Erratum to：J.Cent.South Univ.（2014）21：3811-3820DOI：10.1007/s11771-014-2366-9The original version of this article unfortunately contained three mistakes.The mistakes are corrected as follows：1）The spelling of th...Erratum to：J.Cent.South Univ.（2014）21：3811-3820DOI：10.1007/s11771-014-2366-9The original version of this article unfortunately contained three mistakes.The mistakes are corrected as follows：1）The spelling of third author is incorrect.The correct name is Jae-Young PYUN.2）The information of corresponding author is incorrect.The correct information should be Goo-Rak KWON,Professor,PhD;Tel/Fax：＋98-711-7264102;E-mail：grkwon@chosun.ac.kr展开更多
On August 2,a twin-segment solid rocket motor of the largest diameter,grain mass and thrust in China completed its ground test firing with success.The3 m solid motor was independently developed by the Academy of Aeros...On August 2,a twin-segment solid rocket motor of the largest diameter,grain mass and thrust in China completed its ground test firing with success.The3 m solid motor was independently developed by the Academy of Aerospace Solid Propulsion Technology（AASPT）under CASC.展开更多
Accurate and fine-scale short-term precipitation forecasting is crucial for disaster prevention,mitigation,and socioeconomic development.Currently,the direct precipitation forecasts of numerical weather prediction oft...Accurate and fine-scale short-term precipitation forecasting is crucial for disaster prevention,mitigation,and socioeconomic development.Currently,the direct precipitation forecasts of numerical weather prediction often face great challenges and correction methods are still needed to further improve the forecast accuracy.By utilizing the 500-m resolution fusion precipitation data from the Rapid-refresh Integrated Seamless Ensemble(RISE)system in the Beijing-Tianjin-Hebei(BTH)region,this study proposes a new Segmented Classification and Regression machine learning model based on the extreme gradient boosting(XGBoost)algorithm,termed SCR-XGBoost,which can be applied to correct hourly precipitation forecasts in areas with a dense network of weather stations at lead times of 4-6 h.The performance of the model is evaluated according to six metrics:the accuracy(AC),mean absolute error(MAE),root mean square error(RMSE),correlation coefficient(CC),threat score(TS),and bias score(BS).The results indicate that,although the XGBoost algorithm is almost ineffective for directly forecasting precipitation,the SCR-XGBoost model can significantly improve the forecast performance compared with the original RISE forecast,and the segmented correction method for torrential rainfall(≥20 mm h^(-1))outperforms other precipitation grades,which can effectively alleviate the problem of false alarms in the RISE system for heavy rainfall and above(≥10 mm h^(-1)).The optimization rates after applying the SCR-XGBoost model correction in precipitation forecasts can be improved by 6.49%-23.21%in terms of RMSE and MAE reduction,and the CC and AC can be greatly improved by 35.38%-84.39%.Therefore,the SCR-XGBoost algorithm,which introduces precipitation grade classification and multi-layer piecewise machine learning corrections,can significantly improve the 4-6-h precipitation forecast skill,especially for heavy rainfall.The results of this study not only provide new insights for machine learning-based precipitation forecasting,but also help improve rainfall forecasts and the level of disaster prevention and reduction in the BTH region.展开更多
文摘Erratum to：J.Cent.South Univ.（2014）21：3811-3820DOI：10.1007/s11771-014-2366-9The original version of this article unfortunately contained three mistakes.The mistakes are corrected as follows：1）The spelling of third author is incorrect.The correct name is Jae-Young PYUN.2）The information of corresponding author is incorrect.The correct information should be Goo-Rak KWON,Professor,PhD;Tel/Fax：＋98-711-7264102;E-mail：grkwon@chosun.ac.kr
文摘On August 2,a twin-segment solid rocket motor of the largest diameter,grain mass and thrust in China completed its ground test firing with success.The3 m solid motor was independently developed by the Academy of Aerospace Solid Propulsion Technology（AASPT）under CASC.
基金Supported by the National Natural Science Foundation of China(42275012)National Key Research and Development Program of China(2022YFC3004103)+1 种基金Beijing Municipal Science and Technology Project(Z221100005222012)Key Innovation Team Fund of China Meteorological Administration(CMA2022ZD07).
文摘Accurate and fine-scale short-term precipitation forecasting is crucial for disaster prevention,mitigation,and socioeconomic development.Currently,the direct precipitation forecasts of numerical weather prediction often face great challenges and correction methods are still needed to further improve the forecast accuracy.By utilizing the 500-m resolution fusion precipitation data from the Rapid-refresh Integrated Seamless Ensemble(RISE)system in the Beijing-Tianjin-Hebei(BTH)region,this study proposes a new Segmented Classification and Regression machine learning model based on the extreme gradient boosting(XGBoost)algorithm,termed SCR-XGBoost,which can be applied to correct hourly precipitation forecasts in areas with a dense network of weather stations at lead times of 4-6 h.The performance of the model is evaluated according to six metrics:the accuracy(AC),mean absolute error(MAE),root mean square error(RMSE),correlation coefficient(CC),threat score(TS),and bias score(BS).The results indicate that,although the XGBoost algorithm is almost ineffective for directly forecasting precipitation,the SCR-XGBoost model can significantly improve the forecast performance compared with the original RISE forecast,and the segmented correction method for torrential rainfall(≥20 mm h^(-1))outperforms other precipitation grades,which can effectively alleviate the problem of false alarms in the RISE system for heavy rainfall and above(≥10 mm h^(-1)).The optimization rates after applying the SCR-XGBoost model correction in precipitation forecasts can be improved by 6.49%-23.21%in terms of RMSE and MAE reduction,and the CC and AC can be greatly improved by 35.38%-84.39%.Therefore,the SCR-XGBoost algorithm,which introduces precipitation grade classification and multi-layer piecewise machine learning corrections,can significantly improve the 4-6-h precipitation forecast skill,especially for heavy rainfall.The results of this study not only provide new insights for machine learning-based precipitation forecasting,but also help improve rainfall forecasts and the level of disaster prevention and reduction in the BTH region.
