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Journal of Tropical Meteorology 2014年第2期20卷 103-111页

作者：贾小龙陈丽娟罗京佳 National climate Center Laboratory for Climate Studies China Meteorological Administration Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science & Technology climate Variation Predictability and Applicability Research Program Research Institute for Global Change Japan Agency for Marine-Earth Science and TechnologyJapan

Based on an analysis of the relationship between the tropical cyclone genesis frequency and large-scale circulation anomaly in NCEP reanalysis, large-scale atmosphere circulation information forecast by the JAMSTEC SINTEX-F coupled model is used to build a statistical model to predict the cyclogenesis frequency over the South China Sea and the western North Pacific. The SINTEX-F coupled model has relatively good prediction skill for some circulation features associated with the cyclogenesis frequency including sea level pressure, wind vertical shear, Intertropical Convergence Zone and cross-equatorial air flows. Predictors derived from these large-scale circulations have good relationships with the cyclogenesis frequency over the South China Sea and the western North Pacific. A multivariate linear regression(MLR) model is further designed using these predictors. This model shows good prediction skill with the anomaly correlation coefficient reaching, based on the cross validation, 0.71 between the observed and predicted cyclogenesis frequency. However, it also shows relatively large prediction errors in extreme tropical cyclone years(1994 and 1998, for example).

关键词： CGCM large-scale circulation tropical cyclone climate prediction

Operational climate prediction in the Era of Big Data in China:Reviews and Prospects

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Journal of Meteorological Research 2016年第3期30卷 444-456页

作者：王昕宋连春王国复任宏利吴统文贾小龙吴焕萍吴捷 College of Computer Science and Technology Jilin University National climate Center China Meteorological Administration

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things.With the development of climate observing systems,particularly satellite meteorological observation and high-resolution climate models,and the rapid growth in the volume of climate data,climate prediction is now entering the era of big data.The application of big data will provide new ideas and methods for the continuous development of climate prediction.The rapid integration,cloud storage,cloud computing,and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively,thus predicting the future climate more accurately.This paper describes the application status of big data in operational climate prediction in China;it analyzes the key big data technologies,discusses the future development of climate prediction operations from the perspective of big data,speculates on the prospects for applying climatic big data in cloud computing and data assimilation,and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.

关键词： big data climate prediction operational development prediction method

Progress in climate prediction and Weather Forecast Operations in China

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Advances in Atmospheric Sciences 2012年第5期29卷 943-957页

作者：肖子牛刘波刘华张德 China Meteorological Administration Training Centre 46 Zhongguancun Nandajie National climate Centre 46 Zhongguancun Nandajie

The current status of weather forecasting and climate prediction, and the main progress China has made in recent years, are summarized in this paper. The characteristics and requirements of modern weather forecast operations are described briefly, and the significance of Numerical Weather prediction （NWP） for future development is emphasized. Tile objectives and critical tasks for seamless short-term climate predic- tion that covers the extended-range （15 30 days）, monthly, seasonal, annual, interannual and interdecadal timescales, are proposed.

关键词： weather forecast climate prediction operation in China

IMPROVEMENT OF OCEAN DATA ASSIMILATION SYSTEM AND climate prediction BY ASSIMILATING ARGO DATA

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Journal of Tropical Meteorology 2015年第2期21卷 171-184页

作者：李清泉张人禾刘益民 Laboratory for climate Studies National Climate Center China Meteorological Administration Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science & Technology State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences

The Argo(Array for Real-time Geostrophic Oceanography) data from 1998 to 2003 were used in the Beijing climate Center-Global Ocean Data Assimilation System(BCC-GODAS). The results show that the utilization of Argo global ocean data in BCC-GODAS brings about remarkable improvements in assimilation effects. The assimilated sea surface temperature(SST) of BCC-GODAS can well represent the climatological states of observational data. Comparison experiments based on a global coupled atmosphere-ocean general circulation model(AOCGM) were conducted for exploring the roles of ocean data assimilation system with or without Argo data in improving the climate predictability of rainfall in boreal summer. Firstly, the global ocean data assimilation system BCC-GODAS was used to obtain ocean assimilation data under the conditions with or without Argo data. Then, the global coupled atmosphere-ocean general circulation model(AOCGM) was utilized to do hindcast experiments with the two sets of the assimilation data as initial oceanic fields. The simulated results demonstrate that the seasonal predictability of rainfall in boreal summer, particularly in China, increases greatly when initial oceanic conditions with Argo data are utilized. The distribution of summer rainfall in China hindcast by the AOGCM under the condition when Argo data are used is more in accordance with observation than that when no Agro data are used. The area of positive correlation between hindcast and observation enlarges and the hindcast skill of rainfall over China in summer improves significantly when Argo data are used.

关键词： Argo data ocean data assimilation climate prediction AOGCM

On the climate prediction of nonlinear and non-stationary time series with the EMD method

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Chinese Physics B 2005年第3期14卷 628-633页

作者：万仕全封国林董文杰李建平高新全何文平 DepartmentofAtmosphericSciences NanjingUniversityofInformationScience&TechnologyNanjing210044China DepartmentofPhysics YangzhouUniversityYangzhou225009China//InstituteofAtmosphericPhysicsChineseAcademyofSciencesBeijing100029China NationalclimateCentreofChina Beijing100081China InstituteofAtmosphericPhysics ChineseAcademyofSciencesBeijing100029China CollegeofAtmosphericScience LanzhouUniversityLanzhou730000China DepartmentofPhysics YangzhouUniversityYangzhou225009China

At present, most of the statistical prediction models are built on the basis of the hypothesis that the time series or the observation data are linear and stationary. However, the observations are ordinarily nonlinear and non-stationary in nature, which are very difficult to be predicted by those models. Aiming at the nonlinearity/non-stationarity of the observation data, we introduce a new prediction scheme in this paper, in which firstly using the empirical mode decomposition the observations are stationarized and a variety of intrinsic mode functions (IMF) are obtained; secondly the IMFs are predicted by the mean generating function model separately; finally the predictions are used as new samples to fit and predict the original series. Research results show that the individual IMF, especially the eigen-IMF (namely eigen-hierarchy), has more stable predictability than the traditional methods. The scheme may effectively provide a new approach for the climate prediction.

关键词： empirical mode decompostion nonlinear/non-stationary time series hierarchy theory climate prediction

climate prediction and Diagnostic Analysis of Typhoon Precipitation in Midsummer of Yongzhou

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Meteorological and Environmental Research 2020年第5期11卷 152-156页

作者： Zhiyu LONG Wei FU Shen LIU Yuwen ZHOU Yongzhou Meteorological Bureau of Hunan Province Yongzhou 425000China

By consulting the typhoon yearbook and restoring the historical weather chart,technical separation of typhoon precipitation in Yongzhou from July to September of 1981-2015 was conducted.On this basis,climatic characteristics of typhoon precipitation in midsummer of Yongzhou were analyzed,and climate prediction and diagnostic analysis were carried out.The research results showed that typhoon precipitation was an important component of midsummer precipitation in Yongzhou,but its contribution to total precipitation was not as much as precipitation of the westerly belt system.When the ridge line of the western Pacific subtropical high was northward,typhoon precipitation was more than westerly precipitation in midsummer of Yongzhou;when the subtropical high was southward,there were more patterns of westerly precipitation year;when the subtropical high was normally northward,typhoon precipitation and westerly precipitation were less,with more dry years.In summer,abnormal cold sea surface temperature(SST)in tidal zone and warm pool zone of western Pacific and abnormal warm SST in NinoZ zone(strong El Nino event)were favorable for that the ridge line of the western Pacific subtropical high was southward,and there were more patterns of westerly precipitation year in midsummer of Yongzhou.On the contrary,when subtropical high was northward or normally northerly,there was less westerly precipitation.In non La Nina years when the subtropical high was northward,most of them were typhoon precipitation years.In La Nina years when the subtropical high was northward,most of them were dry years.

关键词： Typhoon precipitation climate prediction Diagnostic analysis

The prediction of Non-stationary climate Series Based on Empirical Mode Decomposition

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Advances in Atmospheric Sciences 2010年第4期27卷 845-854页

作者：杨培才王革丽卞建春周秀骥 Key Laboratory for Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Chinese Academy of Meteorological Sciences

This paper proposes a new approach which we refer to as ＂segregated prediction＂ to predict climate time series which are nonstationary. This approach is based on the empirical mode decomposition method （EMD）, which can decompose a time signal into a finite and usually small number of basic oscillatory components. To test the capabilities of this approach, some prediction experiments are carried out for several climate time series. The experimental results show that this approach can decompose the nonstationarity of the climate time series and segregate nonlinear interactions between the different mode components, which thereby is able to improve prediction accuracy of these original climate time series.

关键词： EMD nonstationarity nonlinear system climate prediction time series prediction

Hybrid Seasonal prediction of Meridional Temperature Gradient Associated with“Warm Arctic-Cold Eurasia”

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Advances in Atmospheric Sciences 2023年第9期40卷 1649-1661页

作者： Tianbao XU Zhicong YIN Xiaoqing MA Yanyan HUANG Huijun WANG Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing 210044China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519080China Nansen-Zhu International Research Centre Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

The meridional gradient of surface air temperature associated with“Warm Arctic–Cold Eurasia”(GradTAE)is closely related to climate anomalies and weather extremes in the mid-low latitudes.However,the climate Forecast System Version 2(CFSv2)shows poor capability for GradTAE prediction.Based on the year-to-year increment approach,analysis using a hybrid seasonal prediction model for GradTAE in winter(HMAE)is conducted with observed September sea ice over the Barents–Kara Sea,October sea surface temperature over the North Atlantic,September soil moisture in southern North America,and CFSv2 forecasted winter sea ice over the Baffin Bay,Davis Strait,and Labrador Sea.HMAE demonstrates good capability for predicting GradTAE with a significant correlation coefficient of 0.84,and the percentage of the same sign is 88%in cross-validation during 1983−2015.HMAE also maintains high accuracy and robustness during independent predictions of 2016−20.Meanwhile,HMAE can predict the GradTAE in 2021 well as an experiment of routine operation.Moreover,well-predicted GradTAE is useful in the prediction of the large-scale pattern of“Warm Arctic–Cold Eurasia”and has potential to enhance the skill of surface air temperature occurrences in the east of China.

关键词： warm Arctic-cold Eurasia year-to-year increment climate prediction sea ice SST

Analysis of Precipitation Anomaly and a Failed prediction During the Dragon-boat Rain Period in 2022

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Journal of Tropical Meteorology 2023年第1期29卷 115-127页

作者：董少柔杨崧刘尉胡娅敏汪明圣刘燕 Guangdong climate Center Guangzhou 510641 China School of Atmospheric Sciences and Guangdong Province Key Laboratory for climate Change and Natural Disaster Studies Sun Yat-sen UniversityZhuhaiGuangdong 519000 China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiGuangdong 519082 China Guangdong Meteorological Society Guangzhou 510080 China

This study investigates the possible causes for the precipitation of Guangdong during dragon-boat rain period(DBRP) in 2022 that is remarkably more than the climate state and reviews the successes and failures of the prediction in2022. Features of atmospheric circulation and sea surface temperature(SST) are analyzed based on several observational datasets for nearly 60 years from meteorological stations and the NCEP/NCAR Global Reanalysis Data. Results show that fluctuation of the 200-h Pa westerly wind as well as the westerly jet is strengthened due to the propagation of wave energy, leading to strong updraft over southern China. Activities of a subtropical high and a shear line provide favorable conditions for the transport of moisture to Guangdong. With the support of powerful southwest winds, extreme precipitation is induced. ENSO is a good indicator of atmospheric circulation at mid-and high-levels during the DBRP in2022 but it performs badly at low levels. During recent years, the influence of ENSO on precipitation during the DBRP has decreased obviously. The SSTA of tropical southeast Atlantic(SEA) in spring may become the key indicator. During the years with warm SEA, wave trains propagate from northwest to southeast over Eurasia with energy enhancing the westerly jet, conducive to updraft over southern China and the occurrence of heavy precipitation. Meanwhile, the Rossby wave is triggered over Maritime Continent by heat sources of southern Atlantic-western Indian Ocean through the Gill response. Thus, strong transport of moisture and heavy rainfall occur.

关键词： dragon-boat rain period(DBRP) precipitation ENSO climate prediction SSTA

Mathematical modeling of atmospheric internal waves phenomenon and its solution by Elzaki Adomian decomposition method

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Journal of Ocean Engineering and Science 2022年第3期7卷 203-212页

作者： Archana C.Varsoliwala Twinkle R.Singh Applied Mathematics and Humanities Department Sardar Vallabhbhai National Institute of TechnologySurat395007GujaratIndia

This article involves the study of atmospheric internal waves phenomenon,also referred to as gravity waves.This phenomenon occurs inside the fluid,not on the surface.The model is based on a shallow fluid hypothesis represented by a system of nonlinear partial differential equations.The basic assumption of the shallow flow model is that the horizontal size is much larger than the vertical size.Atmospheric internal waves can be perfectly represented by this model as the waves are spread over a large horizontal area.Here we used the Elzaki Adomian Decomposition Method(EADM)to obtain the solution for the considered model along with its convergence analysis.The Adomian decomposition method together with the Elzaki transform gives the solution in a convergent series without any linearization or perturbation.Comparisons are built between the results obtained by EADM and HAM to examine the accuracy of the proposed method.

关键词： Atmospheric internal waves Shallow-fluid equations System of nonlinear PDEs Elzaki Adomian decomposition method climate prediction