气候变化领域集成服务门户(CCPortal): Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction

CCPortal

DOI	10.1016/j.atmosres.2020.105077
	Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction
	Chakraborty P.; Sarkar A.; Kumar S.; George J.P.; Rajagopal E.N.; Bhatla R.
发表日期	2020
ISSN	0169-8095
卷号	244
英文摘要	Ensemble Prediction System (EPS) estimates the probability distribution of atmospheric states through multiple integrations of the Numerical Weather Prediction (NWP) model/s with number of perturbed initial conditions. The choice of an ensemble size can affect the skill of the ensemble forecast. Since more ensemble members require more computational requirements, it is important to determine whether the larger ensemble size significantly improves the forecast uncertainty estimates. The EPS at National Centre for Medium Range Weather Forecasting (NEPS) is assessed for the prediction of track and intensity of Tropical Cyclones (TC) over Bay of Bengal (BoB). Medium range forecast of four tropical cyclones over BoB with three different ensemble sizes namely E11 (11 perturbed members), E22 (22 perturbed members) and E44 (44 perturbed members) are presented here. The results of the study indicate that doubling of the ensemble population reduced the mean initial position errors by 25% and increase of ensemble size by four times (E11 to E44) reduced the errors by about 38%. The ensemble mean forecast track has lower errors compared to the deterministic forecast (CNTL) at shorter lead times for all ensemble sizes. The impact of ensemble size is significant on the improvement of mean track prediction skill at longer lead times. Median of central pressure error is very close to zero for all the sizes and the forecasts are slightly underestimated compared to the observed values. The initial error in Maximum Sustained surface Wind speed (MSW) is smaller for E44 as compared to E22 and E11. Interestingly, in all lead time forecasts and all the ensemble sizes, ensemble mean MSW errors are nearly within ±5 ms−1. There is only slight improvement in the ensemble mean precipitation forecasts with the increase of ensemble size. NEPS fails to resolve the heavier rainfall distribution and has the tendency to over-predict light precipitation. Ensembles with larger size predict the TC strike area of intense storms with higher probabilities due to better consensus among member tracks fueled by improved steering flow. But few cases have shown E11 or E22 perform equally well in forecasting TC propagation and in some cases even better compared to E44. Threshold oriented verification was carried out for winds at 850 hPa considering all the cases collectively. Brier Score (BS) and its components are very close to one another for higher thresholds of winds. In general, E44 has lower BS for all different thresholds with small lead time forecasts. Smaller ensembles have slightly better reliability while large ensembles improved the resolution for lower thresholds of wind speed. Continuous ranked probability score indicate that predicted probability distribution agrees well with the analyzed cumulative distribution. Area under relative operating characteristic curve exhibits better discriminating ability of probabilistic events with larger ensemble sizes. This study clearly showed that an increase in the ensemble size has significant beneficial impact on the reduction of the percentage of outliers thereby improving the potential predictive skill of the unexpected events. © 2020 Elsevier B.V.
关键词	Errors Hurricanes Precipitation (meteorology)Probability distributions Storms Tropics Uncertainty analysis Wind Computational requirements Continuous ranked probability scores Cumulative distribution Discriminating abilities Ensemble prediction systems Numerical weather prediction models Relative operating characteristics Tropical cyclone predictions Weather forecasting ensemble forecasting precipitation intensity prediction storm track tropical cyclone weather forecasting wind velocity
语种	英语
来源机构	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/132392
推荐引用方式 GB/T 7714	Chakraborty P.,Sarkar A.,Kumar S.,et al. Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction[J]. Atmospheric Research,2020,244.
APA	Chakraborty P.,Sarkar A.,Kumar S.,George J.P.,Rajagopal E.N.,&Bhatla R..(2020).Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction.,244.
MLA	Chakraborty P.,et al."Assessment of NCMRWF Global Ensemble System with differing ensemble populations for Tropical cyclone prediction".244(2020).