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1.
Hsiao-Chung Tsai Russell L. Elsberry 《Asia-Pacific Journal of Atmospheric Sciences》2014,50(1):669-680
Bifurcation or bi-modal tropical cyclone intensity forecasts may arise due to uncertainty in the timing of formation, timing and magnitude of rapid intensification periods, or track forecast uncertainty leading to landfall or non-landfall or leading to interaction with warm- or cold-ocean eddies. An objective technique is developed and tested to detect these intensity bifurcation situations in our weighted-analog intensity (WANI) forecasts that are based on the 10 best historical analogs to the Joint Typhoon Warning Center (JTWC) official track forecasts. About 19% of the overall sample of 1136 WANI forecasts in the western North Pacific during the 2010–2012 seasons met the criteria for a substantial intensity bifurcation situation. Using a hierarchical clustering technique, two clusters of the 10 best analogs are defined and separate WANI forecasts and intensity spreads are calculated for the two clusters. If an always perfect selection of the correct cluster WANI forecast of each bifurcation situation is made, a substantial improvement in the intensity mean absolute errors is achieved relative to the original WANI forecasts based on all 10 of the best analogs. These perfect-cluster selection WANI forecasts have smaller bias errors and are more highly correlated with the verifying intensities at all forecast intervals through 120 h. Without further bias correction and calibration, the cluster WANI intensity spreads are under-determined as the Probability of Detections are smaller than the desired 68%. Four examples of WANI cluster predictions of intensity bifurcation situations are provided to illustrate how a correct choice of the intensity forecast and the intensity spread can be the basis for improved warnings of the threat from western North Pacific tropical cyclones. 相似文献
2.
Hsiao-Chung Tsai Russell L. Elsberry 《Asia-Pacific Journal of Atmospheric Sciences》2016,52(3):281-290
The accuracy of the western North Pacific tropical cyclone intensity forecast guidance products available at the Joint Typhoon Warning Center (JTWC) is evaluated relative to a new skill metric called Weighted Analog Intensity Pacific (WAIP) that includes knowledge of the JTWC official track forecast and the current intensity, which is information that is available at the time the intensity forecast is generated. An intensity consensus technique called S5XX that includes statistical-dynamic intensity forecasts plus other dynamic and thermodynamic prediction techniques has statistically significant smaller errors than WAIP at 24 h and 48 h and has similar accuracy through 120 h. While the track consensus CONW is a critical input to the JTWC official track forecast, it has no skill relative to WAIP as an intensity forecast. Three regional numerical models also have no skill relative to WAIP, and especially at forecast intervals beyond 72 h because their mean absolute errors are statistically significantly larger than for WAIP. Furthermore, these regional models have statistically significant positive or negative intensity biases relative to the verifying intensities. However, an experimental consensus technique called CMES that includes these three regional models has small accuracy relative to WAIP in the 24 h to 72 h forecast intervals. Geographical-based comparisons of the intensity guidance products with the WAIP indicate almost all of the products are more accurate than WAIP over the South China Sea region. The statistical-dynamic consensus technique S5XX does have skill through 72 h for landfalling situations along the coasts of China and Southeast Asia. At 120 h, the WAIP has superior performance over the guidance products over most areas of the western North Pacific, but again the S5XX is more accurate than WAIP for landfalling tropical cyclones on the Philippine Islands, Southeast Asia, South China, and northeastern Japan. This information will be useful to the forecaster in deciding when and where (or how much) to rely on each guidance product in preparing the five-day intensity forecast once the official track forecast has been established. 相似文献
3.
Hsiao-Chung Tsai Russell L. Elsberry 《Asia-Pacific Journal of Atmospheric Sciences》2014,50(4):507-518
A version of our situation-dependent intensity prediction (SDIP) is proposed for operational application after three modifications: (i) Ten historical track analogs are matched with Joint Typhoon Warning Center (JTWC) official track forecasts rather than besttracks; (ii) Giving two times as much weight to the 72 h — 120 h portion of the track as to the 0–72 h portion to give higher rankings for analog tracks with similar landfall or recurvature positions and timing; and (iii) Weighting both the intensity prediction technique and a new intensity spread guidance product according to new rankings of the track analogs rather than assuming all track analogs are equally likely. These special matchings and weightings of the track analogs in this weighted-analog intensity (WANI) add skill in the 72–120 h forecast intervals in regions where landfalls occur. Viability as an operational technique is demonstrated as the WANI has only 1 kt larger mean absolute errors than the JTWC intensity errors from 12 h through 72 h, and the WANI is 5 kt (20%) better at 120 h. The WANI rank-weighted intensity spreads each 12 h among the 10 best historical track analogs are processed to reduce any intensity bias and calibrated to reduce (increase) the over-determined (under-determined) intensity spreads at early (later) forecast intervals. Thus, the situation-dependent intensity spread guidance is generated that will include about 68% of the verifying intensities at all forecast intervals. Four examples of the WANI intensity predictions and intensity spread guidance are presented to illustrate how the forecaster might use this information in potential landfall and intensity bifurcation situations. 相似文献
4.
Russell L. Elsberry Hsiao-Chung Tsai 《Asia-Pacific Journal of Atmospheric Sciences》2014,50(3):297-306
A situation-dependent intensity prediction (SDIP) technique is developed for western North Pacific tropical cyclones that is based on the average of the intensity changes from the 10 best historical track analogs to the Joint Typhoon Warning Center best-tracks. The selection of the 10 best track analogs is also conditioned on the current intensity, and it is demonstrated that for a subsample of current intensities less than or equal to 35 kt the intensity mean absolute errors (MAEs) and biases are smaller than for the greater than 35 kt intensity subsample. The SDIP is demonstrated to have advantages as an intensity skill measure at forecast intervals beyond 36 h compared to the current climatology and persistence technique that uses only variables available at the initial time. The SDIP has significantly smaller intensity MAEs beyond 36 h with an almost 20% reduction at 120 h, has significantly smaller intensity biases than the present skill metric beyond 12 h, and explains 36% of the intensity variability at 120 h compared to 20% explained variance for the current technique. The probability distributions of intensities at 72 h and 120 h predicted by the SDIP are also a better match of the distribution of the verifying observations. Intensity spread guidance each 12 h to 120 h is developed from the intensity spread among the 10 best historical track analogs. The intensity spread is calibrated to ensure that the SDIP forecasts will have a probability of detection (PoD) of at least 68.26%. While this calibrated intensity spread is specifically for the SDIP technique, it would provide a first-order spread guidance for the PoD for the official intensity forecast, which would be useful intensity uncertainty information for forecasters and decision-makers. 相似文献
5.
The ensemble Kalman filter (EnKF), as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23–30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error me... 相似文献
6.
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
7.
This paper proposes a method for multi-model ensemble forecasting based on Bayesian model averaging (BMA), aiming to improve the accuracy of tropical cyclone (TC) intensity forecasts, especially forecasts of minimum surface pressure at the cyclone center (Pmin). The multi-model ensemble comprises three operational forecast models: the Global Forecast System (GFS) of NCEP, the Hurricane Weather Research and Forecasting (HWRF) models of NCEP, and the Integrated Forecasting System (IFS) of ECMWF. The mean of a predictive distribution is taken as the BMA forecast. In this investigation, bias correction of the minimum surface pressure was applied at each forecast lead time, and the distribution (or probability density function, PDF) of Pmin was used and transformed. Based on summer season forecasts for three years, we found that the intensity errors in TC forecast from the three models varied significantly. The HWRF had a much smaller intensity error for short lead-time forecasts. To demonstrate the proposed methodology, cross validation was implemented to ensure more efficient use of the sample data and more reliable testing. Comparative analysis shows that BMA for this three-model ensemble, after bias correction and distribution transformation, provided more accurate forecasts than did the best of the ensemble members (HWRF), with a 5%–7% decrease in root-mean-square error on average. BMA also outperformed the multi-model ensemble, and it produced “predictive variance” that represented the forecast uncertainty of the member models. In a word, the BMA method used in the multi-model ensemble forecasting was successful in TC intensity forecasts, and it has the potential to be applied to routine operational forecasting. 相似文献
8.
The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts.For nine selected TC cases,the NFSV-tendency perturbations of the WRF model,including components of potential temperature and/or moisture,are calculated when TC intensities are forecasted with a 24-hour lead time,and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts.The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time,and their dominant energies concentrate in the middle layers of the atmosphere.Moreover,such structures do not depend on TC intensities and subsequent development of the TC.The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs,makes larger contributions to the TC intensity forecast uncertainty.Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model,even if using a WRF with coarse resolution. 相似文献
9.
中央气象台台风强度综合预报误差分析 总被引：6，自引：5，他引：1
本文从总误差、逐年趋势、误差分布等方面对2001—2012年中央气象台（Central Meteorological Observatory, CMO）的台风（TC）强度综合预报水平进行分析,初步分析了强度迅速变化台风预报偏差大的原因。结果表明,强度预报水平没有明显改善,预报误差呈现逐年波动状态,强度稳定TC的预报误差最小,迅速加强TC的预报误差最大。24、96~120 h预报偏强的概率较大,而48~72 h预报偏弱的概率大。南海东北部等海域的预报误差较大,应在业务预报中特别予以关注。随着TC强度的逐渐增强,强度预报在120 h内预报偏强的可能性变大,而强度预报偏弱的可能性减小。根据误差分析结果,提出了一个强度概率预报方案,检验结果表明可在业务中参考使用。 相似文献
10.
This study presented an evaluation of tropical cyclone (TC) intensity forecasts from five global ensemble prediction systems (EPSs) during 2015-2019 in the western North Pacific region. Notable error features include the underestimation of the TC intensity by ensemble mean forecast and the under-dispersion of the probability forecasts.The root mean square errors (brier scores) of the ensemble mean (probability forecasts) generally decrease consecutively at long lead times during the five years, but fluctuate between certain values at short lead times.Positive forecast skill appeared in the most recent two years (2018-2019) at 120 h or later as compared with the climatology forecasts. However, there is no obvious improvement for the intensity change forecasts during the 5-yearperiod, with abrupt intensity change remaining a big challenge. The probability forecasts show no skill for strongTCs at all the lead times. Among the five EPSs, ECMWF-EPS ranks the best for the intensity forecast, while NCEP-GEFS ranks the best for the intensity change forecast, according to the evaluation for ensemble mean and dispersion. As for the other probability forecast evaluation, ECMWF-EPS ranks the best at lead times shorter than 72 h, while NCEP-GEFS ranks the best later on. 相似文献
11.
应用国家气象中心全球谱模式T213L31(简称T213) 及其升级版本T639L60(简称T639) 对2009—2010年西北太平洋热带气旋数值预报的结果进行对比。结果表明:T213与T639模式24～120 h预报平均距离误差基本相近,但由于T639模式分辨率较高,T639模式的热带气旋强度预报明显好于T213模式。从分类误差来看,T639模式对于西北行登陆及转向热带气旋的路径预报好于T213模式,但对西行及北上热带气旋预报误差偏大。对于异常路径热带气旋预报,T639模式能较好预报环流形势的突然调整,对路径突变的热带气旋预报比T213模式有明显优势;从登陆类热带气旋预报的移向误差来看,T213模式存在东北偏北向系统性偏差,T639模式存在东北偏东向系统性偏差。 相似文献
12.
以神经网络方法为基础,建立西北太平洋热带气旋强度预测模型,模型首先进行历史相似热带气旋选择。从选择的样本出发,计算得到一组气候持续因子、天气学经验因子和动力学因子, 对这些因子采用逐步回归方法进行筛选,将筛选得到的因子同对应时效的热带气旋强度输入神经网络训练模块,从而得到优化的预测模型。从2004-2005年西北太平洋26个热带气旋过程对12,24,36,48,72h等不同预报时效分别进行的634,582,530,478,426次预测试验结果的统计来看,相对于线性回归模型预测水平,该模型显著降低了各时段的预测误差。从几个热带气旋个例的预测结果来看, 该模型对超强台风, 以及具有强度迅速加强、再次加强等特征的热带气旋过程均有很好的描述能力。 相似文献
13.
数值模式的热带气旋强度预报订正及其集成应用 总被引：2，自引：0，他引：2
提供热带气旋强度预报产品的业务数值天气预报模式有很多,并已表现出一定的预报技巧,为提高对模式热带气旋强度预报产品的定量应用能力,分析2010—2012年7个业务数值模式的西北太平洋热带气旋强度预报,发现预报误差不仅受到模式热带气旋初始强度误差的显著影响,还与热带气旋及其所处环境的初始状况有密切关系,包括热带气旋初始强度、尺度、移速、环境气压、环境风切变、热带气旋发展潜势等。根据这些因子与各模式热带气旋强度预报误差之间的相关性,采用逐步回归方法建立热带气旋强度预报误差的统计预估模型,并通过逐个热带气旋滚动式建模来进行独立样本检验。检验结果表明,基于误差预估的模式订正预报比模式直接输出的热带气旋强度预报有显著改进,在此基础上建立的热带气旋强度多模式集成预报方案相对气候持续性预报方法在12 h有28%的正技巧,在24—72 h则稳定在15%—20%,具有业务参考价值。 相似文献
14.
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
15.
Hee Choon Lee Yong Hee Lee Jong-Chul Ha Dong-Eon Chang Aldo Bellon Isztar Zawadzki Gyuwon Lee 《Asia-Pacific Journal of Atmospheric Sciences》2010,46(3):383-391
The MAPLE system has been implemented in real-time in Korea since June 2008, producing forecasts up to 6 hours every 10 minutes. An object-oriented verification method has been applied for the summer season (June–July–August) over the Korean Peninsula to evaluate and understand the characteristics of the forecast results. The CRA (contiguous rain area) approach is used to decompose the total error into the different error components; location, pattern, and volume errors. The mean displacement error is smaller than 20 km up to the 3-h forecasts and increases with forecast time. The ratio between the displacement (location) error and the total error is less than 7% even for a 3-h forecast. This result indicates that MAPLE produces reliable forecast in terms of precipitation location. However, the pattern error is larger than 90% of the total error. Contingency scores that are defined with different categories of rain intensity and displacement errors show the outstanding performance up to 2.5 hours. MAPLE overpredicts rain areas with the threshold of 1 mm h?1 rain intensity throughout forecast periods. However, the heavy rainfall events are poorly predicted due to the inherent limitation of extrapolation-based nowcasting technique. 相似文献
16.
This paper presents a new multiple linear regression(MLR) approach to updating the hourly, extrapolated precipitation forecasts generated by the INCA(Integrated Nowcasting through Comprehensive Analysis) system for the Eastern Alps.The generalized form of the model approximates the updated precipitation forecast as a linear response to combinations of predictors selected through a backward elimination algorithm from a pool of predictors. The predictors comprise the raw output of the extrapolated precipitation forecast, the latest radar observations, the convective analysis, and the precipitation analysis. For every MLR model, bias and distribution correction procedures are designed to further correct the systematic regression errors. Applications of the MLR models to a verification dataset containing two months of qualified samples,and to one-month gridded data, are performed and evaluated. Generally, MLR yields slight, but definite, improvements in the intensity accuracy of forecasts during the late evening to morning period, and significantly improves the forecasts for large thresholds. The structure–amplitude–location scores, used to evaluate the performance of the MLR approach,based on its simulation of morphological features, indicate that MLR typically reduces the overestimation of amplitudes and generates similar horizontal structures in precipitation patterns and slightly degraded location forecasts, when compared with the extrapolated nowcasting. 相似文献
17.
This study investigates multi-model ensemble forecasts of track and intensity of tropical cyclones over the western Pacific, based on forecast outputs from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts, Japan Meteorological Agency and National Centers for Environmental Prediction in the THORPEX Interactive Grand Global Ensemble (TIGGE) datasets. The multi-model ensemble schemes, namely the bias-removed ensemble mean (BREM) and superensemble (SUP), are compared with the ensemble mean (EMN) and single-model forecasts. Moreover, a new model bias estimation scheme is investigated and applied to the BREM and SUP schemes. The results showed that, compared with single-model forecasts and EMN, the multi-model ensembles of the BREM and SUP schemes can have smaller errors in most cases. However, there were also circumstances where BREM was less skillful than EMN, indicating that using a time-averaged error as model bias is not optimal. A new model bias estimation scheme of the biweight mean is introduced. Through minimizing the negative influence of singular errors, this scheme can obtain a more accurate model bias estimation and improve the BREM forecast skill. The application of the biweight mean in the bias calculation of SUP also resulted in improved skill. The results indicate that the modification of multi-model ensemble schemes through this bias estimation method is feasible. 相似文献
18.
为综合不同模式对不同量级降水的预报优势,设计一种全球模式与区域模式结合的降水分级最优化权重集成预报算法,集成经最优TS评分订正法（optimal threat score,OTS）订正后的欧洲中期天气预报中心降水预报产品（以下简称EC-OTS）和华东区域中尺度模式降水预报产品（以下简称SMS-OTS）。以泛长江区域（23°~39°N,101°~123°E）为研究范围,基于2018年不同降水量级的TS评分最优化确定SMS-OTS和EC-OTS在不同降水量级时的最优权重系数以及最优集成方案,并以2019年降水数据为独立样本进行预报试验。结果表明:对于最优权重系数,EC-OTS在低降水量级权重较大,随着降水量级的加大,SMS-OTS的权重也逐渐加大;最优集成方案为初始集成降水量预报取SMS-OTS,集成运算迭代3次;集成预报在几乎所有预报时效、所有降水量级的TS评分均高于EC-OTS和SMS-OTS,其平均绝对误差略小于EC-OTS,显著小于SMS-OTS;集成预报12 h累积降水预报的TS评分较省级预报员主观预报高-0.009~0.041,24 h累积降水预报的TS评分较国家气象中心预报员主观预报高0.009~0.023。 相似文献
19.
为提升GRAPES_TYM对西北太平洋和中国南海热带气旋路径及强度的预报能力、增加对北印度洋热带气旋的预报,2019年8月GRAPES_TYM 3.0版投入业务运行。GRAPES_TYM 3.0版的模式垂直分层由GRAPES_TYM 2.2版的50层增加到68层;预报区域由覆盖西北太平洋、中国南海扩展到覆盖北印度洋。试验结果显示:模式垂直分层增加可以改进模式对强台风及超强台风的预报能力,减小平均路径预报误差、显著减小平均强度预报误差以及强度预报负偏差;模式预报区域扩大到覆盖北印度洋对平均路径误差和平均强度误差影响不显著,但长时效预报比较敏感,如20°N以北热带气旋120 h预报路径。2016—2018年的回算结果与NCEP-GFS和ECMWF的预报结果对比分析表明:GRAPES_TYM 3.0版的平均路径误差与NCEP-GFS接近,同ECMWF相比误差较大;但24—96 h强度预报误差明显小于NCEP-GFS和ECMWF,NCEP-GFS和ECMWF对热带气旋强度预报存在明显的负偏差。综上所述,模式垂直分层由50层增加到68层对热带气旋强度预报至关重要,而长时效路径预报对模式预报区域扩大到覆盖北印度洋更为敏感。 相似文献
20.
Weather manifests in spatiotemporally coherent structures. Weather forecasts hence are affected by both positional and structural or amplitude errors. This has been long recognized by practicing forecasters(cf., e.g.,Tropical Cyclone track and intensity errors). Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors, most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error.The Forecast Error Decomposition(FED) method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field. The total error is then partitioned into three orthogonal components:(a) large scale positional,(b) large scale structural, and(c) small scale error variance.The use of FED is demonstrated over a month-long MSLP data set. As expected, positional errors are often characterized by dipole patterns related to the displacement of features, while structural errors appear with single extrema, indicative of magnitude problems. The most important result of this study is that over the test period, more than 50% of the total mean sea level pressure forecast error variance is associated with large scale positional error. The importance of positional error in forecasts of other variables and over different time periods remain to be explored. 相似文献