This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing...This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1. l（m）. For the South and Southeast Asian summer monsoon, reasonable skill is found in the model＇s forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Nifio3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and E1 Nifio-Southern Oscillation.展开更多
Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improvin...Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy.To improve forecasting accuracy,this paper focuses on two aspects:①proposing a novel hybrid method using Boosting algorithm and a multistep forecast approach to improve the forecasting capacity of traditional ARMA model;②calculating the existing error bounds of the proposed method.To validate the effectiveness of the novel hybrid method,one-year period of real data are used for test,which were collected from three operating wind farms in the east coast of Jiangsu Province,China.Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared.Test results show that the proposed method achieves a more accurate forecast.展开更多
基金supported by the National Basic Research Program of China (Grant Nos. 2015CB453200 and 2014CB953900)China Meteorological Special Program (Grant Nos. GYHY 201206016 and GYHY201306020)+1 种基金the National Natural Science Foundation of China (Grant Nos. 41305057, 41275076, and 41375081)the Jiangsu Collaborative Innovation Center for Climate Change, China
文摘This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1. l（m）. For the South and Southeast Asian summer monsoon, reasonable skill is found in the model＇s forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Nifio3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and E1 Nifio-Southern Oscillation.
基金supported by the National High Technology Research and Development of China (863 Program) (No. 2012AA050214)the National Natural Science Foundation of China (No. 51077043)the State Grid Corporation of China (Impact research of source-grid-load interaction on operation and control of future power system)
文摘Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy,the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy.To improve forecasting accuracy,this paper focuses on two aspects:①proposing a novel hybrid method using Boosting algorithm and a multistep forecast approach to improve the forecasting capacity of traditional ARMA model;②calculating the existing error bounds of the proposed method.To validate the effectiveness of the novel hybrid method,one-year period of real data are used for test,which were collected from three operating wind farms in the east coast of Jiangsu Province,China.Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared.Test results show that the proposed method achieves a more accurate forecast.
