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Verification of tropical cyclones(TC)wind structure forecasts from global NWP models and ensemble prediction systems(EPSs)

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Tropical Cyclone Research and Review 2022年第2期11卷 88-102页

作者： Xiaoqin Lu Wai Kin Wong Kin Chung Au-Yeung Chun Wing Choy Hui Yu Shanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique China Meteorological AdministrationShanghaiChina Hong Kong Observatory Hong KongChina

Forecasting wind structure of tropical cyclone(TC)is vital in assessment of impact due to high winds using Numerical Weather prediction(NWP)*** usual verification technique on TC wind structure forecasts are based on grid-to-grid comparisons between forecast field and the actual ***,precision of traditional verification measures is easily affected by small scale errors and thus cannot well discriminate the accuracy or effectiveness of NWP model *** this study,the Method for Object-Based Diagnostic Evaluation(MODE),which has been widely adopted in verifying precipitation fields,is utilized in TC’s wind field verification for the first *** TC wind field forecast of deterministic NWP model and ensemble prediction system(EPS)of the European Centre for Medium-Range Weather Forecasts(ECMWF)over the western North Pacific and the South China Sea in 2020 were evaluated.A MODE score of 0.5 is used as a threshold value to represent a skillful(or good)*** is found that the R34(radius of 34 knots)wind field structure forecasts within 72 h are good regardless of DET or *** performance of R50 and R64 is slightly worse but the R50 forecasts within 48 h remain good,with MODE exceeded *** R64forecast within 48 h are worth for reference as well with MODE of around *** study states that the TC wind field structure forecast by ECMWF is skillful for TCs over the western North Pacific and the South China Sea.

关键词： Verification Tropical cyclones wind structure forecasts Numerical weather prediction models ensemble prediction system

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Preliminary Comparison of the CMA,ECMWF,NCEP,and JMA ensemble prediction systems

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Acta meteorologica Sinica 2012年第1期26卷 26-40页

作者：段明铿麻巨慧王盘兴 Key Laboratory of Meteorological Disaster of Ministry of Education Nanjing University of Information Science & Technology

Based on The Observing system Research and Predictability Experiment （THORPEX） Interactive Grand Global ensemble （TIGGE） dataset, using various verification methods, the performances of four typical ensemble prediction systems （EPSs） from the China Meteorological Administration （CMA）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, the US National Centers for Environmental Pre- diction （NCEP）, and the Japan Meteorological Agency （JMA） are compared preliminarily. The verification focuses on the 500-hPa geopotential height forecast fields in the mid- and higb-latitude Eurasian region during July 2007 and January 2008. The results show that for the forecast of 500-hPa geopotential height, in both summer and winter, the ECMWF EPS exhibits the highest forecast skill, followed by that of NCEP, then by JMA, and the CMA EPS gets in the last. The better system behaviors benefit from the better com- bination of the following： data assimilation system, numerical models, initial perturbations, and stochastic model perturbations. For the medium-range forecast, the ensemble forecasting can effectively filter out the forecast errors associated with the initial uncertainty, and the reliability and resolution （the two basic attri- butions of the forecast system） of these EPSs are better in winter than in summer. Specifically, the CMA EPS has certain advantage on the reliability of ensemble probabilistic forecasts. The forecasts are easy to be underestimated by the JMA EPS. The deficiency of ensemble spread, which is the universal problem of El＇S, also turns up in this study. Although the systems of ECMWF, NCEP, and JMA have more ensemble mem- bers, this problem cannot be ignored. This preliminary comparison helps to further recognize the prediction capability of the four EPSs over the Eurasian region, provides important references for wide applications of the TIGGE dataset, and supplies useful information for improving the CMA EPS.

关键词： TIGGE ensemble prediction system comparison verification

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A Comparison of Breeding and ensemble Transform Vectors for Global ensemble Generation

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Acta meteorologica Sinica 2012年第1期26卷 52-61页

作者：邓国田华李晓莉陈静龚建东矫梅燕 National Meteorological Center China Meteorological Administration China Meteorological Administration

To compare the initial perturbation techniques using breeding vectors and ensemble transform vectors, three ensemble prediction systems using both initial perturbation methods but with different ensemble member sizes based on the spectral model T213/L31 are constructed at the National Meteorological Center, China Meteorological Administration （NMC/CMA）. A series of ensemble verification scores such as forecast skill of the ensemble mean, ensemble resolution, and ensemble reliability are introduced to identify the most important attributes of ensemble forecast systems. The results indicate that the ensemble transform tech- nique is superior to the breeding vector method in light of the evaluation of anomaly correlation coefficient （ACC）, which is a deterministic character of the ensemble mean, the root-mean-square error （RMSE） and spread, which are of probabilistic attributes, and the continuous ranked probability score （CRPS） and its decomposition. The advantage of the ensemble transform approach is attributed to its orthogonality among ensemble perturbations as well as its consistence with the data assimilation system. Therefore, this study may serve as a reference for configuration of the best ensemble prediction system to be used in operation.

关键词： breeding ensemble transform ensemble prediction system

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ensemble Hindcasts of ENSO Events over the Past 120 Years Using a Large Number of ensembles

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Advances in Atmospheric Sciences 2009年第2期26卷 359-372页

作者：郑飞朱江王慧 Rong-Hua ZHANG International Center for Climate and Environment Science （ICCES） Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry （ LAPC） Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 National Meteorological Center Beijing 100081 Earth system Science Interdisciplinary Center （ESSIC） University of Maryland College Park Maryland USA

Based on an intermediate coupled model （ICM）, a probabilistic ensemble prediction system （EPS） has been developed. The ensemble Kalman filter （EnKF） data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886-2005 using the EPS with 100 ensemble members and with initial conditions obtained by only assimilating historic SST anomaly observations. By examining the retrospective ensemble forecasts and available observations, the verification results show that the skill of the ensemble mean of the EPS is greater than that of a single deterministic forecast using the same ICM, with a distinct improvement of both the correlation and root mean square （RMS） error between the ensemble-mean hindcast and the deterministic scheme over the 12-month prediction period. The RMS error of the ensemble mean is almost 0.2℃ smaller than that of the deterministic forecast at a lead time of 12 months. The probabilistic skill of the EPS is also high with the predicted ensemble following the SST observations well, and the areas under the relative operating characteristic （ROC） curves for three different ENSO states （warm events, cold events, and neutral events） are all above 0.55 out to 12 months lead time. However, both deterministic and probabilistic prediction skills of the EPS show an interdecadal variation. For the deterministic skill, there is high skill in the late 19th century and in the middle-late 20th century （which includes some artificial skill due to the model training period）, and low skill during the period from 1906 to 1961. For probabilistic skill, for the three different ENSO states, there is still a similar interdecadal variation of ENSO probabilistic predictability during the period 1886~2005. There is high skill in the late 19th centu

关键词： ENSO ensemble prediction system interdecadal predictability hindcast

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