發(fā)布時(shí)間：2018-01-13 12:09

  本文關(guān)鍵詞：基于TIGGE的多中心集合平均預(yù)報(bào)理論與應(yīng)用研究　出處：《南京大學(xué)》2016年博士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 集合預(yù)報(bào) 集合平均 集合成員個(gè)數(shù) T213 TIGGE

【摘要】：作為提高業(yè)務(wù)數(shù)值預(yù)報(bào)水平的有效途徑,集合預(yù)報(bào)方法得到廣泛應(yīng)用,并已成為數(shù)值預(yù)報(bào)體系中舉足輕重的重要部分。而集合平均的預(yù)報(bào)技巧,則是衡量和評(píng)價(jià)集合預(yù)報(bào)系統(tǒng)的重要標(biāo)準(zhǔn)之一。本文對(duì)集合平均的預(yù)報(bào)技巧進(jìn)行理論和實(shí)驗(yàn)研究,從理論角度回答了有關(guān)集合平均的若干問(wèn)題,并針對(duì)基于TIGGE的單中心和多中心集合預(yù)報(bào)系統(tǒng)分別進(jìn)行實(shí)驗(yàn)分析。理論研究表明,等權(quán)集合平均的合理性,首先在于當(dāng)無(wú)法根據(jù)以往的表現(xiàn)斷定各成員的相對(duì)優(yōu)劣時(shí),它不僅能夠避免選用最差的成員,而且一定優(yōu)于所有單個(gè)成員的平均水平。集合平均并非總是優(yōu)于它的所有成員。只有當(dāng)各成員的預(yù)報(bào)技巧差距不大、成員之間不存在明顯的誤差正相關(guān)時(shí),集合平均才能優(yōu)于它的所有成員,取得最佳的預(yù)報(bào)效果。集合平均的預(yù)報(bào)技巧不僅取決于單個(gè)成員的預(yù)報(bào)技巧,而且更主要地取決于各成員誤差之間的相互關(guān)系。隨著集合成員數(shù)的增加,在新成員的預(yù)報(bào)技巧與原有成員基本相當(dāng)、新成員相互之間及其與原有的集合平均之間的誤差相關(guān)性與原有集合成員相互之間的誤差相關(guān)性基本相當(dāng)?shù)那疤嵯?集合平均的均方誤差下降并最終達(dá)到飽和。對(duì)于給定的飽和度,集合平均的預(yù)報(bào)技巧達(dá)到飽和所需要的成員個(gè)數(shù),取決于集合成員均方誤差的平均與集合成員相互之間的誤差協(xié)方差的平均之間的比值。盲目地增加新成員不能起到改進(jìn)集合平均的作用。新成員自身的預(yù)報(bào)技巧固然重要,但更重要的是新成員互相之間、及其與原有集合平均之間的誤差相關(guān)性。將等權(quán)集合平均理論推廣到一般的非等權(quán)集合平均。結(jié)果表明,對(duì)于理想狀態(tài)下的最優(yōu)權(quán)重平均,當(dāng)集合成員無(wú)偏、獨(dú)立時(shí),總存在具有物理意義的最優(yōu)權(quán)重集合平均,使之既優(yōu)于最好的單個(gè)成員,也優(yōu)于等權(quán)平均。預(yù)報(bào)的均方誤差可以分解為預(yù)報(bào)誤差的方差和系統(tǒng)誤差的平方兩個(gè)部分之和。本質(zhì)上,集合平均的目的是為了減小預(yù)報(bào)誤差的方差,而偏差訂正則是為了消除系統(tǒng)誤差。將兩者結(jié)合起來(lái),可以得到更為理想的預(yù)報(bào)效果。通過(guò)將訓(xùn)練期設(shè)為預(yù)報(bào)期,并在此基礎(chǔ)上構(gòu)造帶有偏差訂正的最優(yōu)權(quán)重集合平均,即可得出帶有偏差訂正的非等權(quán)集合平均的均方誤差的下界。這在一定程度上體現(xiàn)了集合平均的可預(yù)報(bào)性。將上述理論應(yīng)用到T213集合預(yù)報(bào)系統(tǒng),15個(gè)成員的等權(quán)集合平均在絕大多數(shù)情況下優(yōu)于控制預(yù)報(bào),特別是在中期預(yù)報(bào)中集合平均的優(yōu)勢(shì)更為明顯。對(duì)于T213集合預(yù)報(bào)系統(tǒng),15個(gè)成員已基本實(shí)現(xiàn)95%的飽和度,意味著在不改變控制預(yù)報(bào)水平和擾動(dòng)成員生成方式的前提下,即使再增加新成員,集合平均的均方誤差最多只可能再降低5%。對(duì)于T213集合預(yù)報(bào)系統(tǒng),等權(quán)集合平均的預(yù)報(bào)技巧已接近恒權(quán)集合平均的理論極限。同時(shí),盡管在中期預(yù)報(bào)階段,變權(quán)集合平均有可能取得比等權(quán)平均更高的技巧,但由于各成員預(yù)報(bào)技巧的相對(duì)排名極不穩(wěn)定,變權(quán)集合平均在業(yè)務(wù)應(yīng)用中仍存在較大困難,也難以取得更高的預(yù)報(bào)技巧。因此,對(duì)于T213集合預(yù)報(bào)系統(tǒng),等權(quán)平均仍是集合預(yù)報(bào)的合理選擇。權(quán)重的改進(jìn)意義不大,只有控制預(yù)報(bào)本身的改進(jìn)、以及擾動(dòng)成員生成方式的改進(jìn),才是進(jìn)一步改進(jìn)T213集合平均的關(guān)鍵。對(duì)于基于TIGGE的多中心控制預(yù)報(bào)集合,結(jié)果表明,對(duì)于風(fēng)、溫、壓、濕等氣象要素,ECMWF和NCEP各有優(yōu)勢(shì),而CMA的預(yù)報(bào)技巧則明顯落后于其它兩個(gè)中心。在絕大多數(shù)情況下,三個(gè)中心的等權(quán)集合平均優(yōu)于其最好的單中心。在濕度場(chǎng)的預(yù)報(bào)和其它要素場(chǎng)的中期預(yù)報(bào)中,三個(gè)中心彼此之間的誤差相關(guān)性較弱,因此集合平均的優(yōu)勢(shì)更為突出。盡管CMA的預(yù)報(bào)技巧明顯落后于ECMWF和NCEP,但對(duì)于濕度場(chǎng)預(yù)報(bào)和其它要素場(chǎng)的8-10天預(yù)報(bào),三個(gè)中心的平均明顯優(yōu)于ECMWF和NCEP兩個(gè)中心的平均,說(shuō)明CMA或其它預(yù)報(bào)技巧較低的成員的加入對(duì)于有效改進(jìn)TIGGE多中心集合平均仍有重要意義；而這種改進(jìn)的來(lái)源,則在于多中心成員之間較低的誤差相關(guān)性。等權(quán)集合平均是基于TIGGE的多中心集合平均的合理方法。三個(gè)中心的等權(quán)集合平均的預(yù)報(bào)技巧,已接近帶有偏差訂正的變權(quán)最優(yōu)權(quán)重集合平均的理論極限。
[Abstract]:As an effective way to improve the service level of the numerical prediction, ensemble prediction method has been widely used, and has become an important part of an important numerical prediction system. The ensemble average skill, is one of the important criteria to measure and evaluate the ensemble prediction system. In this paper, the theoretical and experimental research on ensemble average skills, from theory the perspective of problems related to the average collection, and according to a single center and multi center based on TIGGE ensemble prediction system are analyzed and experimental analysis. The theory research shows that reasonable average weighted aggregation, first of all is that when not according to the relative merits of past performance that members, it can not only avoid the worst members the average and is superior to all individual members. The ensemble average is not always better than that of all its members. Only when the members of the report The skills gap between members and there is no obvious positive correlation error, ensemble average ability is better than all its members, has forecast effect. The best ensemble average skill depends not only on the individual members of the forecast skill, and mainly depends on the relationship between the members of the errors. With the increase of the number of ensemble members the forecast skill of new members, and the original members of the basic premise of the new members, the correlation between the error between the original and the ensemble average and the original members of the collection of error correlation between basic phase when under the set average MSE decline and eventually reached saturation. For a given saturation, average ensemble prediction techniques the number of members required to reach saturation, the error covariance between the members of the collection depends on the mean square error of the ensemble flat The ratio between the both. Blindly add new members can not play a role. The improved ensemble mean forecast skill of the new members of their own is important, but more important is the new members to each other, and the correlation between the error of the original set. The average weight of ensemble average theory is extended to non general ensemble average. The results show that the optimal weight average under ideal condition, when the members of a collection of unbiased, independent, there is always the optimal weights with the physical meaning of ensemble averaging, which is superior to the individual members of the best, is better than the right. The average mean square error of prediction can be decomposed variance and the system error of forecasting error square two parts. In essence, the purpose is to set the average prediction error variance decreases, and the deviation is set regular in order to eliminate system error. Combining the two, can get more satisfactory The result of forecast. Through training period for the forecast period, and on the basis of constructing the optimal weights with bias correction set average, lower bounds can be obtained with non bias correction of ensemble mean square error of the average. This reflects the average predictability set to a certain extent. The application of the above theory the T213 ensemble prediction system, the 15 members of the ensemble average in most cases is better than that of the control forecast, especially in the Medium-term Forecast ensemble average of the more obvious advantages. The T213 ensemble prediction system, 15 members have basically achieved 95% saturation, means without changing the control level and disturbance prediction the members of generation, even add a new member of the ensemble average mean square error is only possible to reduce the 5%. for the T213 ensemble prediction system, such as the right set of forecast skill average is close to constant weight set The theory of limit average. At the same time, although in the Medium-term Forecast stage, variable weight set average may have higher average power than other skills, but because the members of the relative ranking of the forecast skill is very unstable, variable weight set average in business applications still exist great difficulties, it is difficult to obtain a higher forecast skill therefore, the T213 ensemble prediction system, a reasonable choice is still the average ensemble. Little improvement significance of weight, only control the prediction itself is improved, and the improved perturbation members generation, is to further improve the T213 ensemble average. The key for TIGGE based multi center control results show that the prediction set. The wind, temperature, pressure, humidity and other meteorological elements, ECMWF and NCEP have their own advantages, and the forecast skills of CMA is obviously lagged behind the other two. In most cases, three heart right set average Better than the best single center. In the humidity field prediction and other elements of market forecasting error, weak correlation between the three centers of each other, so the ensemble average advantage is more prominent. Although the CMA forecast skill is behind ECMWF and NCEP, but for the humidity field prediction and other elements of the 8-10 day forecast, the average was significantly higher than that of ECMWF and NCEP at three centers in two centers, which show that the addition of CMA or other members of the lower forecast skill for the effective improvement of TIGGE multi center ensemble average is still of significance; and the improved source is the error correlation between multi center members of lower right. The collection is a collection of TIGGE multi center average reasonable method based on average. Three of ensemble forecast skill average, is close to the optimal weights with variable weight deviation correction set theoretical limit of average.

【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：P456.7

【相似文獻(xiàn)】

相關(guān)期刊論文 前3條

1 范彬;胡蔦慶;張曉飛;;耦合隨機(jī)共振陣列集合平均增強(qiáng)效應(yīng)[J];儀器儀表學(xué)報(bào);2011年07期

2 張剛;;歐洲多個(gè)耦合氣候模式對(duì)東亞冬季氣候的預(yù)測(cè)性能研究[J];氣象學(xué)報(bào);2012年04期

3 ;[J];;年期

相關(guān)會(huì)議論文 前1條

1 于水燕;智協(xié)飛;;中國(guó)東部水汽的多模式集成預(yù)報(bào)研究[A];第31屆中國(guó)氣象學(xué)會(huì)年會(huì)S2 災(zāi)害天氣監(jiān)測(cè)、分析與預(yù)報(bào)[C];2014年

相關(guān)重要報(bào)紙文章 前2條

1 金牛理財(cái)網(wǎng)研究中心 蓋明鈺;前4月券商集合平均收益逾3%[N];中國(guó)證券報(bào);2013年

2 金牛理財(cái)網(wǎng) 蓋明鈺;前五月券商集合平均收益7%[N];中國(guó)證券報(bào);2013年

相關(guān)博士學(xué)位論文 前1條

1 李思佳;基于TIGGE的多中心集合平均預(yù)報(bào)理論與應(yīng)用研究[D];南京大學(xué);2016年

相關(guān)碩士學(xué)位論文 前1條

1 袁昊;大型礦用挖掘機(jī)載荷譜及統(tǒng)計(jì)動(dòng)力學(xué)分析[D];東北大學(xué);2014年

，

本文編號(hào)：1418828