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Monthly Extended Predicting Experiments with Nonlinear Regional Prediction.PartⅡ:Improvement of Wave Component Prediction 
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下载免费PDF全文 Based on Chen et al. (2006), the scheme of the combination of the pentad-mean zonal height departure nonlinear prediction with the T42L9 model prediction was designed, in which the pentad zonal heights at all the 12-initial-value-input isobar levels from 50 hPa to 1000 hPa except 200, 300, 500, and 700 hPa were derived from nonlinear forecasts of the four levels by means of a good correlation between neighboring levels. Then the above pentad zonal heights at 12 isobar-levels were transformed to the spectrum coefficients of the temperature at each integration step of T42L9 model. At last, the nudging was made. On account of a variety of error accumulation, the pentad zonal components of the monthly height at isobar levels output by T42L9 model were replaced by the corresponding nonlinear results once more when integration was over. Multiple case experiments showed that such combination of two kinds of prediction made an improvement in the wave component as a result of wave-flow nonlinear interaction while reducing the systematical forecast errors. Namely the monthly-mean height anomaly correlation coefficients over the high- and mid-latitudes of the Northern Hemisphere, over the Southern Hemisphere and over the globe increased respectively from 0.249 to 0.347, from 0.286 to 0.387, and from 0.343 to 0.414 (relative changes of 31.5%, 41.0%, and 18.3%). The monthly-mean root-mean-square error (RMSE) of T42L9 model over the three areas was considerably decreased, the relative change over the globe reached 44.2%. The monthly-mean anomaly correlation coefficients of wave 4-9 over the areas were up to 0.392, 0.200, and 0.295, with the relative change of 53.8%, 94.1%, and 61.2%, and correspondingly their RMSEs were decreased respectively with the rate of 8.5%, 6.3%, and 8.1%. At the same time the monthly-mean pattern of parts of cases were presented better. 相似文献
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Monthly Extended Predicting Experiments with Nonlinear Regional Prediction.PartⅠ:Prediction of Zonal Mean Flow 下载免费PDF全文
下载免费PDF全文 Systematic errors have recently been founded to be distinct in the zonal mean component forecasts, which account for a large portion of the total monthly-mean forecast errors. To overcome the difficulty of numerical model, the monthly pentad-riean nonlinear dynamic regional prediction models of the zonal mean geopotential height at 200, 300, 500, and 700 hPa based on a large number of historical data (NCEP/NCAR reanalysis data) were constituted by employing the local approximation of the phase space reconstruction theory and nonlinear spatio-temporal series prediction method. The 12-month forecast experiments of 1996 indicated that the results of the nonlinear model are better than those of the persistent, climatic prediction, and T42L9 model either over the high- and mid-latitude areas of the Northern and Southern Hemispheres or the tropical area. The root-mean-square of the monthly-mean height of T42L9 model was considerably decreased with a change of 30.4%, 26.6%, 82.6%, and 39.4%, respectively, over the high- and mid-latitudes of the Northern Hemisphere, over the high- and mid-latitudes of the Southern Hemisphere, over the tropics and over the globe, and also the corresponding anomaly correlation coefficients over the four areas were respectively increased by 0.306-0.312, 0.304-0.429, 0.739-0.746, and 0.360-0.400 (averagely a relative change of 11.0% over the globe) by nonlinear correction after integration, implying that the forecasts given by nonlinear model include more useful information than those of T42L9 model. 相似文献
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Hong-Li REN Qing BAO Chenguang ZHOU Jie WU Li GAO Lin WANG Jieru MA Yao TANG Yangke LIU Yujun WANG Zuosen ZHAO 《大气科学进展》2023,(8):1501-1520
Seamless prediction is a weather–climate integrated prediction covering multiple time scales that include days, weeks,months, seasons, years, and decades. Seamless prediction can provide different industries with information such as weather conditions and climate variations from the next few days to years, which have important impacts on economic and social development and important reference value for short-, medium-and long-term decision-making and planning of the country.Therefore, seamless p... 相似文献
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A six level regional primitive equation model has been formulated and tested for monsoon prediction. The model uses dynamic normal mode initialization scheme for obtaining initial balance. The physical processes included are: the large scale condensation, the Kuo type of cumulus convection, the surface friction, the sensible heat supply and evaporation over the sea. The actual smooth orography is included. The model has been integrated for 48 hrs using input of 7 July and 8 August 1979 when the domain of integration was dominated by an intense monsoon depression. In order to investigate the model simulation of formative stage of the depression, the model was also integrated using input of 4 July 1979.Furthermore, the envelope orography has been constructed and included in the model for investigating its effects on the monsoon prediction. Results of the model forecast are presented and discussed. 相似文献
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As one of the participants in the Subseasonal to Seasonal(S2S) Prediction Project, the China Meteorological Administration(CMA) has adopted several model versions to participate in the S2S Project. This study evaluates the models’ capability to simulate and predict the Madden-Julian Oscillation(MJO). Three versions of the Beijing Climate Center Climate System Model(BCC-CSM) are used to conduct historical simulations and re-forecast experiments(referred to as EXP1, EXP1-M, and EXP2, respectively)... 相似文献
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A Correction Method Suitable for Dynamical Seasonal Prediction 总被引：8，自引：0，他引：8
Based on the hindcast results of summer rainfall anomalies over China for the period 1981–2000 by the Dynamical Climate Prediction System (IAP-DCP) developed by the Institute of Atmospheric Physics, a correction method that can account for the dependence of model’s systematic biases on SST anomalies is proposed. It is shown that this correction method can improve the hindcast skill of the IAP-DCP for summer rainfall anomalies over China, especially in western China and southeast China, which may imply its potential application to real-time seasonal prediction. 相似文献
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In this paper,a heavy rainfall process occurring in the Huaihe River Basin during 9-10 July 2005 is studied by the new generation numerical weather prediction model system-GRAPES,from the view of different initial field effects on the prediction of the model.Several numerical experiments are conducted with the initial conditions and lateral boundary fields provided by T213 L31 and NCEP final analyses,respectively. The sensitivity of prediction products generated by GRAPES to different initial conditions,including effects of three-dimensional variational assimilation on the results,is discussed.After analyzing the differences between the two initial fields and the four simulated results,the memonic ability of the model to initial fields and their influences on precipitation forecast are investigated.Analyses show the obvious differences of sub-synoptic scale between T213 and NCEP initial fields,which result in the corresponding different simulation results,and the differences do not disappear with the integration running.It also shows that for the same initial field whether it has data assimilation or not,it only obviously influences the GRAPES model results in the initial 24 h.Then the differences reduce.In addition,both the Iocation and intensity of heavy rain forecasted by GRAPES model Further is very close to the fact,but the forecasting area of strong torrential rain has some differences from the fact.For the same initial field when it has assimilation, the 9-12-,12-24-,and 0-24-h precipitation forecasts of the model are better than those without assimilation. All these suggest that the ability of GRAPES numerical prediction depends on the different initial fields and lateral boundary conditions to some extent,and the differences of initial fields will determine the differences of GRAPES simulated results. 相似文献
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This paper presents a study on the statistical forecasts of typhoon tracks. Numerical models have their own systematic errors, like a bias. In order to improve the accuracy of track forecasting, a statistical model called DLM (dynamic linear model) is applied to remove the systematic error. In the analysis of typhoons occurring over the western North Pacific in 1997 and 2000, DLM is useful as an adaptive model for the prediction of typhoon tracks. 相似文献
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Shingo Yamada Forecast Division Japan Meteorological Agency Tokyo Japan Shuhei Maeda Administration Division Climate Marine Department Japan Meteorological Agency Tokyo Japan K. Gambo c/o Department of Earth Planetary Physi 《大气科学进展》1997,(1)
ＮｏｔｅｓｏｎＥｘｔｅｎｄｅｄ－ＲａｎｇｅＡｔｍｏｓｐｈｅｒｉｃＰｒｅｄｉｃｔｉｏｎｉｎｔｈｅＮｏｒｔｈｅｒｎＨｅｍｉｓｐｈｅｒｅＷｉｎｔｅｒＳｈｉｎｇｏＹａｍａｄａＦｏｒｅｃａｓｔＤｉｖｉｓｉｏｎ，ＪａｐａｎＭｅｔｅｏｒｏｌｏｇｉｃａｌＡｇｅｎｃｙ... 相似文献
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Heavy Rainfall Ensemble Prediction： Initial Condition Perturbation vs Multi-Physics Perturbation 总被引：4，自引：0，他引：4 下载免费PDF全文
下载免费PDF全文 Mesoscale ensemble is an encouraging technology for improving the accuracy of heavy rainfall predictions. Occurrences of heavy rainfall are closely related to convective instability and topography. In mid-latitudes, perturbed initial fields for medium-range weather forecasts are often configured to focus on the baroclinic instability rather than the convective instability. Thus, alternative approaches to generate initial perturba- tions need to be developed to accommodate the uncertainty of the convective instability. In this paper, an initial condition perturbation approach to mesoscale heavy rainfall ensemble prediction, named as Different Physics Mode Method （DPMM）, is presented in detail. Based on the PSU/NCAR mesoscale model MM5, an ensemble prediction experiment on a typical heavy rainfall event in South China is carried out by using the DPMM, and the structure of the initial condition perturbation is analyzed. Further, the DPMM ensem- ble prediction is compared with a multi-physics ensemble prediction, and the results show that the initial perturbation fields from the DPMM have a reasonable mesoscale circulation structure and could reflect the prediction uncertainty in the sensitive regions of convective instability. An evaluation of the DPMM ini- tial condition perturbation indicates that the DPMM method produces better ensemble members than the multi-physics perturbation method, and can significantly improve the precipitation forecast than the control non-ensemble run. 相似文献
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1. Introduction Drought disasters occur frequently in North China, with droughty extension being the widest, droughty intensity the most serious and lasting time the longest. In particular, the serious drought has threatened agriculture extremely in recent years, as such has influenced economy, ecosystem, and even daily life of people. The drought in North China is not only the vital research subject for meteorologists, but also one of the issues concerned for government. Therefore, finding … 相似文献
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In conventional time series analysis, a process is often modeled as three additive components： linear trend, seasonal effect, and random noise. In this paper, we perform an analysis of surface air temperature in a region of China using a decomposition method in time series analysis. Applications to the National Centers for Environmental Prediction/the National Center for Atmospheric Research （NCEP/NCAR） Collaborative Reanalysis data in this region of China are discussed. The main finding was that the surface air temperature trend estimated for January 1948 to February 2006 was not statistically significant at 0.5904℃ （100 yr）^-1. Forecasting aspects are also considered. 相似文献
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The horizontal diffusion coefficients of the operational model (T42L9) in numerical weather prediction are optimized by the steepest descent search of multi-dimensional optimization. In order to improve prediction accuracy in low latitudes, the optimum horizontal diffusion coefficients are chosen, with changing variation of the basic diffusion coefficient with the passage of time, and later forecasts are also made better. In view of the averages of forecast verifications of 9 cases, the forecasts with optimum diffusion coefficients are an improvement on operational forecasts. It means that the forecasts are got much better with optimum values of some important parameters by optimization in numerical weather prediction. 相似文献
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Since the beginning of the Association of Southeast Asian Nations Climate Outlook Forum(ASEANCOF)in 2013,the most difficult challenge has been the rainfall forecast in boreal winter.This is the Maritime Continent monsoon season during which rainfall reaches maximum in the annual cycle.This forecast difficulty arises in spite of the general notion that seasonal predictability of the Maritime Continent rainfall may be higher than most places because of the strong and robust influences of ENSO.The lower predictability is consistent with the lower correlation between ENSO and western Maritime Continent rainfall that reaches minimum during the boreal winter monsoon.Various theories have been proposed to explain this low correlation.In this paper,we review the research on ENSO–Maritime Continent rainfall relationship and show that the main cause of the forecast difficulty is the wind–terrain interaction involving the Sumatran and Malay Peninsula mountains,rather than the effect of sea surface temperature(SST).The wind–terrain interaction due to the low-level regional scale anomalous horizontal circulation offsets the anomalous Walker circulation during both El Ni?o and La Ni?a.The net result of these two opposing responses to ENSO is a lower local predictability.We propose to call this low-predictability region the WIMP(Western Indonesia–Malay Peninsula)region both for its geographical location and its special characteristic of causing difficulties for forecasters to make a confident forecast for the boreal winter.Our result suggests that climate models lack skills in forecasting rainfall in this region because their predictability depends strongly on SST. 相似文献
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Application of ATOVS Microwave Radiance Assimilation to Rainfall Prediction in Summer 2004 总被引：3，自引：0，他引：3
Experiments are performed in this paper to understand the influence of satellite radiance data on the initial field of a numerical prediction system and rainfall prediction. First, Advanced Microwave Sounder Unit A （AMSU-A） and Unit B （AMSU-B） radiance data are directly used by three-dimensional variational data assimilation to improve the background field of the numerical model. Then, the detailed effect of the radiance data on the background field is analyzed. Secondly, the background field, which is formed by application of Advanced Television and Infrared Observation Satellite Operational Vertical Sounder （ATOVS） microwave radiance assimilation, is employed to simulate some heavy rainfall cases. The experiment results show that the assimilation of AMSU-A （B） microwave radiance data has a certain impact on the geopotential height, temperature, relative humidity and flow fields. And the impacts on the background field are mostly similar in the different months in summer. The heavy rainfall experiments reveal that the application of AMSU-A （B） microwave radiance data can improve the rainfall prediction significantly. In particular, the AMSU-A radiance data can significantly enhance the prediction of rainfall above 10 mm within 48 h, and the AMSU-B radiance data can improve the prediction of rainfall above 50 mm within 24 h. The present study confirms that the direct assimilation of satellite radiance data is an effective way to improve the prediction of heavy rainfall in the summer in China. 相似文献
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In terms of the modular fuzzy neural network (MFNN) combining fuzzy c-mean (FCM) cluster and single-layer neural network, a short-term climate prediction model is developed. It is found from modeling results that the MFNN model for short-term climate prediction has advantages of simple structure, no hidden layer and stable network parameters because of the assembling of sound functions of the selfadaptive learning, association and fuzzy information processing of fuzzy mathematics and neural network methods. The case computational results of Guangxi flood season (JJA) rainfall show that the mean absolute error (MAE) and mean relative error (MRE) of the prediction during 1998 2002 are 68.8 mm and 9.78%, and in comparison with the regression method, under the conditions of the same predictors and period they are 97.8 mm and 12.28% respectively. Furthermore, it is also found from the stability analysis of the modular model that the change of the prediction results of independent samples with training times in the stably convergent interval of the model is less than 1.3 mm. The obvious oscillation phenomenon of prediction results with training times, such as in the common back-propagation neural network (BPNN) model, does not occur, indicating a better practical application potential of the MFNN model. 相似文献
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Accurate prediction of tropical cyclone (TC) intensity remains a challenge due to the complex physical processes involved in TC intensity changes. A seven-day TC intensity prediction scheme based on the logistic growth equation (LGE) for the western North Pacific (WNP) has been developed using the observed and reanalysis data. In the LGE, TC intensity change is determined by a growth term and a decay term. These two terms are comprised of four free parameters which include a time-dependent growth rate, a maximum potential intensity (MPI), and two constants. Using 33 years of training samples, optimal predictors are selected first, and then the two constants are determined based on the least square method, forcing the regressed growth rate from the optimal predictors to be as close to the observed as possible. The estimation of the growth rate is further refined based on a step-wise regression (SWR) method and a machine learning (ML) method for the period 1982?2014. Using the LGE-based scheme, a total of 80 TCs during 2015?17 are used to make independent forecasts. Results show that the root mean square errors of the LGE-based scheme are much smaller than those of the official intensity forecasts from the China Meteorological Administration (CMA), especially for TCs in the coastal regions of East Asia. Moreover, the scheme based on ML demonstrates better forecast skill than that based on SWR. The new prediction scheme offers strong potential for both improving the forecasts for rapid intensification and weakening of TCs as well as for extending the 5-day forecasts currently issued by the CMA to 7-day forecasts. 相似文献
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回顾了近年来在中国科学院大气物理研究所开展的有关短期气候预测研究的进展。第一个短期气候数值预测是曾庆存等利用一个耦合了热带太平洋海洋环流模式的全球大气环流模式作出的。1997年，一个基于海气耦合模式的ENSO预测系统，包括一个海洋初始化方案被建立起来，同时也开展了基于海温异常的东亚气候可预测性研究。利用气候变动的准两年信号，王会军等提出了一个可以显著改进模式预测准确率的模式结果修正方案。为了考虑土壤湿度的初始异常对夏季气候的影响，一个利用大气资料如温度、降水等经验地反演土壤湿度的方法也被建立起来。还通过一系列的数值试验研究了 1998年夏季大水发生当中海温异常和大气环流初始异常的作用。 相似文献
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The Evolutionary Modeling and Short-range Climatic Prediction for Meteorological Element Time Series 下载免费PDF全文
下载免费PDF全文 1. Introduction Let us suppose that the meteorological element series is the set of solution by integrating a perfect cli- matic numerical model with certain initial conditions, boundary conditions etc., thus it is also the concen- trated expression of nonlinear interaction between all climatic factors (including itself) in the model. Be- cause of limited understanding the mechanism of cli- matic system changes, the unsolved problems are not less than the solved ones in the climatic numerical … 相似文献