The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the predict...The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the prediction skill of the system at a 10-day lead time for monthly TC activity is given based on 35-year(1981–2015)hindcasts with 24 ensemble members.The results show that FGOALS-f2 can capture the climatology of TC track densities in each month,but there is a delay in the monthly southward movement in the area of high track densities of TCs.The temporal correlation coefficient of TC frequency fluctuates across the different months,among which the highest appears in October(0.59)and the lowest in August(0.30).The rank correlation coefficients of TC track densities are relatively higher(R>0.6)in July,September,and November,while those in August and October are relatively lower(R within 0.2 to 0.6).For real-time prediction of TCs in 2020(July to November),FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement.Besides,the system successfully forecasts the correct sign of monthly anomalies of TC frequency and accumulated cyclone energy for 2020(July to November)in the SCS.展开更多
The approach of getting useful information of monthly dynamical prediction from ensemble forecasts is studied. The extended range ensemble forecasts (8 members, the initial perturbations of the lagged average forecast...The approach of getting useful information of monthly dynamical prediction from ensemble forecasts is studied. The extended range ensemble forecasts (8 members, the initial perturbations of the lagged average forecast (LAF)(0000, 0600, 1200 and 1800 GMT in two consecutive days) of the 500 hPa height field with the global spectral model (T63L16) from January to May 1997 are provided by the National Climate Center of China. The relationship between the spread of ensemble measured by root–mean–square deviation of ensemble member from ensemble mean and forecast skill (the anomaly correlation or the root–mean–square distance between the ensemble mean forecast and the observation) is significant. The spread of ensemble can evaluate the useful forecast days N for the best estimate of 30 days mean. Thus, a weighted mean approach based on ensemble spread is put forward for monthly dynamical prediction. The anomaly correlation of the weighted monthly mean by the ensemble spread is higher than that of both the arithmetic mean and the linear weighted mean. Better results of the monthly mean circulation and anomaly are obtained from the ensemble spread weighted mean. Key words Monthly prediction - Ensemble method - Spread of ensemble Supported by the Excellent National State Key Laboratory Project (49823002), the National Key Project ‘Study on Chinese Short-Term Climate Forecast System’ (96-908-02) and IAP Innovation Foundation (8-1308).The data were provided through the National Climate Center of China. The authors wish to thank Ms. Chen Lijuan for her assistance.展开更多
In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level...In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.展开更多
基金funded by the Na-tional Natural Science Foundation of China[grant number 42005117]the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDB40030205]the Key Special Project for the Introducing Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory(Guangdong)[grant number GML2019ZD0601]。
文摘The monthly prediction skill for tropical cyclone(TC)activity in the South China Sea(SCS)during the typhoon season(July to November)was evaluated using the FGOALS-f2 ensemble prediction system.Specifically,the prediction skill of the system at a 10-day lead time for monthly TC activity is given based on 35-year(1981–2015)hindcasts with 24 ensemble members.The results show that FGOALS-f2 can capture the climatology of TC track densities in each month,but there is a delay in the monthly southward movement in the area of high track densities of TCs.The temporal correlation coefficient of TC frequency fluctuates across the different months,among which the highest appears in October(0.59)and the lowest in August(0.30).The rank correlation coefficients of TC track densities are relatively higher(R>0.6)in July,September,and November,while those in August and October are relatively lower(R within 0.2 to 0.6).For real-time prediction of TCs in 2020(July to November),FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement.Besides,the system successfully forecasts the correct sign of monthly anomalies of TC frequency and accumulated cyclone energy for 2020(July to November)in the SCS.
基金Supported by the Excellent National State Key Laboratory Project! (49823002)the National Key Project 'Study on Chinese Short
文摘The approach of getting useful information of monthly dynamical prediction from ensemble forecasts is studied. The extended range ensemble forecasts (8 members, the initial perturbations of the lagged average forecast (LAF)(0000, 0600, 1200 and 1800 GMT in two consecutive days) of the 500 hPa height field with the global spectral model (T63L16) from January to May 1997 are provided by the National Climate Center of China. The relationship between the spread of ensemble measured by root–mean–square deviation of ensemble member from ensemble mean and forecast skill (the anomaly correlation or the root–mean–square distance between the ensemble mean forecast and the observation) is significant. The spread of ensemble can evaluate the useful forecast days N for the best estimate of 30 days mean. Thus, a weighted mean approach based on ensemble spread is put forward for monthly dynamical prediction. The anomaly correlation of the weighted monthly mean by the ensemble spread is higher than that of both the arithmetic mean and the linear weighted mean. Better results of the monthly mean circulation and anomaly are obtained from the ensemble spread weighted mean. Key words Monthly prediction - Ensemble method - Spread of ensemble Supported by the Excellent National State Key Laboratory Project (49823002), the National Key Project ‘Study on Chinese Short-Term Climate Forecast System’ (96-908-02) and IAP Innovation Foundation (8-1308).The data were provided through the National Climate Center of China. The authors wish to thank Ms. Chen Lijuan for her assistance.
文摘In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.