Selective ensemble‐mean technique for tropical cyclone track forecast by using ensemble prediction systems

Liu, Qi; Yu, Hongbin; Chen, Peiyan

doi:10.1002/qj.2196

Cited by 48 publications

(58 citation statements)

References 29 publications

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“…The SEAV method used in this article is a selective ensemble-mean technique for TC track prediction proposed by Qi et al in 2014. It uses short lead time (SLT) position errors from every ensemble member to compose an ensemble mean for a long lead time (LLT) track forecast with a basic premise that members with smaller SLT errors are likelier to yield more accurate forecasts at LLT. The specific operational procedures are shown by the following steps ( Figure 1): (i) Due to data communication and computational requirements, EPS products often reach forecasters with a 'delay time' which is more than 6 h in operational work.…”

Section: Seav Methodologymentioning

confidence: 99%

“…Additionally, the method produced better results for a multi‐model ensemble than for a single‐model ensemble. Qi et al () proposed a selective ensemble‐mean technique for TC track forecasting from the European Centre for Medium‐range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), US National Centers for Environmental Prediction (NCEP) and China Meteorological Administration (CMA) for 35 TCs in the WNP from 2010 to 2011. The results showed that the means of selected EPS members (SEAV) for the JMA EPS was the most skilful of the tested methods.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the method produced better results for a multimodel ensemble than for a single-model ensemble. Qi et al (2014) proposed There are numerous studies on multi-model ensembles composed of various deterministic predictions from different NWP models for TC prediction. The results showed that average TC track forecasts calculated from several numerical models are generally more accurate than individual member model forecasts in both the WNP and the Atlantic basins (Goerss et al, 2004;Sampson et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Selective ensemble‐mean technique for tropical cyclone track forecast by using time‐lagged ensemble and multi‐centre ensemble in the western North Pacific

Liu

Xiaogang

2016

Quart J Royal Meteoro Soc

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This article investigates the impacts of both the time‐lagged ensemble technique and the multi‐centre ensemble prediction system (EPS) on tropical cyclone (TC) track forecasts. Four EPSs from European Centre for Medium‐range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP) and China Meteorological Administration (CMA) participating in the TIGGE project are considered, and 60 TCs in the western North Pacific from 2010 to 2012 are investigated. The time‐lagged ensemble uses single‐centre EPS (SCE) initialized at different times to increase ensemble members for specific forecast times. Both the means of selected EPS members (SEAV) method and the ensemble‐mean method are applied to the time‐lagged ensemble, respectively. Verification results show that the time‐lagged ensemble yields marginal changes in forecast position errors compared with its zero‐lag counterparts for all SCEs by applying the SEAV method. The mean forecast position errors by the ensemble‐mean method are greater when adding previous data compared with the SEAV method. A variety of multi‐centre EPSs (MCEs) composed of SEAV‐integrated SCEs are investigated. These MCEs, labelled according to their component SCEs, are EC&NCEP, EC&CMA, EC&JMA, EC&NCEP&CMA, EC&NCEP&JMA and EC&NCEP&CMA&JMA. The EC&NCEP MCE, for example, is a SEAV‐integrated combination of the ECMWF and NCEP ensemble TC forecast tracks. It is found that MCE track forecasts are more accurate than SCE track forecasts. EC&NCEP has the best performance among all MCEs. In addition, MCEs are compared with an equally weighted consensus of deterministic model forecasts (MCDs). The MCEs outperform their corresponding MCDs at 24 h. Sensitivity tests on the weights of component SCEs in SEAV‐derived MCEs are conducted. For lead times within 72 h, if the component SCEs in an MCE have comparable performances, the MCE with equal weights of SCEs will be more accurate than those of non‐equal weights of SCEs. Otherwise, non‐equal weights of SCEs are more suitable for MCEs. However, for lead times longer than 72 h, the above result is not convincing.

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Section: Seav Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selective ensemble‐mean technique for tropical cyclone track forecast by using time‐lagged ensemble and multi‐centre ensemble in the western North Pacific

Liu

Xiaogang

2016

Quart J Royal Meteoro Soc

Self Cite

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“…EA can be a means to reduce errors further (Mackey and Krishnamurti, 2001;Qi et al, 2014). To test the EA effect on the TAR prediction, the TARs were averaged at each station by sequentially increasing the number of ensemble members from the typhoon of a higher track similarity, and they were then compared with the observations.…”

Section: Effects Of Track Similarity Ensemble Averaging (Ea) and Imentioning

confidence: 99%

Statistical prediction of typhoon‐induced accumulated rainfall over the Korean Peninsula based on storm and rainfall data

Kim

Moon

Kim

2019

Meteorological Applications

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A statistical model for typhoon‐induced accumulated rainfall (TAR) prediction over the Korean Peninsula using track, intensity and rainfall data of 91 typhoons affecting the peninsula during the period 1977–2014 is developed. The statistical estimation of the TAR consists of three steps: (1) estimating the TAR at 56 observational weather stations for the 91 typhoons; (2) selecting typhoons whose tracks are similar to that of the target typhoon within the area of 32–40 ° N and 120–138 ° E using a fuzzy C‐mean clustering method; and (3) calculating the mean TAR for the 16 selected typhoons based on track similarity after an intensity correction of the TAR using a linear regression between the TAR and intensity anomaly. To validate the model, real case predictions were performed on typhoons Chan‐hom and Goni in 2015 and compared with the observed TARs as well as with those from local and global operational models. The result showed that when the best‐track data are used, the present statistical model can predict the TAR with the accuracy of mean root mean square errors (RMSEs) of 33 mm (Chan‐hom) and 29 mm (Goni) at the 56 stations, which were much less than the results of the local model. With the predicted track and intensity data for the two typhoons, the present model also showed an overall good performance with RMSEs of 30–34 mm (Chan‐hom) and 29–49 mm (Goni), depending on the accuracy of the predicted track and intensity, which were generally less than the results of the global model.

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“…Advanced and new techniques are constantly emerging in predicting TC tracks. For example, Qi et al (2014) proposed a selective ensemble mean technique for TC track forecast based on the errors of ensemble prediction system members at short lead times. Similarly, a method of retrieving optimum information of typhoon tracks in a multi-model ensemble of forecasts is explored by Tien et al (2012).…”

Section: Introductionmentioning

confidence: 99%

Multi-model ensemble forecasts of tropical cyclones in 2010 and 2011 based on the Kalman Filter method

Zhi

You

et al. 2015

Meteorol Atmos Phys

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This study conducted 24- to 72-h multi-model ensemble forecasts to explore the tracks and intensities (central mean sea level pressure) of tropical cyclones (TCs). Forecast data for the northwestern Pacific basin in 2010 and 2011 were selected from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency, and National Centers for Environmental Prediction datasets of the Observing System Research and Predictability Experiment Interactive Grand Global Ensemble project. The Kalman Filter was employed to conduct the TC forecasts, along with the ensemble mean and super-ensemble for comparison. The following results were obtained: (1) The statistical–dynamic Kalman Filter, in which recent observations are given more importance and model weighting coefficients are adjusted over time, produced quite different results from that of the super-ensemble. (2) The Kalman Filter reduced the TC mean absolute track forecast error by approximately 50, 80 and 100 km in the 24-, 48- and 72-h forecasts, respectively, compared with the best individual model (ECMWF). Also, the intensity forecasts were improved by the Kalman Filter to some extent in terms of average intensity deviation (AID) and correlation coefficients with reanalysis intensity data. Overall, the Kalman Filter technique performed better compared to multi-models, the ensemble mean, and the super-ensemble in 3-day forecasts. The implication of this study is that this technique appears to be a very promising statistical–dynamic method for multi-model ensemble forecasts of TCs. © 2015 Springer-Verlag Wie

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Selective ensemble‐mean technique for tropical cyclone track forecast by using ensemble prediction systems

Cited by 48 publications

References 29 publications

Selective ensemble‐mean technique for tropical cyclone track forecast by using time‐lagged ensemble and multi‐centre ensemble in the western North Pacific

Selective ensemble‐mean technique for tropical cyclone track forecast by using time‐lagged ensemble and multi‐centre ensemble in the western North Pacific

Statistical prediction of typhoon‐induced accumulated rainfall over the Korean Peninsula based on storm and rainfall data

Multi-model ensemble forecasts of tropical cyclones in 2010 and 2011 based on the Kalman Filter method

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