Extended range （10-30 d） heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error （NCPE） algorithm that combin...Extended range （10-30 d） heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error （NCPE） algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First, nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process, after which the local change characteristics of the attractors are analyzed. Second, the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5, and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently, without failure, based on the NCPE model; the prediction validity periods for 1-2 d, 3-9 d and 10-30 d are 4, 22 and 74 cases, respectively, without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10-30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability, and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.展开更多
Abstract In this paper,the theory of extended Kalman estimation is applied to state estimate ofcompression system, for which a nonlinear model is developed by Greitzer.A criterion ofdetermining whether surge will occu...Abstract In this paper,the theory of extended Kalman estimation is applied to state estimate ofcompression system, for which a nonlinear model is developed by Greitzer.A criterion ofdetermining whether surge will occur in a turbine engine is presented.The combination ofstate estimation and the criterion of determining surge forms a surge prediction algorithm,which is the theoretical basis of designing a surge indicator for the turbine engine.展开更多
Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastr...Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous met- eorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear cross- prediction error （NCPE） model, and their stability in the prediction validity period in 1 0-30-day extended range fore- casting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small （10＂5-10-2）, minor vari- ation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random er- ror has minimal effect on the prediction. When the ratio is in the range of 10-1-2 （i.e., random error dominates）, at- tention should be paid to the random error instead of only the initial error. When the ratio is around 10 2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecast- ing, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depic- ted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect （m 〉 4） is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperat- ure or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.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
The performance of the operational extended range forecast issued by the India Meteorological Department in the nil,low,moderate and high categories of probability of cyclogenesis has been evaluated based on 868 forec...The performance of the operational extended range forecast issued by the India Meteorological Department in the nil,low,moderate and high categories of probability of cyclogenesis has been evaluated based on 868 forecasts issued every Thursday for week 1 and week 2 for the Arabian Sea(AS),Bay of Bengal(BoB)and north Indian Ocean(NIO)as a whole during April 2018 to December 2023.The forecast is biased towards under-warning for low and moderate categories over the NIO,BoB&AS and towards over-warning for high categories over NIO and BoB in week 1.It is biased towards over-warning for moderate&high categories and under-warning for low category forecast over NIO and BoB for week 2.It is biased towards under-warning for low and high categories and over-warning for moderate category forecasts over AS in week 2.The Brier score(Brier skill score)for week 1 and week 2 are 0.051(48.7%)and 0.087(8.6%)over NIO respectively.The association of Madden Julian Oscillation(MJO),equatorial Rossby waves(ERW)and Kelvin waves(KW)with genesis increases and that of low-frequency background waves(LW)and inter-tropical convergence zone(ITCZ)decreases with an increase in the intensity of storms from depression to very severe cyclonic storms(VSCS).About 100%,92%,92%,92%and 100%of the cases of the genesis of VSCS&above category storms over the NIO are associated with stronger westerlies to the south,stronger easterlies to the north,convective phase of MJO,ERW and KW over the region of genesis.展开更多
基金provided by the National Natural Science Foundation of China(Grant Nos.41275039 and 41471305)the Preeminence Youth Cultivation Project of Sichuan (Grant No.2015JQ0037)
文摘Extended range （10-30 d） heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error （NCPE） algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First, nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process, after which the local change characteristics of the attractors are analyzed. Second, the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5, and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently, without failure, based on the NCPE model; the prediction validity periods for 1-2 d, 3-9 d and 10-30 d are 4, 22 and 74 cases, respectively, without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10-30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability, and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.
文摘Abstract In this paper,the theory of extended Kalman estimation is applied to state estimate ofcompression system, for which a nonlinear model is developed by Greitzer.A criterion ofdetermining whether surge will occur in a turbine engine is presented.The combination ofstate estimation and the criterion of determining surge forms a surge prediction algorithm,which is the theoretical basis of designing a surge indicator for the turbine engine.
基金Supported by the National Natural Science Foundation of China(41505012 and 41471305)Open Research Fund of Plateau Atmosphere and Environment Key Laboratory of Sichuan Province(PAEKL-2017-Y1)+2 种基金Scientific Research Fund of Chengdu University of Information Technology(J201613 and KYTZ201607)Innovation Team Fund(16TD0024)Elite Youth Cultivation Project of Sichuan Province(2015JQ0037)
文摘Extended range forecasting of 10-30 days, which lies between medium-term and climate prediction in terms of timescale, plays a significant role in decision-making processes for the prevention and mitigation of disastrous met- eorological events. The sensitivity of initial error, model parameter error, and random error in a nonlinear cross- prediction error （NCPE） model, and their stability in the prediction validity period in 1 0-30-day extended range fore- casting, are analyzed quantitatively. The associated sensitivity of precipitable water, temperature, and geopotential height during cases of heavy rain and hurricane is also discussed. The results are summarized as follows. First, the initial error and random error interact. When the ratio of random error to initial error is small （10＂5-10-2）, minor vari- ation in random error cannot significantly change the dynamic features of a chaotic system, and therefore random er- ror has minimal effect on the prediction. When the ratio is in the range of 10-1-2 （i.e., random error dominates）, at- tention should be paid to the random error instead of only the initial error. When the ratio is around 10 2-10-1, both influences must be considered. Their mutual effects may bring considerable uncertainty to extended range forecast- ing, and de-noising is therefore necessary. Second, in terms of model parameter error, the embedding dimension m should be determined by the factual nonlinear time series. The dynamic features of a chaotic system cannot be depic- ted because of the incomplete structure of the attractor when m is small. When m is large, prediction indicators can vanish because of the scarcity of phase points in phase space. A method for overcoming the cut-off effect （m 〉 4） is proposed. Third, for heavy rains, precipitable water is more sensitive to the prediction validity period than temperat- ure or geopotential height; however, for hurricanes, geopotential height is most sensitive, followed by precipitable water.
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.
文摘The performance of the operational extended range forecast issued by the India Meteorological Department in the nil,low,moderate and high categories of probability of cyclogenesis has been evaluated based on 868 forecasts issued every Thursday for week 1 and week 2 for the Arabian Sea(AS),Bay of Bengal(BoB)and north Indian Ocean(NIO)as a whole during April 2018 to December 2023.The forecast is biased towards under-warning for low and moderate categories over the NIO,BoB&AS and towards over-warning for high categories over NIO and BoB in week 1.It is biased towards over-warning for moderate&high categories and under-warning for low category forecast over NIO and BoB for week 2.It is biased towards under-warning for low and high categories and over-warning for moderate category forecasts over AS in week 2.The Brier score(Brier skill score)for week 1 and week 2 are 0.051(48.7%)and 0.087(8.6%)over NIO respectively.The association of Madden Julian Oscillation(MJO),equatorial Rossby waves(ERW)and Kelvin waves(KW)with genesis increases and that of low-frequency background waves(LW)and inter-tropical convergence zone(ITCZ)decreases with an increase in the intensity of storms from depression to very severe cyclonic storms(VSCS).About 100%,92%,92%,92%and 100%of the cases of the genesis of VSCS&above category storms over the NIO are associated with stronger westerlies to the south,stronger easterlies to the north,convective phase of MJO,ERW and KW over the region of genesis.
