最优多因子动态配置的东北汛期降水相似动力预报试验

基于中国气象局国家气候中心季节预报业务模式27a(1983—2009年)预报结果和同期美国气候预报中心组合降水分析(CMAP)资料及国家气候中心气候系统诊断预报室74项环流指数和NOAA40个气候指数(1951—2009年),提出了客观定量化的最优多因子动态配置汛期降水相似-动力预测新技术,并对中国东北地区汛期降水进行了预报试验。利用历史资料有用信息估算模式预报误差原理,选取4个历史相似年对应模式误差来估算当前模式预报误差。通过单因子交叉检验距平相关系数确定主导因子及演化相似因子,结合当前及前期优化多因子组合配置确定预报因子集,最后利用历史相似年对应模式误差来估算当前模式预报误差并订正国家气候中心季节预报业务模式的预报结果,得到预报的汛期降水。对2005—2009年进行独立样本检验的结果表明,此技术对中国东北地区汛期降水有一定预报技巧。证实了利用历史资料估计业务模式预报误差的另类途径是可行的,显示了在业务预报应用中的潜在能力。     

数值预报中自由度的压缩及误差相似性规律

利用历史资料的有用信息提高数值模式预报水平是长期以来人们努力的目标。该文提出了一种在气候吸引子上缩小初始场自由度的相似选取方法,有效滤除了小尺度分量,避开了原有相似选取中自由度太大,相似选取困难的问题。分析表明:相似初值间的模式预报误差存在相似性,依此估计的预报误差与实际预报误差很接近。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。这为发展相似-动力方法,利用历史资料改进数值模式提供了支持。     

Analogue Correction Method of Errors by Combining Statistical and Dynamical Methods

Based on the atmospheric analogy principle, the inverse problem that the information of historical analogue data is utilized to estimate model errors is put forward and a method of analogue correction of errors (ACE) of model is developed in this paper. The ACE can combine effectively statistical and dynamical methods, and need not change the current numerical prediction models. The new method not only adequately utilizes dynamical achievements but also can reasonably absorb the information of a great many analogues in historical data in order to reduce model errors and improve forecast skill. Furthermore, the ACE may identify specific historical data for the solution of the inverse problem in terms of the particularity of current forecast. The qualitative analyses show that the ACE is theoretically equivalent to the principle of the previous analogue-dynamical model, but need not rebuild the complicated analogue-deviation model, so has better feasibility and operational foreground. Moreover, under the ideal situations, when numerical models or historical analogues are perfect, the forecast of the ACE would transform into the forecast of dynamical or statistical method, respectively.     

国家气候中心多模式解释应用集成预测

多模式集合和降尺度技术是提升模式预测能力的有效工具。该文对国家气候中心多模式解释应用集成预测 (MODES) 技术与业务应用现状进行了综合介绍。MODES采用欧洲中期天气预报中心、东京气候中心、美国国家环境预报中心和中国气象局国家气候中心4个气候业务季节预测模式输出场,利用EOF迭代、变形的典型相关分析、最优子集回归和高相关回归集成4种统计降尺度方法以及等权平均、经典超级集合等集成方法进行全国月及季节降水和气温预测。目前对MODES进行了夏季回报检验和约1年的实时业务应用。回报检验和业务应用表明,MODES对气温有较好的预测能力 (月预测平均PS评分为76),对降水有一定预测技巧 (月预测平均PS评分为68),具有短期气候预测业务应用价值。     

Multi-year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model (RegCM_NCC). Part Ⅰ: Sensitivity Study

A modified version of the NCAR/RegCM2 has been developed at the National Climate Center (NCC), China Meteorological Administration, through a series of sensitivity experiments and multi-year simulations and hindcasts, with a special emphasis on the adequate choice of physical parameterization schemes suitable for the East Asian monsoon climate. This regional climate model is nested with the NCC/IAP (Institute of Atmospheric Physics) T63 coupled GCM to make an experimental seasonal prediction for China and East Asia. The four-year (2001 to 2004) prediction results are encouraging. This paper is the first part of a two-part paper, and it mainly describes the sensitivity study of the physical process parameterization represented in the model. The systematic errors produced by the different physical parameterization schemes such as the land surface processes, convective precipitation, cloud-radiation transfer process, boundary layer process and large-scale terrain features have been identified based on multi-year and extreme flooding event simulations. A number of comparative experiments has shown that the mass flux scheme (MFS) and Betts-Miller scheme (BM) for convective precipitation, the LPMI (land surface process model I) and LPMII (land surface process model Ⅱ) for the land surface process, the CCM3 radiation transfer scheme for cloud-radiation transfer processes, the TKE (turbulent kinetic energy) scheme for the boundary layer processes and the topography treatment schemes for the Tibetan Plateau are suitable for simulations and prediction of the East Asia monsoon climate in rainy seasons. Based on the above sensitivity study, a modified version of the RegCM2 (RegCM_NCC) has been set up for climate simulations and seasonal predictions.     

季节预测模式对东亚夏季环流的预测能力及其对热带海洋的响应分析

东亚夏季环流变化对中国夏季降水的年际变化有重要影响,因此需要进一步理解季节预测模式对东亚夏季环流的预测能力。利用1991~2013年美国国家环境预测中心（NCEP）、中国气象局国家气候中心（NCC）和日本东京气候中心（TCC）的三个季节预测模式（CFS V2、BCC_CSM V2和MRI-CGCM）以及NCEP/NCAR再分析资料,定量评估了模式对东亚夏季风（EASM）和夏季西太平洋副热带高压（WPSH）强度的预测能力。在此基础上,分析了模式预测的EASM和WPSH对热带海温异常的响应能力,以及ENSO事件对EASM和WPSH预测的影响,阐述了预测误差产生的原因。结果表明:整体而言,三个模式对EASM和WPSH的预测技巧较高,但TCC模式对WPSH的预测技巧相对较低。三个模式预测的850 hPa风场在西北太平洋存在一个异常气旋,使得预测的EASM偏强和WPSH偏弱。同时,二者的年际变率整体比观测小。三个模式预测的EASM和WPSH对热带海洋海温异常的响应随季节演变特征与观测比较接近,但NCEP模式和TCC模式预测的EASM对前期热带太平洋和前期、同期热带印度洋的海温异常响应要强于观测,NCC模式预测的EASM对前期和同期的热带太平洋的海温异常响应明显比观测强。此外,三个模式预测的WPSH对前期和同期的热带太平洋、热带印度洋和热带大西洋的海温异常响应明显强于观测。三个模式预测的EASM和WPSH在ENSO年的平均绝对误差（MAE）整体而言要比正常年的小很多,NCEP模式和NCC模式预测的EASM和WPSH的MAE在La Ni?a年和El Ni?o年差别不大,而TCC模式预测的EASM和WPSH的MAE在El Ni?o年比在La Ni?a年大很多,表明ENSO事件是东亚夏季环流重要的可预报源。     

在动力相似预报中引入多个参考态的更新

针对如何更有效地利用历史资料中的相似信息提高预报水平的问题,在已有相似-动力模式研究基础上,进一步探讨了相似误差订正方法(ACE)的若干理论和技术问题,分析表明,ACE是对以相似离差方程和相似误差订正方程为理论依据的方法的再发展。在此基础上,提出了相似的更新问题和多个参考态的引入,并进而发展出一种考虑多参考态更新的动力相似预报新方法(MRSU)。这一方法通过引入相似更新周期的新概念,在预报进行到相似更新周期时重新选取多个参考态,并采用超平面近似法将相似-动力模式产生的多个预报估计成最佳预报向量,这样的“选取-估计”过程循环往复,从而完成整个时段的预报。Lorenz模式试验显示,相比于以往的相似-动力模式预报,MRSU能更有效减小预报误差,提高预报技巧,并且,ACE的理论优势应用前景也被初步证实。综合诸多研究结果,给出了MRSU的概念流程,这里针对复杂数值模式采用了ACE,能够等价实现相似-动力模式预报过程,无需重建模式,更易于推广。     

Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m)

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1.1(m). For the South and Southeast Asian summer monsoon, reasonable skill is found in the model's forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Niño3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and El Niño-Southern Oscillation.     

时空投影模型(STPM)的次季节至季节(S2S)预测应用进展

随着数值天气预报技术和季节动力预报系统的发展,短期天气预报及长期气候预测的能力持续提高,然而介于两者之间的次季节至季节(S2S,两周至三个月)预测技巧偏低,成为当今气象学界和业务服务的难题。南京信息工程大学国家特聘专家李天明教授团队于2012年研发了基于时空投影技术的统计预报模型(STPM),成功地对中国大陆降水和气温距平,以及区域极端降水、夏季高温、冬季低温和西太平洋台风群发事件等高影响天气进行提前10~30 d的预报,并在国家气候中心及多个省份开展了业务应用。STPM也成功应用于台湾春雨预报、南海季风爆发和ENSO预测等季节至年际变化的预测。本文对S2S预测的理论基础、STPM的发展和应用进行了完整的介绍,并讨论了S2S预测业务中所面临的挑战和未来展望。     

Multi-year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model （RegCM_NCC）. Part Ⅰ： Sensitivity Study

A modified version of the NCAR/RegCM2 has been developed at the National Climate Center （NCC）, China Meteorological Administration, through a series of sensitivity experiments and multi-year simulations and hindcasts, with a special emphasis on the adequate choice of physical parameterization schemes suitable for the East Asian monsoon climate. This regional climate model is nested with the NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM to make an experimental seasonal prediction for China and East Asia. The four-year （2001 to 2004） prediction results are encouraging. This paper is the first part of a two-part paper, and it mainly describes the sensitivity study of the physical process paraxneterization represented in the model. The systematic errors produced by the different physical parameterization schemes such as the land surface processes, convective precipitation, cloud-radiation transfer process, boundary layer process and large-scale terrain features have been identified based on multi-year and extreme flooding event simulations. A number of comparative experiments has shown that the mass flux scheme （MFS） and Betts-Miller scheme （BM） for convective precipitation, the LPMI （land surface process model I） and LPMII （land surface process model Ⅱ） for the land surface process, the CCM3 radiation transfer scheme for cloud-radiation transfer processes, the TKE （turbulent kinetic energy） scheme for the boundary layer processes and the topography treatment schemes for the Tibetan Plateau are suitable for simulations and prediction of the East Asia monsoon climate in rainy seasons. Based on the above sensitivity study, a modified version of the RegCM2 （RegCM_NCC） has been set up for climate simulations and seasonal predictions.     

Development of a Downscaling Method in China Regional Summer Precipitation Prediction

A downscaling method taking into account of precipitation regionalization is developed and used in the regional summer precipitation prediction (RSPP) in China. The downscaling is realized by utilizing the optimal subset regression based on the hindcast data of the Coupled Ocean-Atmosphere General Climate Model of National Climate Center (CGCM/NCC), the historical reanalysis data, and the observations. The data are detrended in order to remove the influence of the interannual variations on the selection of predictors for the RSPP. Optimal predictors are selected through calculation of anomaly correlation coe±cients (ACCs) twice to ensure that the high-skill areas of the CGCM/NCC are also those of observations, with the ACC value reaching the 0.05 significant level. One-year out cross-validation and independent sample tests indicate that the downscaling method is applicable in the prediction of summer precipitation anomaly across most of China with high and stable accuracy, and is much better than the direct CGCM/NCC prediction. The predictors used in the downscaling method for the RSPP are independent and have strong physical meanings, thus leading to the improvements in the prediction of regional precipitation anomalies.     

数值预报误差订正技术中相似-动力方法的发展

Due to the increasing requirement for high-level weather and climate forecasting accuracy, it is necessary to exploit a strategy for model error correction while developing numerical modeling and data assimilation techniques. This study classifies the correction strategies according to the types of forecast errors, and reviews recent studies on these correction strategies. Among others, the analogue-dynamical method has been developed in China, which combines statistical methods with the dynamical model, corrects model errors based on analogue information, and effectively utilizes historical data in dynamical forecasts. In this study, the fundamental principles and technical solutions of the analogue-dynamical method and associated development history for forecasts on different timescales are introduced. It is shown that this method can effectively improve medium- and extended-range forecasts, monthly-average circulation forecast, and short-term climate prediction. As an innovative technique independently developed in China, the analogue- dynamical method plays an important role in both weather forecast and climate prediction, and has potential applications in wider fields.     

统计-动力相结合的相似误差订正法

根据大气相似性原理,提出了利用历史资料的相似信息估计模式误差的反问题,并发展了一种相似误差订正(ACE)方法。该方法将统计和动力两种方法有机结合,在不改变现有数值预报模式的前提下,既充分利用了动力学发展的成就,又能够有效提取大量历史资料中的相似信息,达到减小模式误差、改进当前预报的目的。而且,ACE方法能够针对当前预报的特殊性来区分所利用过去资料的特殊性,提取历史相似信息间接求解反问题。定性分析表明,ACE方法与以往相似-动力模式原理是等价的,但无需重新建立复杂的相似离差预报模式,更具可行性和业务应用前景。在理想化的极限情形下,当数值模式或历史相似完全准确时,ACE方法的预报结果将分别蜕变为动力或统计学方法的预报结果。     

降尺度方法在中国不同区域夏季降水预测中的应用

在中国降水气候分区的基础上,利用降尺度方法进行区域夏季降水预测(RSPP),预测模型建立的基础是寻找影响区域气候的关键因子。降尺度预测模型中使用的资料有国家气候中心海-气耦合模式(CGCM/NCC)回报资料、NCEP/NCAR再分析资料和台站观测资料。为了避免年代际变化特征对季节尺度降水预测的影响,首先对CGCM/NCC模式输出资料、NCEP/NCAR再分析资料、区域平均降水资料去除年代际线性变化趋势,即去除所有预报因子场和预报对象场的长期变化趋势。然后分别计算预报对象和模式资料的预报因子场以及再分析资料的预报因子场的相关系数,把相关系数值同时达到0.05显著性检验水平的区域平均环流特征作为预测因子,保证挑选出的预测因子既能反映实际大气中预测因子与预报对象的关系,同时又是海-气耦合模式预测的高技巧信息。利用最优子集回归作为转换函数的降尺度方法建立区域夏季降水预测模型。交叉检验和独立样本检验结果表明,文中设计的区域夏季降水预测模型对中国大部分地区的夏季降水趋势预测的准确率较高且比较稳定,其预测效果远高于CGCM/NCC直接输出降水结果。进一步对具有较高预测技巧的代表性区域的可预报性来源分析发现,物理意义明确且独立性强的预测因子有助于提高预测准确率。     

动力延伸预报产品在广西月降水预报中的应用

利用1958—2005年NCEP/NCAR再分析资料和2003—2005年国家气候中心的动力延伸预报产品, 运用自然正交函数展开 (EOF) 求取预报关键区内的空间特征向量及其时间系数, 结合相似离度方法查找与预报月份相似的个例, 进而作出广西月降水量预报。独立样本试验证明, 利用动力延伸预报产品制作的区域月降水预报比利用前期实况高度距平场相关区域制作的预报效果更好。     

Improving long-lead seasonal forecasts of precipitation over Southern China based on statistical downscaling using BCC_CSM1.1m

Long-lead precipitation forecasts for 1–4 seasons ahead are usually difficult in dynamical climate models due to the model deficiencies and the limited persistence of initial signals. But, these forecasts could be empirically improved by statistical approaches. In this study, to improve the seasonal precipitation forecast over the southern China (SC), the statistical downscaling (SD) models are built by using the predictors of atmospheric circulation and sea surface temperature (SST) simulated by the Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). The different predictors involved in each SD model is selected based on both its close relationship with the target seasonal precipitation and its reasonable prediction skill in the BCC_CSM1.1 m. Cross and independent validations show the superior performance of the SD models, relative to the BCC_CSM1.1 m. The temporal correlation coefficient of SD models could reach > 0.4, exceeding the 95 % confidence level. The SC precipitation index can be much better forecasted by the SD models than by the BCC_CSM1.1 m in terms of the interannual variability. In addition, the errors of the precipitation forecast in all four seasons are significantly reduced over most of SC in the SD models. For the 2015/2016 strong El Niño event, the SD models outperform the dynamical BCC_CSM1.1 m model on the spatial and regional-average precipitation anomalies, mostly due to the effective SST predictor in the SD models and the weak response of the SC precipitation to El Niño-related SST anomalies in the BCC_CSM1.1 m.     

Application of the Analogue-Based Correction of Errors Method in ENSO Prediction

In this study, a method of analogue-based correction of errors（ACE） was introduced to improve El Ni？o-Southern Oscillation（ENSO） prediction produced by climate models. The ACE method is based on the hypothesis that the flow-dependent model prediction errors are to some degree similar under analogous historical climate states, and so the historical errors can be used to effectively reduce such flow-dependent errors. With this method, the unknown errors in current ENSO predictions can be empirically estimated by using the known prediction errors which are diagnosed by the same model based on historical analogue states. The authors first propose the basic idea for applying the ACE method to ENSO prediction and then establish an analogue-dynamical ENSO prediction system based on an operational climate prediction model. The authors present some experimental results which clearly show the possibility of correcting the flow-dependent errors in ENSO prediction, and thus the potential of applying the ACE method to operational ENSO prediction based on climate models.     

提高月预报业务水平的动力相似集合方法

针对基于大气环流模式的月预报问题,提出了一种能有效减小预报误差并提高预报技巧的动力相似集合预报新方法。该方法着眼于动力模式与统计经验的内在结合,在模式积分过程中通过提取大气环流历史相似性信息,对模式误差进行参数化处理,形成多个时变的相似强迫量来扰动生成预报的集合成员。将这一集合新方法应用到中国国家气候中心业务大气环流模式(BCC AGCM1.0),一组10 a准业务环境下回报试验结果显示,相比于业务集合预报,动力相似集合预报方法能有效改进模式对于大气环流的纬向平均、超长波和长波预报,从而有效提高了月平均环流预报技巧(几乎达到业务可用标准)和逐日环流预报技巧,并显著降低了预报误差,合理增加集合离散度,使二者配置关系得以改善,有望在业务预报中应用。     

东亚夏季风模式跨季预测的EOF-相似误差订正

利用国家气候中心第2代季节气候预测模式BCC_CSM1.1(m) 的1991—2010年每年2月起报的历史回算资料集,考察模式对于5个夏季风指数的预测能力,并通过发展基于经验正交函数分解与相似分析的EOF-相似误差订正方法,对5个夏季风指数的模式预测进行再修正。交叉检验和独立样本检验结果表明:该模式对1991—2010年东亚夏季风指数与西北太平洋夏季风指数预测技巧较高;EOF-相似误差订正方法适用于模式预测技巧较低的指数,这些指数经订正后预测效果均有不同程度改进,而预测技巧较高的夏季风指数经订正后改进效果不明显;在交叉检验中,线性部分订正多优于非线性部分订正效果,而对于独立试报的年份,非线性部分订正多优于线性部分订正效果,显示出良好的应用前景。     

Deficiencies and possibilities for long-lead coupled climate prediction of the Western North Pacific-East Asian summer monsoon

Long-lead prediction of waxing and waning of the Western North Pacific (WNP)-East Asian (EA) summer monsoon (WNP-EASM) precipitation is a major challenge in seasonal time-scale climate prediction. In this study, deficiencies and potential for predicting the WNP-EASM precipitation and circulation one or two seasons ahead were examined using retrospective forecast data for the 26-year period of 1981–2006 from two operational couple models which are the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the Bureau of Meteorology Research Center (BMRC) Predictive Ocean–Atmosphere Model for Australia (POAMA). While both coupled models have difficulty in predicting summer mean precipitation anomalies over the region of interest, even for a 0-month lead forecast, they are capable of predicting zonal wind anomalies at 850 hPa several months ahead and, consequently, satisfactorily predict summer monsoon circulation indices for the EA region (EASMI) and for the WNP region (WNPSMI). It should be noted that the two models’ multi-model ensemble (MME) reaches 0.40 of the correlation skill for the EASMI with a January initial condition and 0.75 for the WNPSMI with a February initial condition. Further analysis indicates that prediction reliability of the EASMI is related not only to the preceding El Niño and Southern Oscillation (ENSO) but also to simultaneous local SST variability. On other hand, better prediction of the WNPSMI is accompanied by a more realistic simulation of lead–lag relationship between the index and ENSO. It should also be noted that current coupled models have difficulty in capturing the interannual variability component of the WNP-EASM system which is not correlated with typical ENSO variability. To improve the long-lead seasonal prediction of the WNP-EASM precipitation, a statistical postprocessing was developed based on the multiple linear regression method. The method utilizes the MME prediction of the EASMI and WNPSMI as predictors. It is shown that the statistical postprocessing is able to improve forecast skill for the summer mean precipitation over most of the WNP-EASM region at all forecast leads. It is noteworthy that the MME prediction, after applying statistical postprocessing, shows the best anomaly pattern correlation skill for the EASM precipitation at a 4-month lead (February initial condition) and for the WNPSM precipitation at a 5-month lead (January initial condition), indicating its potential for improving long-lead prediction of the monsoon precipitation.