共查询到20条相似文献，搜索用时 15 毫秒
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不同的大气云模式对月预报影响的数值试验 总被引：1，自引：0，他引：1
所用的 T42L10月长期数值预报谱模式采用了模式诊断云方案,在辐射计算中,用模式的预报水汽场和模式诊断云代替原民T42L9中的气候纬向平均水汽场和云,模式诊断云计算中还考虑了大地形作用对云的影响.本文利用国家气象中心1992年8月31日的客观分析资料为初始场,进行30天长期数值预报,对比研究了这两种不同的云模式对月际数值预报的影响.模式诊断云方案的预报明显优于气候云方案,采用模式诊断云预报的500hPa 高度场月平均的倾向相关系数有所提高,月平均预报误差减少.模式诊断云方案的预报误差低于对应的持续性误差.模式诊断云方法由于考虑了在预报时间内云分布的时间和空间变化,提高了模式中的辐射计算精度,从而改进模式中的云、辐射作用,克服由于气候云方法所出现的预报偏差,较显著地改善月际数值预报效果. 相似文献
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The limits of predictability of El Niño and the Southern Oscillation (ENSO) in coupled models are investigated based on retrospective forecasts of sea surface temperature (SST) made with the National Centers for Environmental Prediction (NCEP) coupled forecast system (CFS). The influence of initial uncertainties and model errors associated with coupled ENSO dynamics on forecast error growth are discussed. The total forecast error has maximum values in the equatorial Pacific and its growth is a strong function of season irrespective of lead time. The largest growth of systematic error of SST occurs mainly over the equatorial central and eastern Pacific and near the southeastern coast of the Americas associated with ENSO events. After subtracting the systematic error, the root-mean-square error of the retrospective forecast SST anomaly also shows a clear seasonal dependency associated with what is called spring barrier. The predictability with respect to ENSO phase shows that the phase locking of ENSO to the mean annual cycle has an influence on the seasonal dependence of skill, since the growth phase of ENSO events is more predictable than the decay phase. The overall characteristics of predictability in the coupled system are assessed by comparing the forecast error growth and the error growth between two model forecasts whose initial conditions are 1 month apart. For the ensemble mean, there is fast growth of error associated with initial uncertainties, becoming saturated within 2 months. The subsequent error growth follows the slow coupled mode related the model’s incorrect ENSO dynamics. As a result, the Lorenz curve of the ensemble mean NINO3 index does not grow, because the systematic error is identical to the same target month. In contrast, the errors of individual members grow as fast as forecast error due to the large instability of the coupled system. Because the model errors are so systematic, their influence on the forecast skill is investigated by analyzing the erroneous features in a long simulation. For the ENSO forecasts in CFS, a constant phase shift with respect to lead month is clear, using monthly forecast composite data. This feature is related to the typical ENSO behavior produced by the model that, unlike the observations, has a long life cycle with a JJA peak. Therefore, the systematic errors in the long run are reflected in the forecast skill as a major factor limiting predictability after the impact of initial uncertainties fades out. 相似文献
4.
Application of Artificial Neural Networks to Rainfall
Forecasting in Queensland, Australia 总被引：1，自引：0，他引：1
In this study,the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland,Australia,was assessed by inputting recognized climate indices,monthly historical rainfall data,and atmospheric temperatures into a prototype stand-alone,dynamic,recurrent,time-delay,artificial neural network.Outputs,as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009,were compared with observed rainfall data using time-series plots,root mean squared error(RMSE),and Pearson correlation coefficients.A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology’s Predictive Ocean Atmosphere Model for Australia(POAMA)-1.5 general circulation model(GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared.The application of artificial neural networks to rainfall forecasting was reviewed.The prototype design is considered preliminary,with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes. 相似文献
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为了探索地市气象台站短期气候预测的客观预报工具,引进美围NMC（National Meteorological Center）的NRSM（Nested Regional Spectral Model）短期气候模式,利用该模式对台风重灾区浙江省温州市台汛期（7～9月）的短期气候要素——月、季降水量、月平均温度进行预测。将上述要素的模式预报值以及多年平均值进行对比分析,模式对极端降水天气预报效果好,模式预报7月特涝准确率较高,对涝的预测能力较强。;对8、9月的特旱预报准确率较高,对旱的预测能力强;模式对温州各地7～9月台汛期旱涝趋势准确率可达50％～70％;对月平均温度的定量预测能力表现出色;可以将模式进行业务试应用。 相似文献
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Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated. 相似文献
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用15年北半球夏季地面温度的资料分析了月平均场与该月前后一周左右的逐日平均场在时间和波数上的相关。发现越新、越靠后的日平均场与该月的平均场相关越强。用第0天外推的未来30天平均场与实况的相关比用前30天平均场作外推的相关要显著得多。由此得出结论认为各种预报方法的效果检验应与前者定义的惯性预报作比较。文中提出了几个用于作月预报的统计模式，分别用不同时间或不同波数作预报因子。其结果均使预报准确率有所提高。既用不同时间又用不同波数作预报因子的模式更为理想。文中最后讨论了一个源于动力学考虑的统计模式。近1400个月预报例子的结果与实况的平均相关系数高达0.75，显著优于惯性预报。 相似文献
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GU Jianfeng Qingnong XIAO Ying-Hwa KUO Dale M. BARKER XUE Jishan MA Xiaoxing 《大气科学进展》2005,22(3):415-427
Using the recently developed Weather Research and Forecasting (WRF) 3DVAR and the WRF model, numerical experiments are conducted for the initialization and simulation of typhoon Rusa (2002).The observational data used in the WRF 3DVAR are conventional Global Telecommunications System (GTS) data and Korean Automatic Weather Station (AWS) surface observations. The Background Error Statistics (BES) via the National Meteorological Center (NMC) method has two different resolutions, that is, a 210-km horizontal grid space from the NCEP global model and a 10-km horizontal resolution from Korean operational forecasts. To improve the performance of the WRF simulation initialized from the WRF 3DVAR analyses, the scale-lengths used in the horizontal background error covariances via recursive filter are tuned in terms of the WRF 3DVAR control variables, streamfunction, velocity potential, unbalanced pressure and specific humidity. The experiments with respect to different background error statistics and different observational data indicate that the subsequent 24-h the WRF model forecasts of typhoon Rusa‘s track and precipitation are significantly impacted upon the initial fields. Assimilation of the AWS data with the tuned background error statistics obtains improved predictions of the typhoon track and its precipitation. 相似文献
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NASA/Goddard长波辐射方案在GRAPES_Meso模式中的应用研究 总被引：2，自引：0，他引：2
本文将NASA（National Aeronautics and Space Administration）/Goddard长波辐射方案引入到GRAPES_ Meso（Global/Regional Assimilation and PrEdiction System_Meso）模式中,对2006年4月中国地区进行了一个月的模拟试验,并与相应的NCEP（National Centers for Environmental Prediction）再分析资料进行了对比分析。试验结果表明:在模拟区域内,使用GRAPES_Meso模式进行24 h、48 h预报得到的晴空大气顶向外长波辐射通量（the clear sky outgoing longwave radiation flux,OLRC）、地面接收到向下长波辐射通量（the clear sky downward longwave radiation flux at ground,GLWC）分布形势与NCEP再分析资料具有较好的对应关系;模式预报24 h、48 h OLRC和NCEP再分析资料月平均误差百分比控制在－10%～＋10%以内,GLWC月平均误差百分比比OLRC略大,但总体上两者误差都在合理和可接受范围之内。OLRC和GLWC 24 h、48 h的预报和NCEP再分析资料的逐日距平相关系数及标准误差的对比显示,模式24 h预报OLRC、GLWC的距平相关系数月平均值分别为0.96、0.98,标准误差月平均值分别为24.54 W m-2、27.23 W m-2;模式48 h预报OLRC、GLWC的距平相关系数月平均值分别为0.9521、0.9804,标准误差月平均值分别为22.43 W m-2、27.64 W m-2。总体上,模式24 h、48 h预报OLRC和GLWC的距平相关系数都在0.93以上,标准误差都在31 W m-2以内,且GLWC预报和NCEP再分析资料的相关性比OLRC略好,OLRC预报与NCEP再分析资料的的标准误差比GLWC略小。通过和RRTM长波辐射方案对比可知,两者的预报水平基本一致。本文研究结果表明,引入NASA/Goddard长波辐射方案后的GRAPES_Meso模式整体上能够较好地预报OLRC和GLWC,该辐射方案可以作为模式GRAPES_Meso的备选辐射方案之一。 相似文献
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A nested-model system is constructed by embedding the regional climate model RegCM3 into a
general circulation model for monthly-scale regional climate forecast over East China. The systematic
errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system,
and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and
the temperature analysis of the National Meteorological Center (NMC), U.S.A., which are then used for
correcting the original forecast by the system for the period 2001-2005. After the assessment of the original
and corrected forecasts for monthly precipitation and surface air temperature, it is found that the corrected
forecast is apparently better than the original, suggesting that the approach can be applied for improving
monthly-scale regional climate dynamical forecast. 相似文献
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A neuroid BP-type three-layer mapping model is used for monthly rainfall forecasting in terms of 1946-1985 Nanjing monthly precipitation records as basic sequences and the model has the form i × j ＝ 8 × 3, K = 1; by steadily modifying the weighing coefficient, long-range monthly forecasts for January to December, 1986 are constructed and 1986 month-to-month predictions are made based on, say, the January measurement for February rainfall and so on, with mean absolute error reaching 6,07 and 5,73 mm, respectively. Also, with a different monthly initial value for June through September, 1994, neuroid forecasting is done, indicating the same result of the drought in Nanjing dur-ing the summer, an outcome that is in sharp agreement with the observation. 相似文献
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集合方法在月动力预报信息提取中的应用 总被引：1，自引：0，他引：1
本工作将集合方法应用于提取月动力预报有用信息。利用中国气象局国家气候中心Ｔ６３Ｌ１６全球谱模式的５００百帕高度场月集合预报产品（集合成员数为８个，初始场的选取采用滞后方法（ＬＡＦ），即相邻两天的００００，０６００，１２００和１８００ＧＭＴ的初始化资料），就１９９７年１月至５月共１５次预报，分析了集合预报成员间的离散度与预报评分（距平相关系数和均方根误差）的关系，研究了用集合各成员预报离散度作为各个成员逐日预报的权重对月预报效果的影响。结果表明集合预报成员的离散度与预报评分有显著的相关，是有效预报长度Ｎ的一个很好估计；用离散度作为权重平均的月预报高度距平相关系数明显高于算术平均和线性权重，此外个例分析表明月平均环流及其异常的预报得到明显的提高。 相似文献
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Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type 总被引：2，自引：0，他引：2
In terms of 34-year monthly mean temperature series in 1946-1979, the multi-level mapping model of neural network BP type was applied to calculate the system’s fractual dimension D0 = 2.8, leading to a three-level model of this type with i × j = 3 × 2, k = 1, and the 1980 monthly mean temperture prediction on a long-term basis were pre-pared by steadily modifying the weighting coefficient, making for the correlation coefficient of 97% with the measurements. Furthermore, the weighting parameter was modified for each month of 1980 by means of observations, therefore constructing monthly mean temperature forecasts from January to December of the year, reaching the correlation of 99.9% with the measurements. Likewise, the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlation of 98% and the month-to month forecasts of 99.4%. 相似文献
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利用2020年6月1日—2022年5月31日CMA GD模式2 m气温预报产品（预报时效为13—36 h）和同期江西省智能网格预报区域内地面站气温观测资料,计算气温预报准确率、平均误差和均方根误差,并统计分析其时空分布特征。结果表明： 1）模式预报准确率在不同月份、起报时次存在差异,暖季总体较高,冷季总体较低;暖季08时起报产品的月准确率总体高于20时,冷季反之;秋、冬季旬准确率分布更离散。模式预报产品其准确率明显低于中央气象台和江西省气象台订正产品,需订正后使用。08时起报产品对寒潮的预报效果优于20时。2）气温预报年误差分布存在日变化,最大值出现在08时,最小值出现在15时;年均方根误差峰值出现在15时和06时,白天大于夜间。3）冬季平均误差多为正值,夏季为负值,春、秋季平均误差大小界于冬、夏季之间;白天时段夏季均方根误差最大,夜间时段冬季最大。4）气温预报年误差地理分布特征明显,平原地区预报值偏低,年均方根误差最小;丘陵和山区22 h时效预报值偏高,31 h时效偏低;高山站预报值偏高,年均方根误差最大。丘陵地区负误差最大,平原地区最小;山区正误差最大。 相似文献
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Sedef Cakir Mikdat Kadioglu Nihat Cubukcu 《Theoretical and Applied Climatology》2013,111(3-4):703-711
The ensemble method has long been used to reduce the errors that are caused by initial conditions and/or parameterizations of models in forecasting problems. In this study, neural network (NN) simulations are applied to ensemble weather forecasting. Temperature forecasts averaged over 2 weeks from four different forecasts are used to develop the NN model. Additionally, an ensemble mean of bias-corrected data is used as the control experiment. Overall, ensemble forecasts weighted by NN with feed forward backpropagation algorithm gave better root mean square error, mean absolute error, and same sign percent skills compared to those of the control experiment in most stations and produced more accurate weather forecasts. 相似文献
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M. M. Smirnova K. G. Rubinshtein V. P. Yushkov 《Russian Meteorology and Hydrology》2011,36(12):777-785
Carried out is the comparison of the temporal courses of temperature and wind speed at different levels as well as of the
wind and temperature profiles in the atmospheric boundary layer obtained from the WRF regional model forecasts and using the
upper-air in situ and remote measurements in Moscow region. The errors in temperature and wind speed forecasts at different
levels are computed as well as the statistical estimates of the forecast of temperature inversions, atmospheric stratification
types, and monthly mean wind speed profiles on the basis of model forecasts and acoustic sounding. 相似文献
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Storm tracks and zonal mean flow variability: a comparison between observed and simulated data 总被引：6，自引：0，他引：6
A statistical technique is used to analyze the relation between monthly mean zonal flow and storm tracks activity in the observations and numerical simulations (ECHAM4 model). The singular value decomposition technique (SVD) has been used to correlate storm tracks and monthly mean wintertime anomaly fields. The analysis has been performed on data from January 1980 to December 1989 (NMC analyses) and on an ensemble of AGCM simulations with prescribed SST for the same period, separately in the Euro-Atlantic and Pacific sectors. We found good correlation between storm tracks activity and zonal flow in both regions. In both data and simulations the dominant SVD modes show that the storm tracks spatial displacement is in conjunction with jet shifts in the same direction. Our analysis suggests that the model is highly sensitive to the equatorial ocean forcing. Although the model produces an excessive response to El Niño and La Niña phases, it shows good capability of simulating the dynamical connection between storm tracks and jet. 相似文献
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The Potential Predictability of the South China Sea Summer Monsoon in a Dynamical Seasonal Prediction System 
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下载免费PDF全文 The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Physics Dynamical Seasonal Prediction System (IAP DCP),along with the National Centers for Environmental Prediction (NCEP) reanalysis data from the period of 1980-2000.The large-scale characteristics of the SCSSM monthly and seasonal mean low-level circulation have been well reproduced by IAP DCP,especially for the zonal wind at 850 hPa;furthermore,the hindcast variability also agrees quite well with observations.By introducing the South China Sea summer monsoon index,the potential predictability of IAP DCP for the intensity of the SCSSM has been evaluated.IAP DCP showed skill in predicting the interannual variation of SCSSM intensity.The result is highly encouraging;the correlation between the hindcasted and observed SCSSM Index was 0.58,which passes the 95% significance test.The result for the seasonal mean June-July-August SCSSM Index was better than that for the monthly mean,suggesting that seasonal forecasts are more reliable than monthly forecasts. 相似文献
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目前多数快速更新循环同化系统在各分析时刻常使用固定的背景场误差协方差。为在快速更新循环同化系统中采用日变化的背景场误差协方差,基于RMAPS-ST系统分析了其夏季和冬季日变化背景场误差协方差特征,并进行了同化及预报对比试验。结果表明,该系统夏、冬两季的背景场误差协方差均呈现出明显的日变化特征,且夜间各变量(U、V、T、RH)的误差标准差与特征值均大于日间,反映模式系统夜间的预报误差大于日间;而夏季各变量误差标准差和特征值大于冬季,也说明系统在夏季的模式预报误差比冬季大;连续3 d的循环同化试验初步表明,采用日变化背景场误差协方差可以提高同化及预报效果。 相似文献