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1.
以台风路径数值预报的短时效预报偏差和目标时效（指所需订正的时效）的纬度预报为预报因子,采用多元线性回归方法建立了台风路径预报的偏差预估方程,继而对台风路径预报进行实时订正。本文以12 h为短时效,通过对欧洲中期天气预报中心确定性预报模式（ECMWF-IFS）和集合预报模式（ECMWF-EPS）的台风路径预报的应用,得到以下结论:2018年试报结果表明,24 h、36 h、48 h、60 h、72 h、84 h订正后的ECMWF-IFS台风路径预报的平均距离误差分别比订正前减小了7.3 km、9.3 km、8.9 km、6.5 km、6.9 km、2.6 km,总体来说较强台风（指12 h的台风强度实况≥32.7 m s?1）路径预报的订正效果更好。尝试了先对ECMWF-EPS各成员的台风路径预报进行订正,再进行集成预报,并对比了以下5种方式得到的台风路径预报:“订正后的确定性预报”、“所有集合预报成员集合平均”、“优选集合预报成员集合平均”、“所有集合预报成员先订正再集合平均”和“优选集合预报成员先订正再集合平均”,2018年试报结果表明,对于平均距离误差,24 h和36 h“优选集合预报成员先订正再集合平均”最小,48 h和60 h“所有集合预报成员先订正再集合平均”最小,72 h和84 h“优选集合预报成员集合平均”最小,如果在业务中有针对性地进行应用,有望获得一个在各预报时效表现都较优异的台风路径客观综合预报结果。24 h、36 h、48 h、60 h“优选集合预报成员先订正再集合平均”的平均距离误差分别比“所有集合预报成员集合平均”减小了13.3 km、11.7 km、10.0 km、7.6 km,比中央气象台官方预报（对应的时效为12 h、24 h、36 h、48 h）减小了0.7 km、2.0 km、3.9 km、2.4 km。 相似文献
2.
Forecast skill as a function of the ensemble size is examined in a 24-member ensemble of northern winter (DJF) hindcasts
produced with the second generation general circulation model of the Canadian Centre for Climate Modelling and Analysis. These
integrations are initialized from the NCEP reanalyses at 6 h intervals prior to the forecast season. The sea surface temperatures
that are applied as lower boundary conditions are predicted by persisting the monthly mean anomaly observed prior to the forecast
period. The potential predictability that is attributed to lower boundary forced variability is estimated. In lagged-average
forecasting, the forecast skill in the first two weeks, which originates predominately from the initial conditions, is greatest
for relatively small ensemble sizes. The forecast skill increases monotonically with the ensemble size in the rest of the
season. The skill of DJF 500 hPa geopotential height hindcasts in the Northern Hemisphere and in the Pacific/North America
sector improves substantially when the ensemble size increases from 6 to 24. A statistical skill improvement technique based
on the singular value decomposition method is also more successful for larger ensembles.
Received: 22 February 2000 / Accepted: 6 December 2000 相似文献
3.
Summary The National Hurricane Center and Joint Typhoon Warning Center operational tropical cyclone intensity forecasts for the three
major northern hemisphere tropical cyclone basins (Atlantic, eastern North Pacific, and western North Pacific) for the past
two decades are examined for long-term trends. Results show that there has been some marginal improvement in the mean absolute
error at 24 and 48 h for the Atlantic and at 72 h for the east and west Pacific. A new metric that measures the percent variance
of the observed intensity changes that is reduced by the forecast (variance reduction, VR) is defined to help account for
inter-annual variability in forecast difficulty. Results show that there have been significant improvements in the VR of the
official forecasts in the Atlantic, and some marginal improvement in the other two basins. The VR of the intensity guidance
models was also examined. The improvement in the VR is due to the implementation of advanced statistical intensity prediction
models and the operational version of the GFDL hurricane model in the mid-1990s. The skill of the operational intensity forecasts
for the 5-year period ending in 2005 was determined by comparing the errors to those from simple statistical models with input
from climatology and persistence. The intensity forecasts had significant skill out to 96 h in the Atlantic and out to 72 h
in the east and west Pacific. The intensity forecasts are also compared to the operational track forecasts. The skill was
comparable at 12 h, but the track forecasts were 2 to 5 times more skillful by 72 h. The track and intensity forecast error
trends for the two-decade period were also compared. Results showed that the percentage track forecast improvement was almost
an order of magnitude larger than that for intensity, indicating that intensity forecasting still has much room for improvement. 相似文献
4.
K. Prasad 《Meteorology and Atmospheric Physics》2006,91(1-4):183-199
Summary Results of an earlier study of cyclone track prediction using a quasi-Lagrangian model (QLM) to generate track forecasts of
up to 36 hours were reported by Prasad and Rama Rao (2003). Further experiments to produce track forecasts of up to 72 hours
with an updated version of the same model have been carried out in the present study. In this case, the ability of the model
to predict recent historical cyclones in the Bay of Bengal and Arabian Sea has been assessed. Analysis of some of the structural
features of analyzed and predicted fields has been carried out. Such fields include wind distribution and vertical motion
around the cyclone centre. In addition, the merging of an idealized vortex with the large scale initial fields provided by
a global model, has been carried out for a particular case study of a May 1997 storm, which hit the Bangladesh coast. This
current study has demonstrated that the model generates a realistic structure of a tropical cyclone with an idealized vortex.
Performance evaluation has been carried out by computing the direct position errors (DPE). The results of which show that
the mean error for a 24 h forecast is about 122 km, which increases to about 256 km for a 48 h forecast and 286 km for a 72 h
forecast. These figures are comparable to similar errors in respect of tropical cyclone forecasts produced by an advanced
NWP centre, viz., the UKMO global model during the corresponding period, 1997–2000 (obtained from UKMO web site). The average
forecast errors of the UKMO model are 160 km for 24 h, 265 km for 48 h, 415 km for 72 h forecast ranges. 相似文献
5.
基于副热带奇异向量的初值扰动方法已应用于GRAPES （Global and Regional Assimilation PrEdiction System）全球集合预报系统,但存在热带气旋预报路径离散度不足的问题。通过分析发现,热带气旋附近区域初值扰动结构不合理导致预报集合不能较好地估计热带气旋预报的不确定性,是路径集合离散度不足的可能原因之一。通过建立热带气旋奇异向量求解方案,将热带气旋奇异向量和副热带奇异向量共同线性组合生成初值扰动,以弥补热带气旋区域初值扰动结构不合理这一缺陷,进而改进热带气旋集合预报效果。利用GRAPES全球奇异向量计算方案,以台风中心10个经纬度区域为目标区构建热带气旋奇异向量求解方案,针对台风“榕树”个例进行集合预报试验,并开展批量试验,利用中国中央气象台最优台风路径和中国国家气象信息中心的降水观测资料进行检验,对比分析热带气旋奇异向量结构特征和初值扰动特征,评估热带气旋奇异向量对热带气旋路径集合预报和中国区域24 h累计降水概率预报技巧的影响。结果表明,热带气旋奇异向量具有局地化特征,使用热带气旋奇异向量之后,热带气旋路径离散度增加,路径集合平均预报误差和离散度的关系得到改善,路径集合平均预报误差有所减小,集合成员更好地描述了热带气旋路径的预报不确定性;中国台风降水的小雨、中雨、大雨、暴雨各量级24 h累计降水概率预报技巧均有一定提高。总之,当在初值扰动的生成中考虑热带气旋奇异向量后,可改进热带气旋初值扰动结果,并有助于改善热带气旋路径集合预报效果。 相似文献
6.
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
7.
This paper preliminarily investigates the application of the orthogonal conditional nonlinear optimal perturbations(CNOPs)–based ensemble forecast technique in MM5(Fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model). The results show that the ensemble forecast members generated by the orthogonal CNOPs present large spreads but tend to be located on the two sides of real tropical cyclone(TC) tracks and have good agreements between ensemble spreads and ensemble-mean forecast errors for TC tracks. Subsequently, these members reflect more reasonable forecast uncertainties and enhance the orthogonal CNOPs–based ensemble-mean forecasts to obtain higher skill for TC tracks than the orthogonal SVs(singular vectors)–, BVs(bred vectors)– and RPs(random perturbations)–based ones. The results indicate that orthogonal CNOPs of smaller magnitudes should be adopted to construct the initial ensemble perturbations for short lead–time forecasts, but those of larger magnitudes should be used for longer lead–time forecasts due to the effects of nonlinearities. The performance of the orthogonal CNOPs–based ensemble-mean forecasts is case-dependent,which encourages evaluating statistically the forecast skill with more TC cases. Finally, the results show that the ensemble forecasts with only initial perturbations in this work do not increase the forecast skill of TC intensity, which may be related with both the coarse model horizontal resolution and the model error. 相似文献
8.
R. I. C. C. Francis A. B. Mullan J. A. Renwick 《Theoretical and Applied Climatology》2003,75(1-2):1-14
Summary ?We evaluate United Kingdom Meteorological Office (UKMO) one-month ensemble forecasts of mean sea-level pressure (MSLP) in
the southern hemisphere (SH) to 60° S, with a special focus on their utility near New Zealand (NZ). There are 105 9-member
ensembles, at approximately two-week intervals, between 1995 and 1999. Each forecast is averaged over two successive 15-day
periods and verified against the NCEP/NCAR reanalysis data set. Compared to climatology, the skill of the ensemble mean is
slightly positive in days 1–15, and slightly negative in days 16–30. Skill near NZ is slightly lower than the SH averages.
For SH-scale circulation patterns (as seen in the first few principal components), skill is greater than for most individual
grid points, but is still negligible or negative in days 16–30. Moderate skill-spread correlations (ρ ≈−0.5) were found for
some skill scores. The way that skill varies with season and the Southern Oscillation Index is consistent with other research
but not statistically significant for this small data set. Probabilistic forecasts of low and high pressures have skill similar
to that of the ensemble mean. The ensemble spread is generally too small, in that the analysis lies within the ensemble less
often than the theoretically optimum value of 80% of the time. Measured as a fraction of the natural variability, the spread
increases substantially with time and latitude: it is less than 0.5 near the equator in days 1–15, and takes values near 1
only at higher latitudes during days 16–30. The initial sequential structure of the ensembles (a consequence of the use of
time lags in their genesis) is still apparent in days 1–15 but has disappeared by days 16–30. Three potential alternatives
to the ensemble mean were all found to have less skill than it.
Received June 17, 2001; revised July 4, 2002; accepted November 22, 2002
Published online March 17, 2003 相似文献
9.
西北太平洋(含南海)热带气旋路径集成预报分析 总被引：2，自引：1，他引：1
基于2004—2009 年中国中央气象台、日本气象厅、美国联合台风警报中心、欧洲中心对西北太平洋和南海编号热带气旋主客观预报资料,利用算术平均、多元回归以及历史平均误差等三种集成方法,建立了热带气旋路径集成预报业务化系统。通过2007—2009 年的业务运行结果分析发现,欧洲中心客观预报参与的24、48 和72 h 集成比主观预报三个成员集成预报水平分别提高约2%、3%～5%和3%～5%,减小误差2.5 km左右、6～9 km 和10～12 km。技巧分析发现,24～72 h 集成预报有正技巧,多元回归集成技巧相对稍低,而算术平均和以各成员平均误差的平方倒数为权重系数的集成技巧对于各集成成员来说技巧差异不大。96 h 集成预报对欧洲中心的客观预报没有正技巧。 相似文献
10.
2009年夏季西太平洋台风路径和强度的多模式集成预报 总被引：6，自引：3，他引：3
基于TIGGE资料中的中国气象局、欧洲中期天气预报中心、日本气象厅和英国气象局等四个中心的2009年5月1日-8月31日台风预报资料,利用多模式集合平均、消除偏差集合平均和加权消除偏差集合平均等方法,对2009年8月1-31日预报期的西太平洋的台风路径和强度(中心气压)进行24～ 72 h预报时效的多模式集成预报,并对0907号台风“天鹅”和0908号台风“莫拉克”进行个例分析.结果表明:各中心对于不同时效的预报,预报技巧有明显差异.消除偏差集合平均与加权消除偏差集合平均显著地减小了预报误差,预报效果优于最好的单个中心预报和多模式集合平均.对于24 ～ 72 h预报,加权消除偏差集合平均方法始终表现出最好的预报性能. 相似文献
11.
北半球中纬度地区地面气温的超级集合预报 总被引：25，自引：7，他引：18
基于TIGGE资料中的ECMWF、JMA、NCEP和UKMO四个中心2007年6月1日-8月31日北半球中纬度地区地面气温24～168 h集合预报资料,分别利用固定训练期超级集合(SUP, Superensemble)和滑动训练期超级集合(R-SUP, Running Training Period Superensemble )对2007年8月8-31日预报期24 d进行超级集合预报试验.采用均方根误差对预报结果进行检验评估,比较了两种超级集合方法与最好的单个中心模式预报、多模式集合平均的预报效果.结果表明,SUP预报有效降低了预报误差,24～144 h的预报效果优于多模式集合平均(EMN, Ensemble Mean)和最好的单个中心预报,168 h的预报效果略差于EMN.R-SUP预报进一步改善了预报效果.对于24～168 h的预报,R-SUP预报效果都要优于EMN.尤其对于168 h的预报,R-SUP改进了预报效果,优于EMN. 相似文献
12.
Adaptive observations for hurricane prediction 总被引：1，自引：1，他引：0
Summary This study proposes a method that can be used to provide guidelines to aircraft reconnaissance for hurricane observations.
The method combines numerical weather prediction (NWP) model with a statistical approach to target adaptive observations over
areas where the hurricane predictions are very sensitive to the initial analysis for the NWP-model. A single model experiment
is performed using regular initial analysis, while 50 other ensemble runs are performed from randomly perturbed initial states.
Under the perfect model assumption, the single model experiment serves as a true state. The method first computes the forecast
error variances at a certain verification time, e.g. hour 48, and then locates the maximum centers of variances. After the
locations of the maximum forecast error variances are known, various correlations of different variables between these maximum
variance points and the perturbation fields at the target time, e.g. hour 12, are calculated to identify those locations at
the target time, over where the observational errors might be responsible for the growth of forecast error variances at the
verification time. Statistically, these correlation fields indicate where the most sensitive areas are at the target time,
i.e. where the need for additional observations is suggested.
Hurricane Fran of 1996 is used to test the proposed method. The reason for choosing this case is that, during the first 48 hour
forecast, the track forecast from NWP-model was very close to the best track. Two additional experiments were designed to
examine the method. One experiment updates predicted variables at the target time (12 h) over the areas, to where the proposed
method indicates the forecast would be sensitive. The updating combines observations (or truth) with the first guess (predicted)
fields. Another experiment also modifies predicted variables at the target time (12 h), but over the areas where the method
indicates the forecast errors are less correlated to. The results show that the modification has greatly reduced the forecast
error variances at the verification time (48 h) in the first experiment, however it has a very little impact on the variance
fields at the forecast hour (48 h) in the second experiment. It is very clear from our experiments, that the proposed method
is able to identify sensitive areas, where additional observations can help to reduce hurricane forecast errors from an NWP-model.
Received July 19, 1999 Revised November 28, 1999 相似文献
13.
The approach of getting useful information of monthly dynamical prediction from ensemble forecasts is studied. The extended
range ensemble forecasts (8 members, the initial perturbations of the lagged average forecast (LAF)(0000, 0600, 1200 and 1800
GMT in two consecutive days) of the 500 hPa height field with the global spectral model (T63L16) from January to May 1997
are provided by the National Climate Center of China. The relationship between the spread of ensemble measured by root–mean–square
deviation of ensemble member from ensemble mean and forecast skill (the anomaly correlation or the root–mean–square distance
between the ensemble mean forecast and the observation) is significant. The spread of ensemble can evaluate the useful forecast
days N for the best estimate of 30 days mean. Thus, a weighted mean approach based on ensemble spread is put forward for monthly
dynamical prediction. The anomaly correlation of the weighted monthly mean by the ensemble spread is higher than that of both
the arithmetic mean and the linear weighted mean. Better results of the monthly mean circulation and anomaly are obtained
from the ensemble spread weighted mean.
Supported by the Excellent National State Key Laboratory Project (49823002), the National Key Project ‘Study on Chinese Short-Term
Climate Forecast System’ (96-908-02) and IAP Innovation Foundation (8-1308).
The data were provided through the National Climate Center of China. The authors wish to thank Ms. Chen Lijuan for her assistance. 相似文献
14.
Ensemble mean forecast skill and applications with the T213 ensemble prediction system 总被引：1，自引：0，他引：1
Ensemble forecasting has become the prevailing method in current operational weather forecasting. Although ensemble mean forecast skill has been studied for many ensemble prediction systems(EPSs) and different cases, theoretical analysis regarding ensemble mean forecast skill has rarely been investigated, especially quantitative analysis without any assumptions of ensemble members. This paper investigates fundamental questions about the ensemble mean, such as the advantage of the ensemble mean over individual members, the potential skill of the ensemble mean, and the skill gain of the ensemble mean with increasing ensemble size. The average error coefficient between each pair of ensemble members is the most important factor in ensemble mean forecast skill, which determines the mean-square error of ensemble mean forecasts and the skill gain with increasing ensemble size. More members are useful if the errors of the members have lower correlations with each other, and vice versa. The theoretical investigation in this study is verified by application with the T213 EPS. A typical EPS has an average error coefficient of between 0.5 and 0.8; the 15-member T213 EPS used here reaches a saturation degree of 95%(i.e., maximum 5% skill gain by adding new members with similar skill to the existing members) for 1–10-day lead time predictions, as far as the mean-square error is concerned. 相似文献
15.
GRAPES-TCM业务试验结果分析 总被引：3，自引：2，他引：3
应用数值模式GRAPES-TCM2.1（GT2.1）的升级版GRAPES-TCM2.6（GT2.6）对2005年西北太平洋及南海热带气旋路径进行了后报试验和性能检验。在检验预报路径时考察了平均距离误差、系统偏差、距离误差的地理分布和平均移速误差,并在总体检验的基础上,根据与热带气旋路径相关的重要因素（路径类型、强度、有无登陆过程以及有无移向或移速突变）进行了分类检验。结果表明,GT2.6表现出良好的预报性能,其对所有样本的24、48和72小时平均距离误差分别为135.8、230.7和336.0km,但前12小时距离误差较大（近100km）。用系统偏差订正GT2.6对转向类样本的预报路径可获得明显的改进。GT2.6对130°E以西区域特别是靠近我国东南沿海的热带气旋路径预报表现较好。GT2.6的总体平均预报移速在48小时内都比最佳路径移速偏快,初始的12小时内偏快最多（近1m·s^-1）,在48小时后转为偏慢。GT2.6和GT2.1的前48小时路径预报性能的稳定性相当,总体误差特征相似,GT2.6对所有样本的48小时预报有显著改进,平均距离误差减小17km。初步分析表明,对GT2.6的改进应主要集中在数值预报初始场的改进方面,比如在初始化过程中采用较准确的涡旋分离方案、加入引导向量改进初始移动以及适当考虑加入非对称Bogus涡旋;扩大预报区域,并适当选取预报区域起始位置等。 相似文献
16.
Summary A revised 25-point Shuman-Shapiro Spatial Filter (RSSSF) has been applied to six atmospheric circulation models and multi-model
ensemble (MME) predictions, and its effect on the improvement of model forecast skill scores of the Asian summer precipitation
anomaly is discussed in this paper. On the basis of 21-yr model ensemble predictions, the RSSSF can remove the unpredictable
‘noise’ with respect to the 2-grid wavelength in the model precipitation anomaly fields and maintain the large-scale counterpart,
which is related to the response of the model to large-scale boundary forcing. Therefore, this could possibly enhance the
forecast skill of the Asian summer rainfall anomaly in the models and the MME. The potential improvement of model forecasting
skill is found in the Asian summer monsoon region, where the anomaly correlation coefficient (ACC) has been improved by 7–40%,
corresponding to the decreased root mean square error (RMSE) in the model and the MME precipitation anomaly forecasts. 相似文献
17.
New Models for Long Range Forecasts of Summer Monsoon Rainfall over North West and Peninsular India 总被引：1，自引：0，他引：1
M. Rajeevan Pulak Guhathakurta V. Thapliyal 《Meteorology and Atmospheric Physics》2000,73(3-4):211-225
Summary New models based on (a) Multivariate Principal Component Regression (PCR) (b) Neural Network (NN) and (c) Linear Discriminant
Analysis (LDA) techniques were developed for long-range forecasts of summer monsoon (June–September) rainfall over two homogeneous
regions of India, viz., North West India and Peninsular India. The PCR and NN models were developed with two different data
sets. One set consisted 42 years (1958–1999) of data with 8 predictors and the other, 49 years (1951–1999) of data with 6
predictors. The predictors were subjected to the Principal Component Analysis (PCA) before model development. Two different
neural networks were designed with 2 and 3 hidden neurons. To avoid the nonlinear instability, 20 ensemble runs were made
while training the network and the ensemble mean results are discussed. The LDA model was developed with 42 years of data
(1958–1999) for classifying three rainfall intervals with equal prior probability of 0.33. Both the PCR and NN models showed
useful forecast skill for NW India and Peninsular India. Models with 8 predictors performed better than the models with only
6 predictors. The NN model with 3 hidden neurons performed better than model with 2 hidden neurons. For NW India, the NN model
performed better than the PCR model. The RMSE of the NN model and PCR model with 8 predictors for NW India (Peninsular India)
during the independent period 1984–99 was 12.5% (12.2%) and 12.6% (11.5%), respectively. Corresponding figures for the models
with 6 predictors are 15.0% (13.0%) and 13.9% (11.4%) respectively. During the independent period, model errors were large
in 1991, 1994, 1997 and 1999. However all the models showed deteriorating predictive skill after 1988, both for NW India and
Peninsular India. The LDA model correctly classified 62% of grouped cases for NW India and Peninsular India. The LDA model
showed better skill in classifying deficient rainfall (< − 8%) over NW India and excess rainfall (> 3%) over Peninsular India.
Received October 2, 1999 Revised December 28, 1999 相似文献
18.
目前中国气象局全球集合预报系统（China Meteorological Administration Global Ensemble Prediction System,CMA-GEPS）利用CMA全球数值预报系统分析场计算奇异向量（ANSV）,欧洲中期天气预报中心采用同化背景场计算奇异向量（FCSV）,在业务流程上先于计算ANSV,可优化集合预报系统运行时间。为此,在CMA-GEPS中探索采用FCSV进行集合预报的可行性,分析ANSV和FCSV的空间分布及相似指数,进而针对夏秋季节10个个例开展采用ANSV和FCSV的全球集合预报试验,从等压面要素集合预报技巧、中国地区24 h累积降水概率预报技巧、台风路径集合预报技巧、台风中心最低海平面气压预报技巧等方面对比二者结果。结果表明:ANSV和FCSV的主要结构特征相似,两组集合预报结果相当,表明在CMA-GEPS中使用FCSV可行,可作为未来高分辨率CMA-GEPS业务系统建设的选项。 相似文献
19.
2013年欧洲中心台风集合预报的检验 总被引：1，自引：0，他引：1
广州中心气象台利用中国气象局下发的欧洲中心台风集合预报数据,制作了台风集合预报产品,供业务参考应用。利用欧洲中心台风集合预报数据,对2013年1307—1331号热带气旋的集合预报路径和强度进行检验,通过对比集合平均、模式高分辨率确定性预报和预报员主观预报,发现路径集合平均在24~120 h预报误差最小;在有限的预报样本数中,从热带风暴到台风级别的热带气旋,各预报时效路径集合平均的误差随强度增强而减小;强引导气流背景下的热带气旋预报误差小于弱引导气流的误差。对比强度集合平均和模式高分辨率确定性预报,发现各时效集合平均的误差比确定性预报大,随着预报时效的延长误差没有明显增大或减小的趋势,而且强度集合平均预报,在中心最低气压、中心最大风速、热带气旋等级都表现出明显的系统性偏弱特征;对不同级别的热带气旋强度预报,集合平均的误差随强度增强而增大,即强度集合预报对强度较弱的热带气旋有更高的准确率;对比受强、弱引导气流影响的两类热带气旋,集合平均对受弱引导气流影响的一类预报误差更小。 相似文献
20.
Forecast skill as a function of time lead and time averaging is examined in two 6-member ensembles of seasonal hindcasts.
One ensemble is produced with the second generation general circulation model of the Canadian Centre for Climate Modelling
and Analysis (GCM2) and the other with a reduced resolution version of the numerical weather prediction model of the Canadian
Meteorological Centre (SEF). The integrations are initiated from the NCEP/NCAR reanalyzed data. Monthly sea surface temperature
anomalies observed prior to the forecast period are maintained throughout the forecast season. A statistical forecast improvement
technique, based on the singular value decomposition of forecast and reanalyzed fields, is discussed and evaluated. A simple
analogue of the hindcast integrations is used to examine the behavior of two common skill scores, the correlation skill score
and the explained variance skill score. The maximal skill score and the corresponding optimal forecast in this analogue are
identified. The total skill of the optimal forecast is a sum of two terms, one associated with the initial conditions and
the other with the lower boundary forcing. The two sources of skill operate on different time scales, with initial conditions
being more important in the first one-two weeks and the atmospheric response to the boundary forcing becoming more dominant
for longer time leads and time averages. This suggests that these sources of skill should be considered separately in forecast
optimization. The statistical technique is moderately successful in improving the skill of monthly to seasonal forecasts of
500 hPa height (Z
500) and 700 hPa temperature (T
700) in the Northern Hemisphere and in the North Pacific/North America sector. The improvement is better when the forecasts for
the first week and for the rest of the season are optimized separately. The SEF model produces better Z
500 and T
700 forecasts than GCM2 in the first one-two weeks whereas GCM2 performs slightly better at longer time leads. The skill of zero
time lead forecast decays rapidly with averaging interval for time averages up to about 30–45 days and stabilizes, or even
rises, for longer time averages. Excluding the first week from seasonal forecasts results in substantial degradation of predictive
skill.
Received: 1 November 1999 / Accepted: 24 May 2000 相似文献