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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 *** 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 *** application of big data will provide new ideas and methods for the continuous development of climate *** 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 *** 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

Design and testing of a global climate prediction system based on a coupled climate model

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Science China Earth Sciences 2014年第8期57卷 2417-2427页

作者： MA JieHua WANG HuiJun climate Change Research Center Chinese Academy of Sciences Nansen-Zhu International Research Center Institute of Atmospheric Physics Chinese Academy of Sciences

A global climate prediction system （PCCSM4） was developed based on the Community climate System Model, version 4.0, developed by the National Center for Atmospheric Research （NCAR）, and an initialization scheme was designed by our group. Thirty-year （1981-2010） one-month-lead retrospective summer climate ensemble predictions were carded out and analyzed. The results showed that PCCSM4 can efficiently capture the main characteristics of JJA mean sea surface temperature （SST）, sea level pressure （SLP）, and precipitation. The prediction skill for SST is high, especially over the central and eastern Pacific where the influence of E1 Nino-Southem Oscillation （ENSO） is dominant. Temporal correlation coefficients between the pre- dicted Nino3.4 index and observed Nino3.4 index over the 30 years reach 0.7, exceeding the 99% statistical significance level. The prediction of 500-hPa geopotential height, 850-hPa zonal wind and SLP shows greater skill than for precipitation. Overall, the predictability in PCCSM4 is much higher in the tropics than in global terms, or over East Asia. Furthermore, PCCSM4 can simulate the summer climate in typical ENSO years and the interannual variability of the Asian summer monsoon well. These preliminary results suggest that PCCSM4 can be applied to real-time prediction after further testing and improvement.

关键词： climate model climate prediction ENSO monsoon

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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 decomposition 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 *** this basis,climatic characteristics of typhoon precipitation in midsummer of Yongzhou were analyzed,and climate prediction and diagnostic analysis were carried *** 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 *** 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 *** 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 *** the contrary,when subtropical high was northward or normally northerly,there was less westerly *** non La Nina years when the subtropical high was northward,most of them were typhoon precipitation *** La Nina years when the subtropical high was northward,most of them were dry years.

关键词： Typhoon precipitation climate prediction Diagnostic analysis

Recent Researches on the Short-Term climate prediction at IAP-A Brief Review

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Advances in Atmospheric Sciences 2001年第5期18卷 929-936页

作者：王会军周广庆林朝晖赵彦郭裕福马柱国 LASG Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China

Studies on the seasonal to extraseasonal climate prediction at the Institute of Atmospheric Physics (IAP) in recent years were reviewed. The first short-term climate prediction experiment was carried out based on the atmospheric general circulation model (AGCM) coupled to a tropical Pacific oceanic general circulation model (OGCM) In 1997, an ENSO prediction system including an oceanic initialization scheme was set up. At the same time, researches on the SST-induced climate predictability over East Asia were made. Based on the blennial signal in the interannual climate variability, an effective method was proposed for correcting the model predicted results recently In order to consider the impacts of the initial soil mois- ture anomalies, an empirical scheme was designed to compute the soil moisture by use of the atmospheric quantities like temperature, precipitation, and so on. Sets of prediction experiments were carried out to study the impacts of SST and the initial atmospheric conditinns on the flood occurring over China in 1998.

关键词： climate prediction climate model Initialization

climate Precipitation prediction by Neural Network

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Journal of Mathematics and System Science 2015年第5期5卷 207-213页

作者： Juliana Aparecida Anochi Haroldo Fraga de Campos Velho Applied Computing Graduate Program National Institute for Space Research (INPE) Brazil. Associated Laboratory for Computing and Applied Mathematics National Institute for Space Research (INPE).

In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.

关键词： climate prediction Neural Networks Rough Sets Theory

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