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1.
Western North Pacific Tropical Cyclone Intensity Guidance Evaluations Using an Alternative Verification Technique 
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下载免费PDF全文 In this study, six intensity forecast guidance techniques from the East China Regional Meteorological Center are verified for the 2008 and 2009 typhoon seasons through an alternative forecast verification technique. This technique is used to verify intensity forecasts if those forecasts call for a typhoon to dissipate or if the real typhoon dissipates. Using a contingency table, skill scores, chance, and probabilities are computed. It is shown that the skill of the six tropical cyclone intensity guidance techniques was highest for the 12-h forecasts, while the lowest skill of all the six models did not occur in 72-h forecasting. For both the 2008 and 2009 seasons, the average probabilities of the forecast intensity having a small error (6 m s-1) tended to decrease steadily. Some of the intensity forecasts had small skill scores, but the associated probabilities of the forecast intensity errors > 15 m s-1 were not the highest. 相似文献
2.
Probabilistic prediction of Indian summer monsoon rainfall using global climate models 总被引：1，自引：0，他引：1
Makarand A. Kulkarni Nachiketa Acharya Sarat C. Kar U. C. Mohanty Michael K. Tippett Andrew W. Robertson Jing-Jia Luo Toshio Yamagata 《Theoretical and Applied Climatology》2012,107(3-4):441-450
Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low. 相似文献
3.
降水邻域集合概率法是处理高分辨率降水集合预报不确定性的一种新方法。利用2017年5~7月GRAPES(Global and Regional Assimilation and Prediction Enhanced System)区域集合预报系统24 h降水预报资料,进行GRAPES降水邻域集合概率方法试验,并针对邻域概率法的等权重和邻域尺度问题,设计了邻域格点权重修正邻域方案以及二分类权重修正邻域方案,进行降水的集合概率法、等权重邻域集合概率方法、权重修正邻域集合概率方法和二分类权重修正邻域集合概率方法等四种方法的格点相关及敏感性试验,并利用多种概率预报检验评分评估上述四种方法的预报效果。试验结果表明:(1)尽管采用邻域计算方案的三种邻域集合概率方法的降水概率预报评分各有优劣,如等权重邻域集合概率法的相对作用特征曲线面积评分略优,而权重修正邻域集合概率法和二分类权重修正邻域集合概率法的降水概率预报可靠性更高,但采用了邻域计算方案的降水概率预报评分均优于传统的集合概率方法;(2)降水邻域集合概率方法的预报技巧对邻域尺度很敏感,统计评分最优的邻域半径为5~8倍模式水平格距;(3)引入了权重修正的两个邻域集合概率预报方法在24 h降水量超过10 mm时改进较明显,能够提供更加客观的概率预报结果。总体上看,降水邻域集合概率方法具有较好的应用前景,恰当的邻域概率方法及邻域半径可以获得更合理的降水概率预报结果。 相似文献
4.
The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias.In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias.When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification. 相似文献
5.
Decadal predictability and forecast skill 总被引：2，自引：1，他引：1
The “potential predictability” of the climate system is the upper limit of available forecast skill and can be characterized by the ratio p of the predictable variance to the total variance. While the potential predictability of the actual climate system is unknown its analog q may be obtained for a model of the climate system. The usual correlation skill score r and the mean square skill score M are functions of p in the case of actual forecasts and potential correlation ρ and potential mean square skill score $\mathcal{M}$ are the same functions of q in the idealized model context. In the large ensemble limit the connection between model-based potential predictability and skill scores is particularly straightforward with $q=\rho^{2}=\mathcal{M}.$ Decadal predictions of annual mean temperature produced with the Canadian Centre for Climate Modelling and Analysis coupled climate model are analyzed for information on decadal climate predictability and actual forecast skill. Initialized forecast results are compared with the results of uninitialized climate simulations. Model-based values of potential predictability q and potential correlation skill ρ are obtained and ρ is compared with the actual forecast correlation skill r. The skill of externally forced and internally generated components of the variability are separately estimated. As expected, ρ > r and both decline with forecast range τ, at least for the first five years. The decline of skill is associated mainly with the decline of the skill of the internally generated component. The potential and actual skill of a forecast of time-averaged temperature depends on the averaging period. The skill of uninitialized simulations is low for short averaging times and increases as averaging time increases. By contrast, skill is high at short averaging times for forecasts initialized from observations and declines as averaging times increase to about three years, then increases somewhat at longer averaging times. The skills of the initialized forecasts and uninitialized simulations begin to converge for longer averaging times. The potential correlation skill ρ of the externally forced component of temperature is largest at tropical latitudes and the skill of the internally generated component is largest over the North Atlantic, parts of the Southern Ocean and to some extent the North Pacific. Potential skill over extratropical land is somewhat weaker than over oceans. The distribution of actual correlation skill r is broadly similar to that of potential skill for the externally forced component but less so for the internally generated component. Differences in potential and actual skill suggest where improvements in the forecast system might be found. 相似文献
6.
Bin Wang June-Yi Lee In-Sik Kang J. Shukla C.-K. Park A. Kumar J. Schemm S. Cocke J.-S. Kug J.-J. Luo T. Zhou B. Wang X. Fu W.-T. Yun O. Alves E. K. Jin J. Kinter B. Kirtman T. Krishnamurti N. C. Lau W. Lau P. Liu P. Pegion T. Rosati S. Schubert W. Stern M. Suarez T. Yamagata 《Climate Dynamics》2009,33(1):93-117
We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework. 相似文献
7.
采用非齐次高斯回归 (NGR) 技术对国家气象中心区域集合预报系统的2 m温度预报结果开展了一阶偏差和二阶离散度的校准研究。对预报结果比较详尽的检验分析表明:校准后的2 m温度预报可靠性和预报技巧均显著提高,表现为校准后集合预报成员的均方根误差与离散度更为接近;原Talagrand直方图中的“L”形分布现象得到有效改善;Brier评分、最小连续分级概率评分 (CRPS) 明显减小,相对作用特征 (ROC) 面积增大,说明校准后的2 m温度预报表现出更好的预报技能。此外,NGR技术与自适应误差订正技术的对比试验表明,NGR在消除集合平均偏差和提高集合离散度两个方面均有优势。 相似文献
8.
探讨了预报评价的意义及应遵循的原则, 对常用的几种两类预报评分方法进行分析, 指出其应用的局限性, 得到一个判定所作预报水平是否高于随机预报、具有预报技巧的简易判别式; 提出评分权重的概念, 指出以往评分存在问题的根源是评分权重分配不当, 使评分结果的真实性受到影响, 评分无可比性, 进而提出一种考虑了评分权重的新评分方法。新评分方法满足预报评价的原则, 侧重于对两类事件中事件概率较小一方预报效果的评估, 评分结果不受事件概率影响, 具有可比性。对比分析表明:新方法比其他方法优越, 能更准确地反映预报水平, 使不同季节、不同地域的预报评分可进行比较, 是一个通用的评分方法。 相似文献
9.
Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used. 相似文献
10.
John D. Reid 《大气与海洋》2013,51(2):177-186
Abstract A terminal forecast verification scheme employing the ranked probability score (rps) has been developed. The forecast is interpreted in terms of probabilities of six operationally significant ceiling and visibility classes using probability interpretations of the terms vrbl, ocnl, RSK, etc. Comparison with the rps of climatological and persistence forecasts permits the assessment of forecast skill. To illustrate the technique, five months of forecasts for Winnipeg International Airport have been examined. The subjective forecast shows skill over climatology for the first five hours of the forecast period. A persistence forecast shows skill over climatology for three hours into the period and is a little superior to the subjective forecast in the first hour. 相似文献
11.
常规降水检验受空间及时间微小差异所带来的"双重惩罚"影响严重,邻域空间检验FSS(Fraction Skill Score)方法在确定性预报中已体现出弥补这一不足的明显优势。随着集合预报分辨率的不断提高,集合降水预报同样存在与确定性预报相似的问题。本研究将FSS方法拓展至集合预报领域,构建适用于集合预报的降水空间检验指标EFSS(Ensemble Fraction Skill Score),利用欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECMWF)集合预报模式2018年夏季降水预报产品及国家气象信息中心提供的格点化降水融合产品进行分析,发现EFSS评分不受集合成员数影响,可获取一致性的评估结论。通过与适用于集合预报的常规技巧评分EETS(Ensemble Equitable Threat Score)对比分析发现,常规技巧评分受限于评分过低而无法有效反映强降水过程间差异性特征,EFSS方法则可有效提升强降水预报检验辨识度。 相似文献
12.
关于提高天气预报准确率的几个问题 总被引：10，自引：0，他引：10
提高天气预报准确率是气象业务的一项基础性、系统性的工作。作者从天气预报的业务技术体系着眼,借鉴发达国家的发展经验,分析了提高天气预报准确率的若干问题,提出了发展精细化的预报技术体系,将数值预报模式、天气学预报方法、动力诊断和统计释用及基于卫星和雷达等现代探测技术的短时临近预警技术相结合的预报技术路线;提出了有利于精细化预报的业务体系,即发展以定量降水预报、台风预报和灾害性天气短时临近预警为重点的专业化预报业务体系;指出专家型预报队伍的建设是提高预报业务水平的关键环节。 相似文献
13.
Public weather services are trending toward providing users with probabilistic weather forecasts, in place of traditional deterministic forecasts. Probabilistic forecasting techniques are continually being improved to optimize available forecasting information. The Bayesian Processor of Forecast (BPF), a new statistical method for probabilistic forecast, can transform a deterministic forecast into a probabilistic forecast according to the historical statistical relationship between observations and forecasts generated by that forecasting system. This technique accounts for the typical forecasting performance of a deterministic forecasting system in quantifying the forecast uncertainty. The meta-Gaussian likelihood model is suitable for a variety of stochastic dependence structures with monotone likelihood ratios. The meta-Gaussian BPF adopting this kind of likelihood model can therefore be applied across many fields, including meteorology and hydrology. The Bayes theorem with two continuous random variables and the normal-linear BPF are briefly introduced. The meta-Gaussian BPF for a continuous predictand using a single predictor is then presented and discussed. The performance of the meta-Gaussian BPF is tested in a preliminary experiment. Control forecasts of daily surface temperature at 0000 UTC at Changsha and Wuhan stations are used as the deterministic forecast data. These control forecasts are taken from ensemble predictions with a 96-h lead time generated by the National Meteorological Center of the China Meteorological Administration, the European Centre for Medium-Range Weather Forecasts, and the US National Centers for Environmental Prediction during January 2008. The results of the experiment show that the meta-Gaussian BPF can transform a deterministic control forecast of surface temperature from any one of the three ensemble predictions into a useful probabilistic forecast of surface temperature. These probabilistic forecasts quantify the uncertainty of the control forecast; accordingly, the performance of the probabilistic forecasts differs based on the source of the underlying deterministic control forecasts. 相似文献
14.
J. N. Mutemi L. A. Ogallo T. N. Krishnamurti A. K. Mishra T. S. V. Vijaya Kumar 《Meteorology and Atmospheric Physics》2007,95(1-2):87-113
Summary This study examines the predictability of weather over several regions in Africa using a multimodel superensemble technique
developed at the Florida State University, which is an objective means of combining daily forecasts from multilevel global
models. It is referred to as FSUSE and up to 7 different models are used to construct the superensemble. The benchmark reanalysis
fields used are the precipitation data sets from CMORPH and all other global fields from ECMWF daily operational analysis.
The FSUSE works by using multiple linear regression to derive weights from a comparison of each member model forecast to the
benchmark analysis during a training period of the most recent 120 days, and these weights are passed to the forecast phase.
This procedure removes the bias of each model and allows for an optimal linear combination of the individual model forecasts
by taking account of the relative skill of each model to give a consensus forecast that is superior to the ensemble mean and
all the members.
Results show that bad models and poor analysis fields used during the training phase degrade the skill of the FSUSE. In the
forecasts of rainfall events over all regions of Africa, the FSUSE root-mean-square (R M S) error, equitable threat skill
score (E T S), and bias on the daily forecasts of rainfall were invariably superior to the best member model. The skills deteriorate
as the forecast lead time in days increases, with the degradation being most significant beyond day 3. In all cases, the bias
score of the FSUSE was approximately 1, while the anomaly correlation scores were to the order of 0.9. These scores indicate
the robustness of the FSUSE forecasts. Over East Africa, the FSUSE forecasts were consistent with the spatial-temporal pattern
of the Intertropical Convergence Zone (ITCZ), the main rain bearing synoptic mechanism across tropical Africa. Thus, in addition
to superior forecasts, the use of FSUSE based data sets may provide a better understanding of the dynamical processes within
the ITCZ over the region.
These results could be further improved if the daily series of operational analysis had included gauge data and if the resolution
were higher. It is hardly possible to get uniformly consistent and continuous daily observations over these diverse regions
of Africa. However, given the availability of the satellite based estimates of daily rainfall, such as CMORPH and global analysis
that are exchanged very fast nowadays, the FSUSE scheme for numerical weather predictions (N W P) provides useful medium range
weather forecasts in real-time. 相似文献
15.
R. M. Vil’fand V. F. Martazinova V. Yu. Tsepelev V. M. Khan N. P. Mironicheva G. V. Eliseev E. K. Ivanova V. A. Tishchenko D. N. Utkuzova 《Russian Meteorology and Hydrology》2017,42(8):485-493
The results of experiments on the integration of synoptic and hydrodynamic forecasts of monthly air temperature for the northwestern part of Eurasia for the period of 2011–2015 are presented. The comprehensive comparison of skill scores of individual and integrated forecasts is provided. It is demonstrated that during the cold season hydrodynamic forecasts have the highest skills among the other forecasts. It is revealed that empirical methods are characterized by the forecast skill increase in summer and by its decrease in winter and during the period of spring transformation of atmospheric circulation. In most cases the integration of diverse methods allows combining their benefits and improving the resultant forecast skill. The synchrony was detected in the forecast skill variations. It is shown that the forecast skill mainly depends on the persistence of macrocirculation patterns. 相似文献
16.
Traditional skill scores (e.g., the threat score) used in the high-resolution verification of precipitation are affected by a “double penalty” caused by slight spatial or temporal displacements, which can lead to misleading evaluations. The fractions skill score (FSS) is a popular spatial verificaiton measure that can be used to solve these problems. It can determine useful and skillful scores by neighborhood analysis, which can be used to monitor the performance of operational forecasts. However, the FSS provides different scores at each spatial scale and it is difficult to obtain a definite score for the assessment of precipitation to analyze the temporal variabilities of daily forecasts. We previously reported a modified FSS assessment method and showed that a particular analysis scale had a significant advantage in the verification of operational forecasts of precipitation. To compensate for the lack of artificial definition in the analysis scale, we report here a new integrated score that satisfies a Gaussian weight function to average the FSS over all scales. We describe the advantages of the new score in the verification of forecasts of daily and hourly precipitation, taking forecast products from the GRAPES regional model and quantitative precipitation estimation products from the National Meteorological Information Center during June and July 2017 and investigating the differences between these results and those obtained with the traditional category score. We found that a value of 0.5 can be used as a standard for the skillful FSS in the forecast of heavy rainfall. The integrated score can maintain all the advantages seen in previous studies in the verification of daily and hourly precipitation and show excellent application prospects. The long-term verification including different seasons also find that the score can effectively improve the identification characteristics of the assessment. 相似文献
17.
通过对中国气象局现行PS评分办法与理论PS评分的对比,发现现行评分办法对理论PS评分进行了两处修改。一处修改是扩大了预报正确的评定范围,但在PS评分办法实施后,在气候预测业务中,却出现了只预报2个等级的普遍现象,即在能获得高分的同时却降低了预报能力。另一处修改是按统一的要素距平划分等级,结果出现了预报对象的等级分布随着测站、月份变化而变化的现象。预报对象的等级分布是无技巧预报评分的决定因素,而预报技巧是由PS评分与无技巧预报评分之差决定的。在无技巧预报评分有差异的情况下,不同月份、不同区域之间的PS评分便失去了对比的基础。该文针对上述问题,对现行PS评分办法提出了修改建议。 相似文献
18.
中国夏季降水多模式集成概率预报研究 总被引：1，自引：0，他引：1
基于TIGGE资料中的中国气象局（CMA）、欧洲中期天气预报中心（ECMWF）、日本气象厅（JMA）、美国国家环境预报中心（NCEP）以及英国气象局（UKMO）五个中心2007-2011年5月25日-8月31日中国地区逐日12-36 h、36-60 h、60-84 h、84-108 h、108-132 h与132-156 h累积降水集合预报资料,分别利用PoorMan （POOL）和多模式消除偏差（MBRE）两种方法对2011年各中心降水概率预报进行集成,并采用RPS和BS评分方法对预报效果进行评估。结果表明,对于12-156 h逐24 h累积降水量概率预报,多模式集成预报效果优于单模式预报效果,且多模式消除偏差概率预报效果最好;针对小雨、中雨以及大雨以上降水,PoorMan和MBRE概率预报较单中心预报效果均有提高,MBRE概率预报效果优于PoorMan方法。 相似文献
19.
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
20.
V. N. Kryzhov 《Russian Meteorology and Hydrology》2012,37(5):291-297
Proposed is a method of downscaling of the global ensemble seasonal forecasts of air temperature computed using the SLAV model of the Hydrometcenter of Russia. The method is based on the regression and suggests a probabilistic interpretation of forecasts based on the assessment of uncertainty associated with the regression and model forecast ensemble spread. The verification of the method for 70 weather stations of North Eurasia using the rank probability skill score RPSS showed a significant advantage of downscaled forecasts over the forecasts interpolated from the model grid points. It is concluded that the use of the downscaling method is reasonable for the long-range forecasting of the station air temperature for North Eurasia. 相似文献