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1.
The ensemble Kalman filter (EnKF), as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23–30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error me... 相似文献
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基于TIGGE资料集中的ECMWF、CMA和JMA的数值预报产品,利用加权集成、回归集成和消除偏差集成等线性集成方式与遗传算法优化的BP神经网络(GABP)集成,对我国大部开展地面2 m温度在24 h、48 h和72 h预报时效的多模式集成预报试验。通过对2013年1—6月的预报检验,结果表明:GABP集成预报效果有较大提升,均方误差明显小于各单一模式预报。GABP集成的误差分布在新疆和华北均方误差较大,但是在预报效果改进上GABP集成在西部地区相对单一模式的误差减小更加明显。在进行几种多模式集成方式时,GABP集成相比线性方法预报结果更加精准。对于天气过程个例的预报,GABP集成预报出预报量的变化趋势,预报效果优于单一模式和线性集成预报。无论是较长时间段还是短时间的天气过程,在改进预报效果上GABP集成都起到了最佳的作用。 相似文献
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经过近60年的发展,我国短期气候预测技术和方法也有了长足进步。近年来,一些新的预报技术和机理认识不断应用于短期气候预测业务。ARGO海洋观测资料的使用大大提高了业务模式的预测技巧,新一代气候预测模式系统已经投入准业务化运行,研发了多种模式降尺度释用技术,多模式气候预测产品解释应用集成系统(MODES)和动力-统计结合的季节预测系统(FODAS)逐渐应用于业务中,大气季节内振荡(MJO)逐步在延伸期预报中得到应用。近年来,对全球海洋、北极海冰、欧亚积雪、南半球环流系统对东亚季风影响的新认识也不断引入到短期气候预测业务中。这些新技术和新认识的应用极大提高了我国短期气候预测的业务能力。 相似文献
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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. 相似文献
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大气环境数值模拟研究新进展 总被引：14，自引：1，他引：13
近五年来,中国科学院大气物理研究所(简称大气所)在大气环境数值模拟方面取得了丰硕的成果,通过自主发展和引进,建立了完备的多尺度、多成分的大气环境数值模式,包括全球大气化学输送模式、区域和城市空气质量预报模式。大气所利用这些模式研究各种空间尺度上污染物浓度时空分布以及污染物的输送和演变,研究了多种污染过程的成因和污染变化规律,在污染物输送、低对流层臭氧高污染、区域及城市污染等方面取得了很多成果,并对区域或城市空气质量进行业务化实时预报。大气所还拓展了我国大气环境模拟研究的新领域:大气化学资料同化、污染模式集合预报、污染源反演新方法。初步建立了空气质量模式的资料同化系统(分别基于最优插值技术和集合卡曼滤波技术)和多模式集合预报体系,提高了模式预报水平;在污染源反演新方法方面进行了初步的探索。结合我国目前仍然面临着的大气环境问题,对今后大气环境数值模式的发展方向进行了展望。 相似文献
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中国智能网格天气预报已初步建立0—30 d涵盖基本气象要素的无缝隙气象预报业务体系。近年深度学习技术兴起,给不同领域带来前所未有的变革。同样,深度学习的非线性映射能力、海量信息提取能力、时空建模能力等优势为进一步提升智能网格预报的准确性和精细化水平提供了新的思路和方法。越来越多的研究将深度学习技术应用于智能网格预报的各个方面,包括数值预报订正和解释应用、集合天气预报、相似集合、统计降尺度、纯数据驱动的预报模型和极端天气预报等,并展示出良好的应用潜力。然而,目前深度学习技术在天气预报领域的应用仍处于起步阶段,将其引入智能网格预报业务体系还面临诸多挑战,主要包括算法的选择、算法的数据基础、多源数据融合以及模型的可解释性、可信度、可用性和工程化等。通过回顾近年来深度学习技术在智能网格预报中的应用进展和前景,同时对面临的挑战与应对进行探讨,将有利于促进深度学习技术在天气客观预报领域更好、更稳定的发展。 相似文献
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Near real-time ocean circulation assimilation and prediction in the Intra-Americas Sea with ROMS 总被引：2，自引：0，他引：2
B.S. Powell A.M. Moore H.G. Arango E. Di Lorenzo R.F. Milliff R.R. Leben 《Dynamics of Atmospheres and Oceans》2009,48(1-3):46
We present the feasibility of a prototype, near real-time assimilation and ensemble prediction system for the Intra-Americas Sea run autonomously aboard a ship of opportunity based on the Regional Ocean Modeling System (ROMS). Predicting an ocean state depends upon numerical models that contain uncertainties in their modeled physics, initial conditions, and model state. An advanced model, four-dimensional variational assimilation, and ensemble forecasting techniques are used to account for each of these uncertainties. Every 3 days, data from the previous 7 days period were assimilated to generate an estimate of the circulation and to create an ensemble of 2 weeks forecasts of the ocean state. This paper presents the methods and results for a multi-resolution assimilation system and ensemble forecasts of surface fields and dominant surface circulation features. When compared to post-processed science quality observations, the state estimates suffer from our reliance on real-time, quick-look satellite observations of the ocean surface. Despite a number of issues, the ensemble forecast estimate is often superior to observational persistence. This proof-of-concept prototype performed well enough to reveal deficiencies, provide useful insights, valuable lessons, and guidance for future improvements in real-time ocean forecasting. 相似文献
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《Dynamics of Atmospheres and Oceans》2010,49(1-3):46-68
We present the feasibility of a prototype, near real-time assimilation and ensemble prediction system for the Intra-Americas Sea run autonomously aboard a ship of opportunity based on the Regional Ocean Modeling System (ROMS). Predicting an ocean state depends upon numerical models that contain uncertainties in their modeled physics, initial conditions, and model state. An advanced model, four-dimensional variational assimilation, and ensemble forecasting techniques are used to account for each of these uncertainties. Every 3 days, data from the previous 7 days period were assimilated to generate an estimate of the circulation and to create an ensemble of 2 weeks forecasts of the ocean state. This paper presents the methods and results for a multi-resolution assimilation system and ensemble forecasts of surface fields and dominant surface circulation features. When compared to post-processed science quality observations, the state estimates suffer from our reliance on real-time, quick-look satellite observations of the ocean surface. Despite a number of issues, the ensemble forecast estimate is often superior to observational persistence. This proof-of-concept prototype performed well enough to reveal deficiencies, provide useful insights, valuable lessons, and guidance for future improvements in real-time ocean forecasting. 相似文献
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通过设计3组不同的观测误差均方差,对2012年8月1日—29日进行了基于GRAPES-M EPS(Global/Regional Assimilation and Prediction System-Mesoscale Ensemble Prediction System)的集合预报敏感性试验,研究观测误差均方差对集合预报初始扰动场结构、扰动量及垂直扰动总能量发展的影响,评估集合预报结果的差异,并分析了一次典型的江淮流域强降水个例。结果显示,模式变量扰动结构和扰动振幅对观测误差均方差较敏感,较小的观测误差均方差使得温度和风等模式变量的初始扰动量增大,扰动总能量增长更快,降水集合预报效果更优。因此在GRAPES-MEPS中,可以考虑对观测误差均方差进行适当的扰动,以体现观测误差均方差的不确定性对集合预报的影响,提高GRAPES-MEPS的集合预报技巧。 相似文献
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大气污染资料同化与应用综述 总被引：1，自引：0，他引：1
我国正面临以高浓度臭氧和细颗粒物为典型特征的大气复合污染问题,对其进行模拟和预报是有效应对大气污染的关键。大气复合污染预报的不确定性来源复杂,同时存在化学非线性的影响,各种模式输入不确定性对模拟预报影响的时空差异较大,从而导致很多不确定性约束方法难以确定关键的不确定性因子而进行有针对性的约束和订正。利用资料同化方法融合模式、多源观测等信息,减小模式输入数据的不确定性成为提升大气污染模拟预报精度的关键。本文将简要介绍大气污染资料同化相关的模式不确定性、同化算法以及污染物浓度场同化、源反演研究上的进展,探讨大气污染资料同化面临的主要挑战和发展趋势。 相似文献
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该文总结回顾了中央气象台近年来的天气预报业务技术进展。天气预报质量的历史演变显示了预报业务水平的提高, 这种业务能力的提高既反映了预报技术的发展, 也带来了天气预报业务的变化。对业务天气预报中各种预报技术应用进展的分析表明:数值预报在天气预报业务能力提高中发挥着重要的基础性作用; 同时, 基于对不同尺度天气影响系统发展演变过程深入认识的基础上, 天气学的预报方法依然是预报业务中的重要技术方法; 动力诊断预报已成为灾害性天气预报中的重要手段之一, 数值预报产品的解释应用是实现气象要素精细定量预报的技术途径。 相似文献
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综合论述了近年来已在国际上引起高度重视的新一代动力随机预报方法 ——— 集合预报。 随着计算机技术的迅猛发展和由于大气初值和数值模式中物理过程存在着不确定性的事实, 这一方法无疑代表了数值天气预报未来演变发展的方向。 未来的天气预报产品预计将从“决定论”的预报转变为“随机论”的预报来正确地表达气象科学中这一所谓“可预报性问题”, 以便更好地为用户服务。 文中扼要地叙述了集合预报的概念、基本问题及其最新的研究动态和发展, 包括(1)如何建立和评估一个集合预报系统;(2)如何正确地表征大气初值和模式物理过程的不确定性与随机性;(3)如何从集合预报中提炼有用的预报信息和合理地解释、检验集合预报的产品, 特别是概率预报。 除了直接在天气预报上的应用, 还提到集合预报在气象观测和资料同化方面应用的动态, 以引起有关研究人员的注意。 相似文献
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An Ensemble-Based Three-Dimensional Variational Assimilation Method for Land Data Assimilation 总被引：1，自引：0，他引：1 
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下载免费PDF全文 Land surface models are often highly nonlinear with model physics that contain parameterized discontinuities. These model attributes severely limit the application of advanced variational data assimilation methods into land data assimilation. The ensemble Kalman filter （EnKF） has been widely employed for land data assimilation because of its simple conceptual formulation and relative ease of implementation. An updated ensemble-based three-dimensional variational assimilation （En3-DVar） method is proposed for land data assimilation This new method incorporates Monte Carlo sampling strategies into the 3-D variational data assimilation framework. The proper orthogonal decomposition （POD） technique is used to efficiently approximate a forecast ensemble produced by the Monte Carlo method in a 3-D space that uses a set of base vectors that span the ensemble. The data assimilation process is thus significantly simplified. Our assimilation experiments indicate that this new En3-DVar method considerably outperforms the EnKF method by increasing assimilation precision. Furthermore, computational costs for the new En3-DVar method are much lower than for the EnKF method. 相似文献
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Reducing the prediction uncertainties of high-impact weather and climate events: An overview of studies at LASG 下载免费PDF全文
下载免费PDF全文 This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to reduce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limitations of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear optimal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the El Niño–Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reducedprojection- based four-dimensional variational data assimilation (DRP-4DVar) approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction, which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors (NLLVs) to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs represent a candidate for possible development as an ensemble method in operational forecasts. Despite the considerable efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events. 相似文献
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中国数值天气预报的自主创新发展 总被引：1，自引：0，他引：1
数值天气预报是天气预报业务和防灾、减灾的核心科技。中国数值天气预报研究和业务应用一直受到高度重视,在理论、方法和数值模式研究方面取得了有广泛国际影响的研究成果。在回顾新中国数值天气预报自主创新研究成果的基础上,重点对GRAPES（Global Regional Assimilation and PrEdiction System）半隐式半拉格朗日格点模式与物理过程的研发和业务应用的状况以及所取得的重要科学进展进行了综述。近年来,通过自主研发建立了中国数值天气预报业务体系—GRAPES体系。首次以自主技术实现了从区域3—10 km到全球25—50 km分辨率的确定性预报和集合预报系统,并在模式动力框架、四维变分同化和卫星资料同化技术等方面有所突破,建立了大气化学数值天气预报、台风数值预报和海浪预报等系统。自主研发的数值天气预报体系的建立是长期坚持既定科学技术方向以及研究和业务紧密结合、经验不断积累的结果,是中国自主发展数值天气预报技术的重要起点。 相似文献
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降水数值预报有很大的不确定性,与降水预报密切相关的物理过程参数化方案中关键参数的不确定性是降水数值预报误差来源之一,对这些参数引入随机扰动的随机参数扰动方法（Stochastically Perturbed Parameterization,简称SPP方法）可以代表模式降水预报的不确定性,是国际集合预报前沿研究领域。为了认识该方法能否代表中国冬季降水数值预报的不确定性,为业务应用提供科学依据,基于中国气象局中尺度区域集合预报模式（Global/Regional Assimilation and Prediction System-Regional Ensemble Prediciton System,简称GRAPES-REPS）,从对模式降水预报不确定性有较大影响的积云对流、云微物理、边界层及近地面层等四个参数化方案中选取了16个与降水密切相关的关键参数,引入了随机参数扰动方法,并通过2018年12月12日至2019年1月12日总计31天的冬季集合预报试验,对比分析了SPP方法对等压面要素及降水的集合预报效果。结果显示:在冬季应用SPP方法时,等压面要素的概率预报技巧总体来说优于无SPP方法扰动的对比试验,且对于低层、近地面要素的改进效果优于对中高层等压面要素的改进;但对降水概率预报而言,尽管检验评分数值略优于对比预报试验,但并未通过显著性检验,这表明,在东亚冬季风影响下,随机参数扰动方法对中国冬季降水概率预报技巧没有明显的改进。究其原因,可能是由于SPP方法主要代表对流性降水预报的不确定性,而中国冬季降水过程主要与斜压不稳定发生发展有关,模式降水以大尺度格点降水为主,对流性降水较少,故对冬季降水预报改进不明显,这为业务集合预报模式中应用随机参数扰动方法提供了科学依据。 相似文献
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Song-You Hong Young Cheol Kwon Tae-Hun Kim Jung-Eun Esther Kim Suk-Jin Choi In-Hyuk Kwon Junghan Kim Eun-Hee Lee Rae-Seol Park Dong-Il Kim 《Asia-Pacific Journal of Atmospheric Sciences》2018,54(1):267-292
The Korea Institute of Atmospheric Prediction Systems (KIAPS) began a national project to develop a new global atmospheric model system in 2011. The ultimate goal of this 9-year project is to replace the current operational model at the Korea Meteorological Administration (KMA), which was adopted from the United Kingdom’s Meteorological Office’s unified model (UM) in 2010. The 12-km Korean Integrated Model (KIM) system, consisting of a spectral-element non-hydrostatic dynamical core on a cubed sphere grid and a state-of-the-art physics parameterization package, has been launched in a real-time forecast framework, with initial conditions obtained via the advanced hybrid four-dimensional ensemble variational data assimilation (4DEnVar) over its native grid. A development strategy for KIM and the evolution of its performance in medium-range forecasts toward a world-class global forecast system are described. Outstanding issues in KIM 3.1 as of February 2018 are discussed, along with a future plan for operational deployment in 2020. 相似文献
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To support short-range weather forecast, a high-resolution model (1km) is developed and technicallyupgraded in the South China Regional Center, including the improvement of the 3D reference scheme and predictor-corrector method for Semi-Implicit and Semi-Lagrangian (SISL) in model dynamical core, as well as the improvementof physical parameterization. Furthermore, the multi-process parallel I/O and parallel nudging techniques are developedand have facilitated rapid updating in the assimilation prediction system and fast-output post processing process. Theexperimental results show that the improved 3D reference scheme and upgraded physic schemes can effectively improvethe prediction accuracy and stability with a longer integration time step. The batch test shows that the precipitationforecast performance of 1-km model is significantly better than that of 3-km model. The 1-km model is in operation withrapidly updating cycle at 12-minute intervals, which can be applied to short-range forecast and nowcasting application. 相似文献