the initial full-field forecast is computed to rescale perturbations. Preliminary evaluations of the method based on a nature run were performed in terms of three aspects: perturbation structure, spread, and the RMSE of the forecast. The experimental results confirm that the local adaptability of perturbation schemes improves after rescaling by the LBGM method. For perturbation physical variables and some near-surface meteorological elements, the LBGM method could increase the spread and reduce the RMSE of forecast, improving the performance of the ensemble forecast system. In addition, different from those existing methods of global orthogonalization approach, this new initial-condition perturbation method takes into full consideration the local characteristics of the convective-scale weather system, thus making convectionallowing ensemble forecast more accurate. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of SCIENCE CHINA Earth Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11430-017-9167-5
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 462
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      – SubjectFull: Algorithm software
        Type: general
      – SubjectFull: Breeding
        Type: general
      – SubjectFull: Weather forecasting
        Type: general
      – SubjectFull: Ecological disturbances
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      – SubjectFull: Atmospheric models
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      – TitleFull: Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting.
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              Text: Apr2018
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              Y: 2018
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