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 Extended-range weather forecast refers to 10–30-day weather forecast. It is of great value to meteorological disaster prevention and mitigation and sustainable economical and social development, fills time gap between 1–3-day short-range weather forecast, 3–10-day medium-range weather forecast and short-range climate prediction (monthly prediction and seasonal prediction) and meets requirement of establishing complete forecast system. Thus, it is of highlighted scientific value and very high application value and has been one of the important development directions of international weather and climate operational forecast in nearly a decade.

 

 The core of 10–30-day extended-range forecast is formation and transformation of persistent abnormal weather events (especially extreme weather events). Extended-range forecast is deemed to be successful as long as it can forecast main abnormal weather process (such as heavy rainfall) in the extended range. Many atmospheric phenomena with long life cycles have obvious influence over weather scale system, for example, the planetary scale CGT in the upper troposphere in northern hemisphere’s tropical zone ( Ding, et al ,2005), SCGT in southern hemisphere mid-latitude region (Yang,2009), the 30-60 day oscillation in tropic(Xie,et al,1963)[ Madden-Julian oscillation (MJO) ]and Arctic Oscillation(AO), etc. Researches in the past show that when Intraseasonal Oscillation signal is obviously sustained, 10–30-day extended-range weather processes usually have good predictability and usually lead to later extreme weather and climate events (such as heavy rainfalls, strong temperature rise or fall, etc).

 

 In general, methods of extended-range forecast mainly consist of dynamical model and statistical method which is based on development of atmospheric low frequency signal and big data method. Dynamical model is used by America, British, Canada, Europe Central, Australia, etc in the extended-range forecast of a 30-60 day oscillation in tropic and real-time business model is mainly used to predict MJO index in the following 2 weeks. However, as numerical prediction model is limited to predict MJO’s extended rage, it is still quite difficult to have extended-range forecast for MJO related severe weather progress and abnormal events.

 

 On the other hand, physically, within extended-range scale, ISO is strong signal of atmospheric activities as well as an important element in atmospheric circulation evolution. Therefore, it is another main research direction besides numerical methods to conduct extended-range forecast on basis of ISO signals. From a climate standpoint, ISO is direct background of high frequency weather changes, as well as the main component of monthly and seasonal climate. It is “weather-climate interface” and direct link between weather and climate. By handle and resolute observing data properly, the main atmospheric ISO types of different time scale which are closely related to high impact extreme events can be drew directly. Based on research on elements affecting ISO types evolution and time change rule, taking advantage of atmospheric low frequency oscillation mechanism to analyze communicable signals’ (for example, the ISO type closely related to heavy rainfall and other extreme events in the lower Yangtze region) magnification and effect on forecast areas and based on combined action of different types of ISO, simplified dynamic-stochastic model can be established and can be used to make more effective extended-range weather forecast than that of random judgment and statistical calculation alone.

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. Analytical investigations for big data are major driving force for development of weather forecast methods over 10 days. It is not presupposed the physical condition and may not be limited by the forecast of numerical predictability limit. These methods reduce the subjective assumption influence brought when people treat the data, and the essence is to find the unexpected but valuable knowledge and information occasionally. This needs all data under a certain condition and evaluates whether there is enough data support of different important degrees. The possibility of finding the principles or incidence relation will be increased through data overlay, and the extended range forecast accuracy will be remarkably improved with the increase of observation information increment through multiple observation data.

 On the whole, empirical statistical model and big data model currently have better ISO prediction ability than dynamical model. These statistical and big data method are undoubtedly the more effective ways to study ISO predictability before dynamical model is further improved.