Although extended-range forecasting has exceeded the limit of daily predictability of weather,there are still partially predictable characteristics of meteorological fields in such forecasts.A targeted forecast scheme...Although extended-range forecasting has exceeded the limit of daily predictability of weather,there are still partially predictable characteristics of meteorological fields in such forecasts.A targeted forecast scheme and strategy for extended-range predictable components is proposed.Based on chaotic characteristics of the atmosphere,predictable components and unpredictable random components are separated by using the standpoint of error growth in a numerical model.The predictable components are defined as those with slow error growth at a given range,which are not sensitive to small errors in initial conditions. A numerical model for predictable components(NMPC)is established,by filtering random components with poor predictability.The aim is to maintain predictable components and avoid the influence of rapidly growing forecast errors on small scales. Meanwhile,the analogue-dynamical approach(ADA)is used to correct forecast errors of predictable components,to decrease model error and statistically take into account the influence of random components.The scheme is applied to operational dynamical extended-range forecast(DERF)model of the National Climate Center of China Meteorological Administration (NCC/CMA).Prediction results show that the scheme can improve forecast skill of predictable components to some extent, especially in high predictability regions.Forecast skill at zonal wave zero is improved more than for ultra-long waves and synoptic-scale waves.Results show good agreement with predictability of spatial scale.As a result,the scheme can reduce forecast errors and improve forecast skill,which favors operational use.展开更多
A continuous overcast-rainy weather(CORW) process occurred over the mid-lower reaches of the Yangtze River(MLRYR) in China from February 14 to March 9 in 2009,with a large stretch and long duration that was rarely see...A continuous overcast-rainy weather(CORW) process occurred over the mid-lower reaches of the Yangtze River(MLRYR) in China from February 14 to March 9 in 2009,with a large stretch and long duration that was rarely seen in historical records.Using the empirical orthogonal function(EOF),we analyzed the geopotential height anomaly field of the NCEP-DOE Reanalysis II in the same period,and defined the stable components of extended-range(10-30 days) weather forecast(ERWF).Furthermore,we defined anomalous and climatic stable components based on the variation characteristics of the variance contribution ratio of EOF components.The climatic stable components were able to explain the impact of climatically averaged information on the ERWF,and the anomalous stable components revealed the abnormal characteristics of the continuous overcast-rainy days.Our results show that the stable components,especially the anomalous stable components,can maintain the stability for a longer time(more than 10 days) and manifest as monthly scale low-frequency variation and ultra-long-wave activities.They also behave as ultra-long waves of planetary scale with a stable and vertically coherent structure,reflect the variation of general circulation in mid-high latitudes,display the cycle of the zonal circulation and the movement and adjustment of the ultra-long waves,and are closely linked to the surface CORW process.展开更多
Variables fields such as enstrophy, meridional-wind and zonal-wind variables are derived from monthly 500 hPa geopotential height anomalous fields. In this work, we select original predictors from monthly 500-hPa geop...Variables fields such as enstrophy, meridional-wind and zonal-wind variables are derived from monthly 500 hPa geopotential height anomalous fields. In this work, we select original predictors from monthly 500-hPa geopotential height anomalous fields and their variables in June of 1958 - 2001, and determine comprehensive predictors by conducting empirical orthogonal function (EOF) respectively with the original predictors. A downscaling forecast model based on the back propagation (BP) neural network is built by use of the comprehensive predictors to predict the monthly precipitation in June over Guangxi with the monthly dynamic extended range forecast products. For comparison, we also build another BP neural network model with the same predictands by using the former comprehensive predictors selected from 500-hPa geopotential height anomalous fields in May to December of 1957 - 2000 and January to April of 1958 - 2001. The two models are tested and results show that the precision of superposition of the downscaling model is better than that of the one based on former comprehensive predictors, but the prediction accuracy of the downscaling model depends on the output of monthly dynamic extended range forecast.展开更多
This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scale...This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales.Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth.The predictable components are defined as those with a slow error growth at a given range.A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast(DERF)model of the National Climate Center.At the same time,useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data,reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components.Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method.The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.展开更多
基金supported by National Natural Science Foundation of China (Grant Nos.41105070,40930952 and 41005041)State Key Program of Science and Technology of China(Grant No.2009BAC51B04)Meteorological Special Project of China(Grant No.GYHY 201106016)
文摘Although extended-range forecasting has exceeded the limit of daily predictability of weather,there are still partially predictable characteristics of meteorological fields in such forecasts.A targeted forecast scheme and strategy for extended-range predictable components is proposed.Based on chaotic characteristics of the atmosphere,predictable components and unpredictable random components are separated by using the standpoint of error growth in a numerical model.The predictable components are defined as those with slow error growth at a given range,which are not sensitive to small errors in initial conditions. A numerical model for predictable components(NMPC)is established,by filtering random components with poor predictability.The aim is to maintain predictable components and avoid the influence of rapidly growing forecast errors on small scales. Meanwhile,the analogue-dynamical approach(ADA)is used to correct forecast errors of predictable components,to decrease model error and statistically take into account the influence of random components.The scheme is applied to operational dynamical extended-range forecast(DERF)model of the National Climate Center of China Meteorological Administration (NCC/CMA).Prediction results show that the scheme can improve forecast skill of predictable components to some extent, especially in high predictability regions.Forecast skill at zonal wave zero is improved more than for ultra-long waves and synoptic-scale waves.Results show good agreement with predictability of spatial scale.As a result,the scheme can reduce forecast errors and improve forecast skill,which favors operational use.
基金supported by National Natural Science Foundation of China (Grant No.40930952)Science and Technology Supporting Project (Grant No.2009BAC51B04)
文摘A continuous overcast-rainy weather(CORW) process occurred over the mid-lower reaches of the Yangtze River(MLRYR) in China from February 14 to March 9 in 2009,with a large stretch and long duration that was rarely seen in historical records.Using the empirical orthogonal function(EOF),we analyzed the geopotential height anomaly field of the NCEP-DOE Reanalysis II in the same period,and defined the stable components of extended-range(10-30 days) weather forecast(ERWF).Furthermore,we defined anomalous and climatic stable components based on the variation characteristics of the variance contribution ratio of EOF components.The climatic stable components were able to explain the impact of climatically averaged information on the ERWF,and the anomalous stable components revealed the abnormal characteristics of the continuous overcast-rainy days.Our results show that the stable components,especially the anomalous stable components,can maintain the stability for a longer time(more than 10 days) and manifest as monthly scale low-frequency variation and ultra-long-wave activities.They also behave as ultra-long waves of planetary scale with a stable and vertically coherent structure,reflect the variation of general circulation in mid-high latitudes,display the cycle of the zonal circulation and the movement and adjustment of the ultra-long waves,and are closely linked to the surface CORW process.
基金Publicity of New Techniques of China Meteorological Administration (CMATG2005M38)
文摘Variables fields such as enstrophy, meridional-wind and zonal-wind variables are derived from monthly 500 hPa geopotential height anomalous fields. In this work, we select original predictors from monthly 500-hPa geopotential height anomalous fields and their variables in June of 1958 - 2001, and determine comprehensive predictors by conducting empirical orthogonal function (EOF) respectively with the original predictors. A downscaling forecast model based on the back propagation (BP) neural network is built by use of the comprehensive predictors to predict the monthly precipitation in June over Guangxi with the monthly dynamic extended range forecast products. For comparison, we also build another BP neural network model with the same predictands by using the former comprehensive predictors selected from 500-hPa geopotential height anomalous fields in May to December of 1957 - 2000 and January to April of 1958 - 2001. The two models are tested and results show that the precision of superposition of the downscaling model is better than that of the one based on former comprehensive predictors, but the prediction accuracy of the downscaling model depends on the output of monthly dynamic extended range forecast.
基金supported by the National Natural Science Foundation of China (Grant Nos. 40930952, 41105055)Global Change Study of Major National Scientific Research Plan of China (Grant No. 2012CB955902)Meteorological Special Project of China (Grant Nos. GYHY201106016, GYHY201106015)
文摘This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales.Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth.The predictable components are defined as those with a slow error growth at a given range.A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast(DERF)model of the National Climate Center.At the same time,useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data,reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components.Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method.The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.
