基于可预报分量的6-15天数值天气预报业务技术研究
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摘要
经过几十年的发展,数值天气预报取得了很大的进步,天气形势的可用预报目前为5-7天。在短期天气预报取得成功的基础上,世界上主要的数值预报中心均在努力研发各种技术来提高天气预报的准确率与时效。但目前对更长时效的6-15天中期延伸期尺度,预报技巧仍然很低,需要进一步提高预报能力。而恰恰是该时间尺度的预报,对开展防灾、救灾工作具有极其重要的意义,因此这方面的深入探索具有重要的科学价值和应用价值。
本文以建立一个可以实时运行的中期延伸期预报系统为主要目标。针对目前中期延伸期预报的困难,提出了新的预报方法和策略。以国家气候中心业务月动力延伸预报系统为基础,开展了6-15天数值天气预报业务技术研究,最终建立了一个可以实时运行的中期延伸期预报系统。本文主要结论如下：
(1)分析了可预报性与时空尺度的关系,为从时空尺度的角度分离可预报分量和混沌分量提供依据。利用方差分析的方法分别讨论了球谐函数谱分量和EOF分量的可预报性。对比球谐函数谱分量和EOF谱分量的可预报性,认为EOF谱分量更能反映出与低频流型的联系,与可预报性呈现出更好的关系。
(2)从可预报性理论出发,提出了分离可预报分量和混沌分量的方法。将误差增长不超过某个阈值的分量定义为可预报分量,明确了它是确定性预报的预报对象。从可预报分量解释方差的角度给出了可预报度的定义。通过在支撑气候吸引子的基上压缩自由度,结合可预报性与时空尺度的联系,给出了实际应用中可预报分量的计算方法。
(3)提出了确定性预报对象为动力学方程在预报时段内对初值不敏感的可预报分量,以避开预报中内部误差的快速增长。以业务模式为基础,建立了针对可预报分量的预报模式,该模式的建立过程与原始方程模式中处理高频重力波的思路类似,通过在预报过程中抑制混沌分量的发展,避免了重新建立可预报分量的方程组和数值模式,但能同样达到有针对性预报的目的。
(4)基于分离出的可预报分量,提出了一种新的相似判别方法,该方法判别的相似更能代表整个初始场状态的相似。利用该相似判别方法分析了初值相似程度与其误差演变之间的关系,结果表明,误差的相似程度与初值的相似程度成正比,相似初值提供的预报误差信息很接近于实际的预报误差。初值越相似,其短期误差演变的相似性也越高,同时,在空间分布特征上,相似初值间的模式预报误差也有很好的一致性。
(5)分析讨论了利用历史资料改进动力模式的相似-动力方法面临的两个困难,并提出了解决的途径,发展了相似-动力方法。相似选取和误差订正均只对可预报分量进行,避免了小尺度分量预报误差的快速增长对预报效果的影响,试验表明能有效改进大尺度的可预报分量的确定性预报。同时,相似选取和误差订正是针对模式的所有变量和层次进行的,因此预报结果的改善对各个变量的各个层次都存在,这为该方法的业务应用奠定了基础。
(6)提出了可预报分量和混沌分量的集合预报方法。对具有不同特性的可预报分量和混沌分量,采用不同的集合方式。对可确定性预报的可预报分量,从考虑模式不确定性的角度来减小其预报误差；混沌分量预报的信息源并非来源于初值,由于模式误差的存在,由初值集合给出的混沌分量的集合预报值得商榷,进而从最有可能气候概率的角度来给出混沌分量的概率分布,避免了模式误差对混沌分量概率分布的影响。综合可预报分量和混沌分量的集合方式构成可预报分量和混沌分量集合预报方法,以此为基础,建立了6-15天中期延伸期预报系统。
(7)理论讨论了整个中期延伸期预报系统中一些关键问题,包括可预报分量的确定和利用历史资料改进动力模式的方法等方面,并进行部分敏感性试验的分析。
本文的研究致力于在我国现有条件下提高6-15天中期延伸期业务预报水平,一系列研究表明本文建立的中期延伸期预报系统对提高原业务模式预报水平是有效的,显示出潜在的业务应用前景,为进一步改进和发展该系统奠定了基础。
In the past few decades, the skill of numerical weather prediction has been improved significant. Useful skill in medium-range weather forecasts from present-day numerical weather prediction models typically extends to about 6 days. On the basis of the success of short-range weather forecast, the major numerical weather prediction centers around the world engage in technology development for improving the accuracy and timeliness of weather forecasts. However, for longer time scale, such as 6-15 days medium and extended range, the forecast quality is still poor. It is necessary to improve the quality of the forecast results. Medium and extended range forecast is very important for disaster prevention and mitigation. Therefore, further investigation on medium and extended range forecast has important for science and application issue.
In this work, we attempt to establish a real-time medium and extended range forecasting system. For the present difficulties on medium and extended range forecast, some innovative methods and strategies are proposed. Based on an operational dynamical extended range forecasts (DERF) model of NCC/CMA, the 6-15 days operational prediction technology are studied. A real-time medium and extended range forecasting system has been established. The major results and conclusions of this study are summarized as follows:
(1) The relationship of predictability between spatial and temporal scales is analyzed, in order to provide the basis for separating the predictable components and chaotic components from the perspective of time and spatial scales. The predictability of the spectral coefficients of spherical harmonics and empirical orthogonal functions (EOF) are examined by employing variance analysis. Through the comparison of the predictability of the spectral coefficients of spherical harmonics and empirical orthogonal functions, it is clear that EOF spectral component showing a better relationship with predictability, because it better reflects the link with the low-frequency flow.
(2) Based on predictability theory, a method for separating the predictable components and chaotic components has been proposed. The predictable components are defined as that components which the error growth will not exceed a certain threshold, and they are the predictant of deterministic forecast. The definition of predictability is provided by the explained variance of predictable components. By reducing degree of freedom low dimensional climate attractor and combining the relation of predictability and the spatial and temporal scales, the computing method of predictable components have been proposed for application.
(3) Regard the predictable components, which defined as the dynamical equation is not sensitive to initial conditions in forecasts, as the predictant of deterministic forecast, it is helpful to avoid the rapid internal error growth. A targeted numerical model for predictable components has been established based on an operational dynamical extended range forecasts (DERF) model of NCC/CMA. The model building processes are similar to the processing procedure of high-frequency gravity waves in primitive equation model, and avoid the re-establishment of predictable components equations and numerical model with inhibiting the development of chaotic components.
(4) An analogue selection method under small degree of freedom is proposed, which is achieved by reducing degree of freedom on low dimensional climate attractor and predictable components. The regularity of the analogy of error is investigated. Analyses about the relationship of analogue degree between initial and error are studied. The results show that when the forecast have similar initial conditions, the forecast error has analogical characteristics. Compared with the system error, the error under analogical initial condition is more close to actual forecast error that is based on current initial value. Meanwhile, in the spatial structure, forecast error between analogical initial conditions has good consistency.
(5) In this study, we tried to find a solution to the problem for two difficulties in analogue-dynamical method, especially with the emphasis in precondition of analogue-dynamical method. Because the analogue selection and error correction are only for predictable components, forecasting experiments indicate that this method can effectively improve the deterministic forecast skill of large-scale predictable components, and avoid the adverse effect of rapid error growth in small-scale components. Meanwhile, the analogue selection and error correction are for all the variables and levels of model, so that the improvement of forecast are for all the variables and levels of model too, which lay a foundation for its operational application.
(6) An ensemble prediction method is proposed for predictable components and chaotic components. The predictable components and chaotic components, with different characteristics, should be forecast in different ensemble ways. For the predictable components, the forecast error has been reduced by accounting for uncertainties of model error; meanwhile, the forecast information of chaotic components are not from initial conditions because the chaotic components are so sensitive to given initial conditions, and because of model error, the initial perturbation method, which aim to generate an ensemble model forecast; have a debatable premise in common. So the probability distribution of the chaotic components forecast, is not based on an ensemble of initial conditions, but from the perspective of most likely climate probability distribution. It is valid to avoid the effect of model error. Based on the ensemble prediction method for predictable components and chaotic components, a 6-15 days medium and extended range forecasting system is established.
(7) Some key problems for medium and extended range forecast system are discussed. The determination of predictable components and some respects in analogue-dynamical method with historical data to improve the dynamical model are discussed, respectively. Meanwhile, some studies of sensitive experiments on medium and extended range forecast system are investigated.
This study is committed to improve the operational 6-15 days dynamical medium and extended range forecast level. A series of studies show that, comparing to the original operational forecast system, the forecast skills of developed medium and extended range forecast system are significantly improved. The results indicate that the new forecast system has potential operational application prospects, and our results provide support for further improvement and development of the real-time medium and extended range forecasting system.
本文以建立一个可以实时运行的中期延伸期预报系统为主要目标。针对目前中期延伸期预报的困难,提出了新的预报方法和策略。以国家气候中心业务月动力延伸预报系统为基础,开展了6-15天数值天气预报业务技术研究,最终建立了一个可以实时运行的中期延伸期预报系统。本文主要结论如下：
(1)分析了可预报性与时空尺度的关系,为从时空尺度的角度分离可预报分量和混沌分量提供依据。利用方差分析的方法分别讨论了球谐函数谱分量和EOF分量的可预报性。对比球谐函数谱分量和EOF谱分量的可预报性,认为EOF谱分量更能反映出与低频流型的联系,与可预报性呈现出更好的关系。
(2)从可预报性理论出发,提出了分离可预报分量和混沌分量的方法。将误差增长不超过某个阈值的分量定义为可预报分量,明确了它是确定性预报的预报对象。从可预报分量解释方差的角度给出了可预报度的定义。通过在支撑气候吸引子的基上压缩自由度,结合可预报性与时空尺度的联系,给出了实际应用中可预报分量的计算方法。
(3)提出了确定性预报对象为动力学方程在预报时段内对初值不敏感的可预报分量,以避开预报中内部误差的快速增长。以业务模式为基础,建立了针对可预报分量的预报模式,该模式的建立过程与原始方程模式中处理高频重力波的思路类似,通过在预报过程中抑制混沌分量的发展,避免了重新建立可预报分量的方程组和数值模式,但能同样达到有针对性预报的目的。
(4)基于分离出的可预报分量,提出了一种新的相似判别方法,该方法判别的相似更能代表整个初始场状态的相似。利用该相似判别方法分析了初值相似程度与其误差演变之间的关系,结果表明,误差的相似程度与初值的相似程度成正比,相似初值提供的预报误差信息很接近于实际的预报误差。初值越相似,其短期误差演变的相似性也越高,同时,在空间分布特征上,相似初值间的模式预报误差也有很好的一致性。
(5)分析讨论了利用历史资料改进动力模式的相似-动力方法面临的两个困难,并提出了解决的途径,发展了相似-动力方法。相似选取和误差订正均只对可预报分量进行,避免了小尺度分量预报误差的快速增长对预报效果的影响,试验表明能有效改进大尺度的可预报分量的确定性预报。同时,相似选取和误差订正是针对模式的所有变量和层次进行的,因此预报结果的改善对各个变量的各个层次都存在,这为该方法的业务应用奠定了基础。
(6)提出了可预报分量和混沌分量的集合预报方法。对具有不同特性的可预报分量和混沌分量,采用不同的集合方式。对可确定性预报的可预报分量,从考虑模式不确定性的角度来减小其预报误差；混沌分量预报的信息源并非来源于初值,由于模式误差的存在,由初值集合给出的混沌分量的集合预报值得商榷,进而从最有可能气候概率的角度来给出混沌分量的概率分布,避免了模式误差对混沌分量概率分布的影响。综合可预报分量和混沌分量的集合方式构成可预报分量和混沌分量集合预报方法,以此为基础,建立了6-15天中期延伸期预报系统。
(7)理论讨论了整个中期延伸期预报系统中一些关键问题,包括可预报分量的确定和利用历史资料改进动力模式的方法等方面,并进行部分敏感性试验的分析。
本文的研究致力于在我国现有条件下提高6-15天中期延伸期业务预报水平,一系列研究表明本文建立的中期延伸期预报系统对提高原业务模式预报水平是有效的,显示出潜在的业务应用前景,为进一步改进和发展该系统奠定了基础。
In the past few decades, the skill of numerical weather prediction has been improved significant. Useful skill in medium-range weather forecasts from present-day numerical weather prediction models typically extends to about 6 days. On the basis of the success of short-range weather forecast, the major numerical weather prediction centers around the world engage in technology development for improving the accuracy and timeliness of weather forecasts. However, for longer time scale, such as 6-15 days medium and extended range, the forecast quality is still poor. It is necessary to improve the quality of the forecast results. Medium and extended range forecast is very important for disaster prevention and mitigation. Therefore, further investigation on medium and extended range forecast has important for science and application issue.
In this work, we attempt to establish a real-time medium and extended range forecasting system. For the present difficulties on medium and extended range forecast, some innovative methods and strategies are proposed. Based on an operational dynamical extended range forecasts (DERF) model of NCC/CMA, the 6-15 days operational prediction technology are studied. A real-time medium and extended range forecasting system has been established. The major results and conclusions of this study are summarized as follows:
(1) The relationship of predictability between spatial and temporal scales is analyzed, in order to provide the basis for separating the predictable components and chaotic components from the perspective of time and spatial scales. The predictability of the spectral coefficients of spherical harmonics and empirical orthogonal functions (EOF) are examined by employing variance analysis. Through the comparison of the predictability of the spectral coefficients of spherical harmonics and empirical orthogonal functions, it is clear that EOF spectral component showing a better relationship with predictability, because it better reflects the link with the low-frequency flow.
(2) Based on predictability theory, a method for separating the predictable components and chaotic components has been proposed. The predictable components are defined as that components which the error growth will not exceed a certain threshold, and they are the predictant of deterministic forecast. The definition of predictability is provided by the explained variance of predictable components. By reducing degree of freedom low dimensional climate attractor and combining the relation of predictability and the spatial and temporal scales, the computing method of predictable components have been proposed for application.
(3) Regard the predictable components, which defined as the dynamical equation is not sensitive to initial conditions in forecasts, as the predictant of deterministic forecast, it is helpful to avoid the rapid internal error growth. A targeted numerical model for predictable components has been established based on an operational dynamical extended range forecasts (DERF) model of NCC/CMA. The model building processes are similar to the processing procedure of high-frequency gravity waves in primitive equation model, and avoid the re-establishment of predictable components equations and numerical model with inhibiting the development of chaotic components.
(4) An analogue selection method under small degree of freedom is proposed, which is achieved by reducing degree of freedom on low dimensional climate attractor and predictable components. The regularity of the analogy of error is investigated. Analyses about the relationship of analogue degree between initial and error are studied. The results show that when the forecast have similar initial conditions, the forecast error has analogical characteristics. Compared with the system error, the error under analogical initial condition is more close to actual forecast error that is based on current initial value. Meanwhile, in the spatial structure, forecast error between analogical initial conditions has good consistency.
(5) In this study, we tried to find a solution to the problem for two difficulties in analogue-dynamical method, especially with the emphasis in precondition of analogue-dynamical method. Because the analogue selection and error correction are only for predictable components, forecasting experiments indicate that this method can effectively improve the deterministic forecast skill of large-scale predictable components, and avoid the adverse effect of rapid error growth in small-scale components. Meanwhile, the analogue selection and error correction are for all the variables and levels of model, so that the improvement of forecast are for all the variables and levels of model too, which lay a foundation for its operational application.
(6) An ensemble prediction method is proposed for predictable components and chaotic components. The predictable components and chaotic components, with different characteristics, should be forecast in different ensemble ways. For the predictable components, the forecast error has been reduced by accounting for uncertainties of model error; meanwhile, the forecast information of chaotic components are not from initial conditions because the chaotic components are so sensitive to given initial conditions, and because of model error, the initial perturbation method, which aim to generate an ensemble model forecast; have a debatable premise in common. So the probability distribution of the chaotic components forecast, is not based on an ensemble of initial conditions, but from the perspective of most likely climate probability distribution. It is valid to avoid the effect of model error. Based on the ensemble prediction method for predictable components and chaotic components, a 6-15 days medium and extended range forecasting system is established.
(7) Some key problems for medium and extended range forecast system are discussed. The determination of predictable components and some respects in analogue-dynamical method with historical data to improve the dynamical model are discussed, respectively. Meanwhile, some studies of sensitive experiments on medium and extended range forecast system are investigated.
This study is committed to improve the operational 6-15 days dynamical medium and extended range forecast level. A series of studies show that, comparing to the original operational forecast system, the forecast skills of developed medium and extended range forecast system are significantly improved. The results indicate that the new forecast system has potential operational application prospects, and our results provide support for further improvement and development of the real-time medium and extended range forecasting system.
引文
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