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1.
近年长期数值天气预报滤波模式的进展 总被引：1，自引：1，他引：1
近年由于短期数值天气预报、大气环流研究以及数值模拟等方面的成功,激发了人们用动力学方法做长期天气预报的兴趣。美国Spar等和Caverly等用大气环流模式分别进行了一个月的积分试验,其中一些例子获得了成功。苏联Marchuk等也提出了一个长期数值天气预报的方法,并认为在简化条件下可用来做月到季的平均温度距平预报,但现在还未看到预报结果发表。因此,总的来看长期数值天气预报还处于试验阶段。 相似文献
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1.长期预报 目前长期天气预报实际应用的方法,基本上还是预报经验结合资料统计,D·L吉尔曼形象地称长期预报方法是“经验技巧和统计技术的“烹调”混合物”。预报方法缺乏充分的物理基础,长期数值预报虽燃正在试验,如国外的大气环流模式(GCM),它通过逐日积分作月平均环流预报。预报的月平均环流距平同观测值的相关系数约为0.4,达不到业务化的水平。何况,即使环流预报正确,也不能报出一个月内的具体天气过程及相应的天气特征,应在如下方面进行工作: 相似文献
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随着数值天气预报技术和季节动力预报系统的发展,短期天气预报及长期气候预测的能力持续提高,然而介于两者之间的次季节至季节(S2S,两周至三个月)预测技巧偏低,成为当今气象学界和业务服务的难题。南京信息工程大学国家特聘专家李天明教授团队于2012年研发了基于时空投影技术的统计预报模型(STPM),成功地对中国大陆降水和气温距平,以及区域极端降水、夏季高温、冬季低温和西太平洋台风群发事件等高影响天气进行提前10~30 d的预报,并在国家气候中心及多个省份开展了业务应用。STPM也成功应用于台湾春雨预报、南海季风爆发和ENSO预测等季节至年际变化的预测。本文对S2S预测的理论基础、STPM的发展和应用进行了完整的介绍,并讨论了S2S预测业务中所面临的挑战和未来展望。 相似文献
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基于动力气候模式进行月一季尺度预报的“两步法”思想,提出一种新的预报海温场统计模型,并以该统计模型预报的海温场驱动NCAR CAM3.1模式对1981-2000年月时间尺度的东亚500 hPa高度距平场进行客观回报试验;在此基础上,提出了对预报结果的订正方法。结果表明:统计预报海温模型的预报海温场能够反映出全球海温空间分布的基本特征,并对表征ENSO事件的Ni?o3.4区海温变化的预报能力较强。该统计模型预报的海温场驱动的CAM3.1模式可以较好地预报出东亚500 hPa环流的主要分布特征,试验表明:适当的统计订正方法可以在一定程度上提高CAM3.1模式对东亚夏季500 hPa环流背景的预报技巧。 相似文献
6.
本文在已揭示的夏季(6、7、8月)海洋与冬季(12、1、2月)大气的热力遥相关联系的基础上,通过参数化形成了相应的数学模型,从夏季6月海洋与冬季1月大气的热力预报方程出发,分析了海气非同步影响函数的时间变异性,确定了数学模型的稳定性,由此进行了北半球500hPa月平均大气非绝热热流量距平、温度距平的长期预报试验。非独立和独立预报表明:利用海气热力遥相关的长期天气预报方法具有一定的预报能力和潜力。 相似文献
7.
一、引言天气预报中的动力学方法(即采用流体动力学的规律研究建立数值预报模式,对未来的天气形势场作出预报)和统计学方法(即采用气象要素之间前后期数值之间的关系,建立统计预报模式,对未来气象要素的数值作出预报)具有定量、客观及可以采用计算机进行自动化处理等优点。从方法本身来看,前者能把握大形势场的动力发展规律, 相似文献
8.
长期天气预报业务和方法研究的最新进展 总被引：7，自引：0，他引：7
该文从预报内容、预报方法和预报水平等方面总结了国内外长期天气预报业务概况。从统计和动力两方面总结了长期天气预报方法研究的现状;其中重点介绍了用GCM做月平均环流预报的进展。最后展现了长期天气预报在未来十年的发展。 相似文献
9.
本文根据世界气象组织自1984年以来召开的几次规模较大的长期天气预报学术会议及海气相互作用的专题讨论会的报告和文集,简要地介绍了这方面的动态和主要进展。指出ENSO循环和季节内部振荡的观测研究和数值模拟研究对加强长期天气预报的物理基础具有重要意义,重点介绍了英国气象局用5层和11层大气环流模式进行长期预报试验的结果。这些试验表明,在长期数值天气预报中采用实测的海表温度对减小预报误差,提高预报技巧有肯定的作用。 相似文献
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Variations of surface air temperature(SAT) are key in affecting the hydrological cycle,ecosystems and agriculture in western China in summer.This study assesses the seasonal forecast skill and reliability of SAT in western China,using the Glo Sea5 operational forecast system from the UK Met Office.Useful predictions are demonstrated,with considerable skill over most regions of western China.The temporal correlation coefficients of SAT between model predictions and observations are larger than 0.6,in both northwestern China and the Tibetan Plateau.There are two important sources of skill for these predictions in western China:interannual variation of SST in the western Pacific and the SST trend in the tropical Pacific.The tropical SST change in the recent two decades,with a warming in the western Pacific and cooling in the eastern Pacific,which is reproduced well by the forecast system,provides a large contribution to the skill of SAT in northwestern China.Additionally,the interannual variation of SST in the western Pacific gives rise to the reliable prediction of SAT around the Tibetan Plateau.It modulates convection around the Maritime Continent and further modulates the variation of SAT on the Tibetan Plateau via the surrounding circulation.This process is evident irrespective of detrending both in observations and the model predictions,and acts as a source of skill in predictions for the Tibetan Plateau.The predictability and reliability demonstrated in this study is potentially useful for climate services providing early warning of extreme climate events and could imply useful economic benefits. 相似文献
12.
Predictions of averaged SST monthly anomalous series for Nino 1-4 regions in the context of auto-adaptive filter are made using a model combining the singular spectrum analysis (SSA) and auto-regression (AR). The results have shown that the scheme is efticient in forward forecaning of the strong ENSO event in 1997-1998, it is of high reliability in retrospective forecasting of three corresponding historical strong ENSO events. It is seen that the scheme has stable skill and large accuracy for experiments of both independent samples and real cases.With modifications, the SSA-AR scheme is expected to become an efficient model in routine predictions of ENSO. 相似文献
13.
A statistical calibration scheme is applied to multi-model global seasonal ensemble reforecasts in order to predict the interannual variability of summer averaged surface maximum temperature over Italy. In some cases, this technique is shown to be able to improve the skill scores of the seasonal predictions during the last 35 years, with respect to the direct model output (DMO), using seasonal predictions initialised 1 month before the beginning of the season. It is shown that the presence of some skill in the DMO multi-model predictions is mostly due to the correct prediction of the observed secular trends in maximum temperature, and, partly, to the correct prediction of outliers, in particular, of the summer of 2003. At the same time, while the removal of trends produces a small reduction of skill in both the raw and calibrated predictions, the removal of outliers improves the performance of the calibration scheme. Once all trends and outliers are removed, the DMO predictions have no skill, while the calibrated predictions still present a detectable skill. The improvement introduced by the calibration are shown to be statistically significant by applying resampling techniques. It is shown that the reason of this partial success is linked to the fact that although the models present several shortcomings, some models can capture the existence of a weak large-scale signal, possibly linked with the presence of a summer teleconnection between the equatorial Pacific and Europe, with a spatial pattern substantially different from that associated with the temperature secular trend. The teleconnection is associated with a modulation of the quasi-stationary barotropic eddies in the Northern Hemisphere extra-tropics. 相似文献
14.
Jacques Derome Gilbert Brunet André Plante Normand Gagnon George J. Boer Francis W. Zwiers 《大气与海洋》2013,51(4):485-501
Abstract Two dynamical models are used to perform a series of seasonal predictions. One model, referred to as GCM2, was designed as a general circulation model for climate studies, while the second one, SEF, was designed for numerical weather prediction. The seasonal predictions cover the 26‐year period 1969–1994. For each of the four seasons, ensembles of six forecasts are produced with each model, the six runs starting from initial conditions six hours apart. The sea surface temperature (SST) anomaly for the month prior to the start of the forecast is persisted through the three‐month prediction period, and added to a monthly‐varying climatological SST field. The ensemble‐mean predictions for each of the models are verified independently, and the two ensembles are blended together in two different ways: as a simple average of the two models, denoted GCMSEF, and with weights statistically determined to minimize the mean‐square error (the Best Linear Unbiased Estimate (BLUE) method). The GCMSEF winter and spring predictions show a Pacific/North American (PNA) response to a warm tropical SST anomaly. The temporal anomaly correlation between the zero‐lead GCMSEF mean‐seasonal predictions and observations of the 500‐hPa height field (Z500) shows statistically significant forecast skill over parts of the PNA area for all seasons, but there is a notable seasonal variability in the distribution of the skill. The GCMSEF predictions are more skilful than those of either model in winter, and about as skilful as the better of the two models in the other seasons. The zero‐lead surface air temperature GCMSEF forecasts over Canada are found to be skilful (a) over the west coast in all seasons except fall, (b) over most of Canada in summer, and (c) over Manitoba, Ontario and Quebec in the fall. In winter the skill of the BLUE forecasts is substantially better than that of the GCMSEF predictions, while for the other seasons the difference in skill is not statistically significant. When the Z500 forecasts are averaged over months two and three of the seasons (one‐month lead predictions), they show skill in winter over the north‐eastern Pacific, western Canada and eastern North America, a skill that comes from those years with strong SST anomalies of the El Niño/La Niña type. For the other seasons, predictions averaged over months two and three show little skill in Z500 in the mid‐latitudes. In the tropics, predictive skill is found in Z500 in all seasons when a strong SST anomaly of the El Niño/La Niña type is observed. In the absence of SST anomalies of this type, tropical forecast skill is still found over much of the tropics in months two and three of the northern hemisphere spring and summer, but not in winter and fall. 相似文献
15.
Summary Errors produced by a nonlinear predictive scheme contain information about both the observations and the prediction system.
Therefore, its error history would be expected to contribute to increasing the skill of the predictions if it is included
in the forecast. In this study an error recycling procedure is developed for tropical cyclone track prediction. Errors are
defined here as differences between the model forecast and the best track position. Error histories are incorporated into
a nonlinear analogue, or simplex, forecast scheme and applied to tropical cyclone track prediction, using the archives of
observed position data associated with the forecast errors. Various forecast experiments of the cyclone tracks are performed:
standard simplex predictions using observed positions only; simplex predictions improved by error forecasts based on libraries
of both observations and the recycled forecast errors; and, finally, predictions that include NWP-model forecasts and their
errors as predictors. The resulting gains in skill of predictions out to 72 hours ahead are found to be substantial.
Received August 12, 1999 Revised November 5, 1999 相似文献
16.
Decadal climate predictions with a coupled OAGCM initialized with oceanic reanalyses 总被引：2，自引：1，他引：1
A. Bellucci S. Gualdi S. Masina A. Storto E. Scoccimarro C. Cagnazzo P. Fogli E. Manzini A. Navarra 《Climate Dynamics》2013,40(5-6):1483-1497
We investigate the effects of realistic oceanic initial conditions on a set of decadal climate predictions performed with a state-of-the-art coupled ocean-atmosphere general circulation model. The decadal predictions are performed in both retrospective (hindcast) and forecast modes. Specifically, the full set of prediction experiments consists of 3-member ensembles of 30-year simulations, starting at 5-year intervals from 1960 to 2005, using historical radiative forcing conditions for the 1960–2005 period, followed by RCP4.5 scenario settings for the 2006–2035 period. The ocean initial states are provided by ocean reanalyses differing by assimilation methods and assimilated data, but obtained with the same ocean model. The use of alternative ocean reanalyses yields the required perturbation of the full three-dimensional ocean state aimed at generating the ensemble members spread. A full-value initialization technique is adopted. The predictive skill of the system appears to be driven to large extent by trends in the radiative forcing. However, after detrending, a residual skill over specific regions of the ocean emerges in the near-term. Specifically, natural fluctuations in the North Atlantic sea-surface temperature (SST) associated with large-scale multi-decadal variability modes are predictable in the 2–5 year range. This is consistent with significant predictive skill found in the Atlantic meridional overturning circulation over a similar timescale. The dependency of forecast skill on ocean initialization is analysed, revealing a strong impact of details of ocean data assimilation products on the system predictive skill. This points to the need of reducing the large uncertainties that currently affect global ocean reanalyses, in the perspective of providing reliable near-term climate predictions. 相似文献
17.
Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River (MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands, initialized from 1 March. In addition, the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature (SST) anomalies, but the individual members show great discrepancies, indicating large uncertainty in the forecasts. Furthermore, the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982–2020, albeit with underestimated magnitudes. The Weather Forecast and Research (WRF) downscaling hindcast experiments with a finer resolution of 30 km, which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method, display improved predictions of the extreme summer precipitation anomalies to some extent. However, the performance of the downscaling predictions is highly dependent on the global model forecast skill, suggesting that further improvements on both the global and regional climate models are needed. 相似文献
18.
Yoo-Geun Ham Michele M. Rienecker Max J. Suarez Yury Vikhliaev Bin Zhao Jelena Marshak Guillaume Vernieres Siegfried D. Schubert 《Climate Dynamics》2014,42(1-2):1-20
A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS-5 Atmosphere–Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multi-variate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO’s atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 % improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the subpolar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts. 相似文献
19.
Prediction skill of monthly SST in the North Atlantic Ocean in NCEP Climate Forecast System version 2 总被引：1，自引：0，他引：1
Zeng-Zhen Hu Arun Kumar Bohua Huang Wanqiu Wang Jieshun Zhu Caihong Wen 《Climate Dynamics》2013,40(11-12):2745-2759
This work evaluates the skill of retrospective predictions of the second version of the NCEP Climate Forecast System (CFSv2) for the North Atlantic sea surface temperature (SST) and investigates the influence of El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on the prediction skill over this region. It is shown that the CFSv2 prediction skill with 0–8 month lead displays a “tripole”-like pattern with areas of higher skills in the high latitude and tropical North Atlantic, surrounding the area of lower skills in the mid-latitude western North Atlantic. This “tripole”-like prediction skill pattern is mainly due to the persistency of SST anomalies (SSTAs), which is related to the influence of ENSO and NAO over the North Atlantic. The influences of ENSO and NAO, and their seasonality, result in the prediction skill in the tropical North Atlantic the highest in spring and the lowest in summer. In CFSv2, the ENSO influence over the North Atlantic is overestimated but the impact of NAO over the North Atlantic is not well simulated. However, compared with CFSv1, the overall skills of CFSv2 are slightly higher over the whole North Atlantic, particularly in the high latitudes and the northwest North Atlantic. The model prediction skill beyond the persistency initially presents in the mid-latitudes of the North Atlantic and extends to the low latitudes with time. That might suggest that the model captures the associated air-sea interaction in the North Atlantic. The CFSv2 prediction is less skillful than that of SSTA persistency in the high latitudes, implying that over this region the persistency is even better than CFSv2 predictions. Also, both persistent and CFSv2 predictions have relatively low skills along the Gulf Stream. 相似文献
20.
Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas (so-called "sensitive areas"). And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Niño prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Niño prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year, increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations. 相似文献