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Low frequency component forecast models (LFCF1.0-3.0) for the extended range

Source:      Pubdate:2019-09-26      Hits:808


Low frequency component forecast models(LFCF1.0-3.0)

 for the extended range


Yang Qiuming  Jiangsu Meteorological Science Institute  Research Group of Extended Range Weather Forecast


With the development of many observation methods, including satellite remote sensing, various global data of climatological observation obtained are increasing in recent years. These scientific big data with high data dependency and multiple data property reflect and reflect complicated natural phenomena and relationship. By adopting data decomposition, extension and conversion, and other technologies, partial valid data are extracted from the large amount of data; low frequency variability information of middle latitude area of two hemispheres more comprehensive than sampling analysis can be obtained, providing better development foundation for extreme weather event forecast for 10-30 days extended range and forming seamless operational forecast system from weather to climate. The major global ISO model closely related to extreme weather event of specific area (for example Yangtze River valley) can be extracted from a large amount of multivariable observation data with long number sequence and high coupling, reducing the system complexity to drive the complicated low frequency variability process and system construction by dynamic data so as to extend the forecast lead time of regional extreme weather process remarkably. Yang Qiuming (2014) established a series of simplified time-varying linear forecast models (such as MLR, ECAR, MLR/PC-CAR[1]), obviously extending the forecast lead time, able to forecast following 40~50 d rainfall and 20~30 d low frequency component variation of the lower reaches of Yangtze River. Among them, low frequency component forecast model (LFCF) based on independent regression equations for MLR forecast model is called LFCF1.0[2]; ECAR forecast model based on complex number autoregressive recursion is called LFCF2.0[3]; MLR/PC-CAR forecast model through time coupling of independent regression equations and complex number autoregressive recursion is called LFCF3.0[4]. Based on the big data, these extended range forecast methods are superior to that of small data age, such as ensemble numerical prediction model and classical statistics model, in lead time and stability. It relates to the extended range forecast theoretical research and practice application to improve the forecast accuracy remarkably through the dynamic data update.




[1] Yang Qiuming. Prospects and progresses in the research of the methods for 10-30 days extended-range weather forecast. Advances in Earth Science,2015,30(9): 970-984.

 [2] Yang Qiuming. Multivariable lagged regressive model of low frequency rains over the lower reaches of Yangtze River Valley for extended-range forecast in the early summer of 2013. Meteorological monthly,2015,41(7):881-889.

[3] Yang Qiuming. Extended complex autoregressive model of low frequency rainfalls over the lower reaches of Yangtze river valley for extended-range forecast in 2013. Acta Physica Sinica,2014, 63, doi: 10.7498/aps.63.199202.

[4] Yang Qiuming. Study of the method of the extended-range forecast for the low frequency rainfall over the lower reaches of the Yangtze River in summer based on the 20-30 day oscillation. Acta Meteorologica Sinica, 2014, 72(3): 494-507.