集合预报的现状和前景
PRESENT SITUATION AND PROSPECTS OF ENSEMBLE NUMERICAL PREDICTION
-
摘要: 综合论述了近年来已在国际上引起高度重视的新一代动力随机预报方法 ——— 集合预报。 随着计算机技术的迅猛发展和由于大气初值和数值模式中物理过程存在着不确定性的事实, 这一方法无疑代表了数值天气预报未来演变发展的方向。 未来的天气预报产品预计将从“决定论”的预报转变为“随机论”的预报来正确地表达mobilesport365_365游戏盒子_28365备用网址官方网站科学中这一所谓“可预报性问题”, 以便更好地为用户服务。 文中扼要地叙述了集合预报的概念、基本问题及其最新的研究动态和发展, 包括(1)如何建立和评估一个集合预报系统;(2)如何正确地表征大气初值和模式物理过程的不确定性与随机性;(3)如何从集合预报中提炼有用的预报信息和合理地解释、检验集合预报的产品, 特别是概率预报。 除了直接在天气预报上的应用, 还提到集合预报在mobilesport365_365游戏盒子_28365备用网址官方网站观测和资料同化方面应用的动态, 以引起有关研究人员的注意。Abstract: Over the past few years ensemble prediction has come to the fore as a major element in defining the future of numerical weather prediction (NWP) and operational weather forecasting. This stems basically from convergence of increasing recognition of the importance of explicitly addressing the intrinsic uncertainties in forecasts (originated from both initial conditions and model physics) with rapid advance in expanding capability to provide quantitative estimates of those uncertainties. It is widely agreed that ensemble based probabilities and measures of confidence hold the best potential for enhancing the ability to make user dependent informed decisions. Indeed, the U. S. National Weather Service is requiring that many forecast products evolve to probabilistic in nature, especially for quantitative precipitation forecasting. In this paper, the basic concepts, outstanding issues and recent development of ensemble technique are briefly described, which include (1) how to establish and validate an ensemble forecasting system; (2) how to correctly represent intrinsic uncertainties in both initial conditions and model physics; and (3) how to extract useful information out of an ensemble of forecasts and how to interpret and evaluate ensemble products especially probabilistic forecasts. Besides its application to direct weather forecasting, application of ensemble technique to adaptive observation and data assimilation are also mentioned.
-
Key words:
- Ensemble prediction;
- Deterministic;
- Stochastic;
- Uncertainties
-
[1] Lorenz E N. A study of the predictability of a 28-variable atmospheric model. Tellus, 1965, 17: 321-333. https://www.researchgate.net/publication/227661690_A_Study_of_the_Predictability_of_a_28-Variable_Atmospheric_Model [2] Epstein E S. Stochastic dynamic prediction. Tellus, 1969, 21, 739-759. [3] Leith C E. Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 1974, 102:409-418. doi: 10.1175/1520-0493(1974)102<0409:TSOMCF>2.0.CO;2 [4] 杜钧. 集合预报概论. 东亚季风和中国暴雨, 北京:mobilesport365_365游戏盒子_28365备用网址官方网站出版社, 1998. 457-462. [5] Mullen S L, Du J. Monte Carlo forecasts of explosive cyclogenesis with a limited-area, mesoscale model. Preprints, 10th Conference on Numerical Weather Prediction, Portland, Oregon, Amer. Meteor. Soc., 1994. 638-640. [6] Stensrud D J, Bao J, Warner T. Using initial condition and model physics perturbation in short-range ensemble simulations of mesoscale connective system. Mon. Wea Rev., 2000, 128:2077-2107. doi: 10.1175/1520-0493(2000)128<2077:UICAMP>2.0.CO;2 [7] Mylne K R, Evans R E, Clark R T. Multi-model multi-analysis ensemble forecasting in quasi-operational medium range forecasting. submitted to Quart. J. Roy. Meteor. Soc., 2000. [8] Talagrand O, Vautard R, Strauss B. Evaluation of probabilistic prediction systems. Proc. ECMWF Workshop on Predictability, Reading, United Kingdom, ECMWF, 1997. 1-26. [9] Hamill T M, Colucci S J. Verification of Eta-RSM short-range ensemble forecasts. Mon. Wea Rev., 1997, 125:1312-1327. doi: 10.1175/1520-0493(1997)125<1312:VOERSR>2.0.CO;2 [10] Whitaker J S, Loughe A F. The relationship between ensemble spread and ensemble mean skill. Mon. Wea Rev., 1998, 126:3292-3302. doi: 10.1175/1520-0493(1998)126<3292:TRBESA>2.0.CO;2 [11] Stensrud D J, Brooks J H E, Du J, Tracton M S, Rogers E. Using ensembles for short-range forecasting. Mon. Wea Rev., 1999, 127:433-446. doi: 10.1175/1520-0493(1999)127<0433:UEFSRF>2.0.CO;2 [12] Mullen S L, Baurahefner D P. Monte Carlo simulations of explosive cyclogenesis. Mon. Wea Rev., 1994, 122:1548-1567. doi: 10.1175/1520-0493(1994)122<1548:MCSOEC>2.0.CO;2 [13] Du, J, Mullen S L, Sanders F. Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 1997, 125:2427-2459. doi: 10.1175/1520-0493(1997)125<2427:SREFOQ>2.0.CO;2 [14] Tracton M S, Du J. Short-range ensemble forecasting (SREF) at the National Centers for Environment Prediction. Preprints of 12th Conference on Numerical Weather Prediction, Phoenix, Arizona, Amer. Meteor. Soc., 1998. 269-272. [15] Molteni F, Palmer T N, Buizza R, Pertroliagis T. The ECMWF ensemble prediction system methodology and verification. Quart. J. Roy. Met. Soc., 1996, 122:73-121. doi: 10.1002/(ISSN)1477-870X [16] Toth Z, Kalnay E. Ensemble forecasting at NMC, the generation of perturbations. Bull. Amer. Meteor. Soc., 1993, 74:2317-2330. doi: 10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2 [17] Toth Z, Kalnay E. Ensemble forecasting at NCEP and the breeding method. Mon. Wea Rev., 1997, 125:3297-3319. doi: 10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2 [18] Barkmeijer J, Buizza R, Palmer T N. 3D-Var Hussein singular vectors and their potential use in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor, Soc., 1999, 125:2333-2351. doi: 10.1002/(ISSN)1477-870X [19] Houtekamer P L, Derome J. Methods for ensemble prediction. Mon. Wea Rev., 1995, 123: 2181-2196. doi: 10.1175/1520-0493(1995)123<2181:MFEP>2.0.CO;2 [20] Sindic-Rancic G, Toth Z, Lalnay E. Storm scale ensemble experiments with the ARPS model preliminary results. Preprints, 12th Conference on Numerical Weather Prediction, Phoenix, Arizona, Amer. Meteor. Soc. 1998. 279-280. [21] Mullen S L, Du J, Sanders F. The dependence of ensemble dispersion on analysis forecast system implications to short-range ensemble forecasting of precipitation. Mon. Wea Rev., 1999, 127:1674-1686. doi: 10.1175/1520-0493(1999)127<1674:TDOEDO>2.0.CO;2 [22] Hamill T M, Colucci S J. Perturbations to the land-surface condition in short-range ensemble forecasts. Preprints, 12th Conference on Numerical Weather Prediction, Phoenix, Arizona, Amber, Meteor. Soc., 1998a. 273-276. [23] Buizza R, Miller M, Palmer T N. Stochastic representation of model uncertainties in the ECMWF EPS. Quart. J. Roy. Meteor, Soc., 1999, 125:2887-2908. doi: 10.1002/qj.49712556006 [24] Houtekamer P L, Lefaivre L, Derome J, et al. A system simulation approach to ensemble prediction. Mon. Wea Rev., 1996, 124: 1225-1242. doi: 10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2 [25] Stensrud D J, Bao J, Warner T. Using initial condition and model physics perturbation in short-range ensemble simulations of mesoscale connective system. Mon. Wea Rev., 2000, 128:2077-2107. doi: 10.1175/1520-0493(2000)128<2077:UICAMP>2.0.CO;2 [26] Mylne K R. Decision-making from probability forecasts using calculations of forecast value. Submitted to Meteorl. Appl. 2000. [27] Hou D, Kalnay E, Droegemeler K K. Objective verification of the SAMEX'98 ensemble forecasts. Mon. Wea. Rev., 2001, 129:73-91. doi: 10.1175/1520-0493(2001)129<0073:OVOTSE>2.0.CO;2 [28] Du J, Tracton M S. Impact of lateral boundary conditions on regional-model ensemble prediction. In: H. Ritchie, ed. Research Activities in Atmospheric and Oceanic Modeling. Report 28, CAS/JSC Working Group Numerical Experimentation (WGNE), WMO/TD-No. 1999. [29] Warner T T, Peterson R A, Treadon R E. A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical prediction. Bull. Amer. Meteor. Soc. 1997, 78: 2599-2617. doi: 10.1175/1520-0477(1997)078<2599:ATOLBC>2.0.CO;2 [30] Zhang Z, Krishnamurti T N. Ensemble forecasting of hurricane tracks. Bull. Amer. Meteor. Soc., 1998, 78:2785-2795. [31] Eckel F A, Walter M K. Calibrated probabilistic quantitative precipitation forecasts based on the MRF ensemble. Wea. Forecasting, 1998, 13:1132-1147. doi: 10.1175/1520-0434(1998)013<1132:CPQPFB>2.0.CO;2 [32] Zhu Y, Toth A, Kalnay E, et al. Probabilistic quantitative precipitation forecasts based on the NCEP global ensemble. Preprints, 16th Conference on Weather Analysis and Forecasting, Phoenix, Arizona, Amer. Meteor. Soc., 1998. 8-11. [33] Aberson S D, Lord J, DeMaria M, et al. Short-range ensemble forecasting of hurricane tracks. Preprints. The 21st Conference on Hurricanes and Tropical Meteorology, Miami, Florida, Amer. Meteor, Soc., 1995. 494-496. [34] Aberson S D, Bender M A, Tuleya R E. Ensemble forecasting of tropical cyclone tracks. Preprints. The 12nd Conference on Numerical Weather Prediction, Phoenix, Arizona, Amer. Meteor. Soc., 1998a. 290-292. [35] Aberson S D, Bender M A, Tuleya R E. Ensemble forecasting of tropical cyclone intensity. Preprints, Symposium on Tropical Cyclone Intensity Change, Phoenix, Arizona, Amer. Meteor. Soc., 1998b. 150-153. [36] Cheung K K W, Chan J C L. Ensemble forecasting of tropical cyclone motion using a barotropical model, Part I:Perturbations of the environment. Mon. Wea. Rev., 1999a, 127: 1229-1243. doi: 10.1175/1520-0493(1999)127<1229:EFOTCM>2.0.CO;2 [37] Cheung K K W, Chan J C L. Ensemble forecasting of tropical cyclone motion using a barotropical model. Part II:Perturbations of the vertex. Mon. Wea. Rev., 1999b, 127:2617-2640. [38] Du J. Applications of Monte Carlo method in short-range weather forecasting precipitation and connective activity. Preprints, 13 th Conference on Probability and Statistics in the Atmospheric Sciences, San Francisco, California, Amer. Meteor. Soc., 1996. 281-290. [39] Hamill T M, Colucci S J. Evaluation of Eta-RSM ensemble probabilistic precipitation forecasts. Mon. Wea. Rev., 1998b, 126:711-724. doi: 10.1175/1520-0493(1998)126<0711:EOEREP>2.0.CO;2 [40] Du J, Mullen S L, Sanders F. Removal of distortion error from an ensemble forecast. Mon. Wea. Rev., 2000, 128:2427-3351. [41] Wilks D S. Statistical Methods in the Atmospheric Sciences. Academic Press, 1995. 467. [42] Murphy A H. A new vector partition of the probability score. J. Appl. Meteor., 1975, 12:595-600. https://www.researchgate.net/publication/234395762_A_New_Vector_Partition_of_the_Probability_Score [43] Murphy A H. The value of climatological, categorical and probabilistic forecasts in the cost-lost situation. Mon. Wea Rev., 1977, 105:803-816. doi: 10.1175/1520-0493(1977)105<0803:TVOCCA>2.0.CO;2 [44] Richardson D. Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Met. Soc., 2000, 126(Part B):649-667. http://www.citeulike.org/user/eimaj42jdp/article/244699 [45] Toth Z. Ensemble forecasting in WRF. Bull. Amer. Meteor. Soc. 2001, 82:695-697. doi: 10.1175/1520-0477(2001)082<0695:MSEFIW>2.3.CO;2 [46] Buizza R. Potential forecast skill of ensemble prediction and spread and skill distributions of the ECMWF ensemble prediction system. Mon. Wea Rev., 1997, 125:99-119. doi: 10.1175/1520-0493(1997)125<0099:PFSOEP>2.0.CO;2 [47] Evans R E, Harrison M S J, Graham R J, et al. Joint medium-range ensembles from the Met. Office and ECMWF systems. Mon. Wea Rev., 2000, 128:3104-3127. doi: 10.1175/1520-0493(2000)128<3104:JMREFT>2.0.CO;2 [48] Brooks H E, Tracton M S, Stensrud D J, et al. Short-range ensemble forecasting (SREF). Report from a workshop. Bull. Amer. Met, Soc., 1995, 76: 1617-1624. [49] Du J, Mullen S L. Application of MM4 in short-range ensemble forecasting of 2nd-order physical variables. Preprints, 5th Workshop on PSU/NCAR Mesoscale Modeling System, Boulder, Colder, Colorado, MMM, NCAR, 1995. 9-10. [50] Du J, Tracton M S. Implementation of real-time short-range ensemble forecasting system at NCEP, an update. Preprints, 9th Conference on Mesoscale Processes, Ft. Lauderdale, Florida, Amer. Meteor. Soc., 2000. in press. [51] Black T L. The new NMC mesoscale Eat model description and forecast examples. Wea. Forecasting, 1994, 9:265-278. doi: 10.1175/1520-0434(1994)009<0265:TNNMEM>2.0.CO;2 [52] Juang H M, Kanamitsu. The NMC nested regional spectral model. Mon. Wea Rev., 1994, 122:3-26. doi: 10.1175/1520-0493(1994)122<0003:TNNRSM>2.0.CO;2 [53] Benjamin S G, et al. The operational RUC-2. Preprints, 16th Conference on Weather Analysis and Forecasting, Phoenix, Arizona, Amer. Meteor. Soc., 1998. 249-252. [54] Tracton M S, Du J. Application of the NCEP/EMC short-range ensemble forecast system (SREF) to predicting extreme precipitation events. Preprints, Symposium on Precipitation Extremes, Prediction, Impacts, and Responses, Albuquerque, New Mexico, Amer. Meteor. Soc. 2001. [55] Krzysztofowicz R. Probabilistic hydro meteorological forecasts toward a new era in operational forecasting. Bull. Amer. Soc., 1998, 79:243-251. doi: 10.1175/1520-0477(1998)079<0243:PHFTAN>2.0.CO;2 [56] NWS. NWS Vision 2005-National Weather Service Strategic Plan for Weather, Water, and Climate Services 2000-2005. Maryland:NWS, 1999. [57] Toth Z, Kalnay E, Wobus R. On the economic value of ensemble based weather forecasts. Bull. Amer. Meteor. Soc., 2001, 82. in press. [58] Hamill T M, Mullen S L, Snyder C, et al. Ensemble forecasting in the short to medium range. Report from a workshop. Bull Amer. Met. Soc., 2000, 81. [59] Toth Z, et al. Ensemble-based targeted observations during FASTEX. Preprints, 12th Conference on Numerical Weather Prediction, Phoenix. Arizona, Amer. Meteor. Soc. 1998. 24-27. [60] Houtekamer P L, Mitchell H L. Data assimilation using ensemble Kalman filter technique. Mon . Wea Rev., 1998, 126:796-811. doi: 10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2 [61] Hamill T M, Synder C. A hybrid ensemble Kalman filter-three-dimensional variational analysis scheme. Mon. Wea. Rev., 2000, 128:2905-2919. doi: 10.1175/1520-0493(2000)128<2905:AHEKFV>2.0.CO;2
点击查看大图
计量
- 摘要浏览量: 6860
- HTML全文浏览量: 3787
- PDF下载量: 5417
- 被引次数: 0