【作者】 王皓；

【导师】 黄建平；

【作者基本信息】 兰州大学 ， 大气物理学与大气环境， 2015， 硕士

【摘要】 月尺度的气候预测在众多国内外学者的不懈努力下,取得了长足的发展,但是其预测误差仍旧是客观存在的。尤其是在当前气候变化的大背景下,气象中的极端事件发生频次越来越高,月尺度的气候预测对于气象灾害的提前预知,减少其带来的损失有着至关重要的意义。这从客观上提高了国家对月尺度气候预测准确率的要求,但是由于其时间尺度超越了短期逐日天气预报可预报性的上限,且同时受到初值和边值问题的影响,其目前的预报精度还远远不能满足国家和社会的需求,因此探索和发展针对其有效订正策略的研究具有重要的应用价值。本文旨在探索如何将已有的误差订正技术合理运用于业务运行模式中,以期提高其预报准确率,同时也为模式预报结果的释用提供有效信息。鉴于此,本文基于已经业务运行的国家气候中心第二代月动力延伸预测系统(DERF2.0)中大气环流模式(BCC_AGCM)的回报资料和各种再分析资料,首先分析了该模式对冬季气温预测的误差特征及其与外强迫的联系；进而围绕模式夏季降水的回报结果,开展了一系列数值试验,探索了针对其误差订正的有效策略和方案,取得了较好的效果。本文主要结论如下：(1)模式能够在整体上较好地反映出欧亚区域冬季气温的变化趋势,抓住东亚冬季风区气温年际变化的主要空间模态,对东亚冬季风区冬季气温具有一定的预报能力。预测误差在陆地大于海洋,高纬地区大于低纬地区,同时与海拔高度也有密切关系。预测误差的主要模态与一些关键区域的海温和海冰存在显著的相关性,表明模式对外强迫异常的响应能力存在缺陷。这为结合模式对关键区海温和海冰异常的响应能力,有针对性地改进模式对东亚冬季风区冬季气温的预测能力提供了依据。(2)模式夏季降水误差主要是由北半球大尺度环流及海温异常造成的。根据关键因子与模式预测误差的高相关性,建立针对误差相关模态进行订正的模型。结果表明,超过60%的年份订正后,其ACC都较原始结果及系统订正有明显的改进。其中2003年-2011年平均的ACC评分超过0.15,而原始结果仅为0.03,系统订正后仅为0.07,而MSSS评分也在绝大多数地区为正值,表明订正方案能在一定程度上减小由于模式对关键因子响应不足造成的误差。同时,通过提取与模式预测误差不同模态的主分量显著相关的前期环流及气候因子,可以一定程度上揭示模式的预报性能对哪些因子较为敏感,这也为模式夏季降水预报策略的研究和模式预报结果的释用提供了一些新思路和有用的信息。(3)在基于EOF订正方案的制定中,通过交叉验证,对观测和预报场分别取不同的模态数参与回归建模方法的订正效果进行了考察。结果表明,并不是利用相同的模态进行回归建模时的订正技巧最佳；此外,订正技巧主要随着预报模态个数的变化而变化,而受到观测模态个数的影响较小；引入模式预报场的高阶模态会使得订正技巧大幅下降。通过试验,综合ACC及其标准差的大小,基于EOF的订正方案最终采用30个成员集合平均,这样的做法克服了单个成员预报技巧的不稳定。(4)通过基于SVD订正方案模态个数随订正技巧变化的数值试验发现,利用前6个模态进行订正可使得效果达到最优,当订正效果达到最优后,伴随累积模态个数的增加,ACC则出现迅速的回落,这也说明高阶模态可能提供了虚假的信号,引入其进行订正会带来不利的影响。(5)基于EOF和SVD的方案都能显著的提高模式夏季降水的预报技巧。从ACC的角度来看,EOF方案ACC评分均值略高于SVD方案,其稳定性也略优于SVD方案；从MSSS的角度而言,EOF方案的MSSS评分正值的区域比SVD更大,其量值也略高；另外,二者对于模式原始预报平均误差量值的减小效果十分显著。总体来说,综合各项检验标准,EOF方案订正效果略优于SVD方案,同时,由于EOF方案采用了集合平均的结果,其订正效果的稳定性也略优于SVD方法。更多还原

【Abstract】 Monthly extended range forecasting has made great progresses with the unremitting efforts of many meteorological researchers, but there still exist prediction errors; especially, the increasing frequency of extreme climate events under the background of present climate change requires a higher accuracy of the monthly extended range forecast. The precision of monthly extended range forecasting is still far from meeting the demands of the country and society for the reason that the time-scales of month-scale climate phenomena exceed the upper limit of the predictability of daily weather forecast as well as the impacts of initial/boundary conditions on the prediction model. Therefore, the further investigation and development of the efficiency correction strategies and methods are necessary and have great potential value of application.We explore how to efficiently apply the existing error correction skill to the operating models, in order to improve the accuracy of weather prediction and also provide some useful information for better interpretation and application of the model forecast products. We examine the characteristics of winter temperature forecast error from this model and the relationship between this error and external forcing, by using the National Climate Center Atmospheric General Circulation Model (BCC_AGCM) from the second generation monthly Dynamic Extended Range Forecast System (DERF2.0) and some other reanalysis datasets in this paper. Moreover, a series of numerical model simulations based on the re-prediction of summer precipitation are developed to explore the efficient strategy and scheme for using the error correction method. The results are listed as following:1. The model can simulate the trend of Eurasian winter temperature as a whole and present the principal spatial modes of the inter-annual variability of wintertime temperature. Overall, the model has considerate forecast ability for wintertime temperature over the East Asian winter monsoon regions. The spatial distribution and temporal evolution of the forecast error indicate that the errors over land and over high latitude region are larger than those over ocean and over low latitude region, respectively, and the error is correlated to altitude. The principal modes of model forecast error are highly correlated to the sea surface temperature (SST) over some key ocean regions, which indicates the model lacks ability to respond to the external anomaly. These results can provide the basis for improving model forecast ability for wintertime temperature over the East Asian winter monsoon regions with the model response to SST and sea ice anomaly over key ocean regions.2. The model error of summer precipitation is mainly caused by the large scale circulation in the Northern Hemisphere and SST anomalies. We build a regression model based on the high correlation between the key factors and model forecast error to correct model errors. The results show more than 60% of total years have noteworthy improvement on anomaly correlation coefficient (ACC) against raw and systematic corrected results. From 2003 to 2011, the raw average model ACC is only 0.03 and systemic corrected ACC is 0.07, whereas error corrected ACC exceeds 0.15. The mean square skill scores (MSSS) score after error correction is positive over most of the regions, indicating the error correction scheme can somehow reduce the error due to the lack of model response to the external forcing. The previous circulation and climate factors, which are highly correlated to the principal components from different model error modes, can reveal the sensitivity of model forecast ability to those factors in a certain extent. This will provide some useful ideas to further improve summertime precipitation forecast and application of model forecast products.3. To obtain the error correction scheme based on empirical orthogonal function (EOF), we compare the correction effects of using different number of modes on observation and forecast fields in the regression models by cross validation method. The results indicate that it’s not best correction scheme when observation and forecast fields have same number of modes. Meanwhile, the correction efficiency changes mainly with the number of modes of forecast fields, relatively less influenced by the number of modes of observation modes. When introducing the high order modes of forecast field, the correction efficiency decreases rapidly. Considering both ACC and variance, the best error correction scheme based on EOF is to use 30 modes as a cluster, which will overcome the forecast instability from certain mode.4. The test on the error correction scheme based on singular value decomposition (SVD) shows that correction efficiency is the best when using the first six modes in the correction. Thereafter, ACC will decreases rapidly with increasing modes, which indicates the high order modes may provide fake signal that can have bad influence on the correction efficiency.5. The error correction schemes based on EOF and SVD can both remarkably improve model forecast efficiency on summer precipitation. EOF scheme has relatively higher ACC score and is more stable than SVD scheme, and has larger positive region and higher value on MSSS score than SVD scheme as well. In addition, these two schemes both have significant effect on reducing original model forecast error. Overall, EOF scheme is relatively better than SVD scheme. EOF scheme is also more stable than SVD scheme by using the mean value of mode cluster.更多还原