延伸期预报中的可预报性浅析
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摘要
延伸期(10～30 d)预报是无缝隙集约化预报预测业务体系的重要一环,一直以来在防灾减灾科学决策中扮演着重要作用。然而在实际预报中,其准确率较中短期天气预报和短期气候预测均明显偏低,原因在于随着预报时效延长,预报结果存在明显的不确定性。因此,如何在业务预报中评估所预报对象的可预报性至关重要。本文简述延伸期时效可预报性的主要来源以及国际主流数值模式的预报性能,阐述了预报业务中如何对可预报性进行评估,介绍延伸期预报产品的应用和发展趋势,以期为延伸期预报服务能力进一步提升提供参考。
The extended forecast(10-30 days) is an important part of the seamless intensive forecasting and forecasting business system, and has played an important role in the scientific decision-making of disaster prevention and reduction. However, in the actual forecast, the accuracy of extended forecast is significantly lower than the short, medium-range weather forecasts and climate predictions. The reason is that there are obvious uncertainties as the forecast leading time increases.Therefore, the predictability of forecast objects needs to be fully considered in operational forecasting.This paper introduces the source of predictability of extended period, and thestate of the art in performance of mainstream numerical models, introduces the ideas in operational forecast and some predictiveproducts commonly referenced by forecaster, and reveals the application of predictability theory in extended period forecast. Meanwhile, this study also provides some clues for the future development direction.
The extended forecast(10-30 days) is an important part of the seamless intensive forecasting and forecasting business system, and has played an important role in the scientific decision-making of disaster prevention and reduction. However, in the actual forecast, the accuracy of extended forecast is significantly lower than the short, medium-range weather forecasts and climate predictions. The reason is that there are obvious uncertainties as the forecast leading time increases.Therefore, the predictability of forecast objects needs to be fully considered in operational forecasting.This paper introduces the source of predictability of extended period, and thestate of the art in performance of mainstream numerical models, introduces the ideas in operational forecast and some predictiveproducts commonly referenced by forecaster, and reveals the application of predictability theory in extended period forecast. Meanwhile, this study also provides some clues for the future development direction.
引文
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[18]Ineson S,Scaife A A,Knight J R,et al.Solar forcing of winter climate variability in the Northern Hemisphere[J].Nat Geosci,2011,4:753-757.
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[27]Baxter S,Nigam S.A subseasonal teleconnection analysis:PNA development and its relationship to the NAO[J].Climate,2013,26(18):6733-6741.
[28]Annarita Mariotti,Paolo M.Ruti&Michel Rixen.Progress in subseasonal to seasonal prediction through a joint weather and climate community effort[J].Climate and Atmospheric Science,2018.https://doi.org/10.1038/s41612-018-0014-z.
[29]金荣花,马杰,毕宝贵.10-30天延伸期预报研究进展和业务现状[J].沙漠与绿洲气象,2010,4(2):1-5.
[30]Ferranti L,Corti S.New clustering products[J].ECMWFNewsletter No,2011,127:6-11.
[31]翟盘茂,倪允琪,陈阳.我国持续性重大天气异常成因与预报方法研究回顾与未来展望[J].地球科学进展,2013,28(11):1177-1188.
[32]Mc Govern A,Elmore K L,Gagne D J,ea al.Williams.Using artificial intelligence to improve real-time decisionmaking for high-impact weather[J].Bull Amer Meteor Soc,2017,98:2073-2090.
[33]王太微,陈德辉.数值预报发展新方向-集合数值预报[J].气象研究与应用,2007,28(1):6-12.
[34]矫梅燕,郑国光.现代天气业务[M].北京:气象出版社,2010.
[2]Lorenz E N.The predictability of hydrodynamic flow Trans New York Academy of Sciences[J].Ser.II,1963b,25:409-433.
[3]丑纪范.长期数值天气预报[M].北京:气象出版社,1986.
[4]丑纪范.大气动力学的新进展[M].兰州:兰州大学出版社,1990.
[5]丑纪范.大气科学中的非线性与复杂性[M].兰州:气象出版社,2002.
[6]巢纪平,季劲钧,何家骅,等.一种长期数值天气预报方法的物理基础[J].中国科学,1977(2):162-172.
[7]巢纪平,季劲钧,朱志辉,等.长期数值天气预报的滤波方法[J].中国科学,1979(1):75-84.
[8]袁为,杨海军.Madden-Julian振荡对中国东南部冬季降水的调制[J].北京大学学报(自然科学版),2010,46(2):207-214.
[9]Hoskins B J,Karoly D J.The Steady Linear Response of a Spherical Atmosphere to Thermal and Orographic Forcing[J].Journal of the Atmospheric Sciences,1981,38:1179-1196.
[10]Karoly D J.Southern Hemisphere circulation features associated with El Ni觡o-Southern Oscillation events[J].Climate,1989,2:1239-1252.
[11]Vitart,F.Ardilouze C,Bonet A,et al.The subseasonal to seasonal(S2S)prediction project database[J].Am Meteor Soc.2017,98:163-173.
[12]Koster R D,Coauthors.The second phase of the global land atmosphere coupling experiment:soil moisture contributions to subseasonal forecast skill[J].Hydrometeor,2011,12:805-822.
[13]Lin H,Wu Z.Contribution of the autumn Tibetan Plateau snow cover to seasonal prediction of North American winter temperature[J].Climate,2011,24:2801-2813.
[14]Jeong J-H,Linderholm H W,Woo S-H,et al.Impacts of snow initialization on subseasonal forecasts of surface air temperature for the cold season[J].Climate.2012,26:1956-1972.
[15]Kumar A,F Yang.Comparative influence of snow and SSTvariability on extratropical climate in northern winter[J].Climate,2003,16:2248-2261.
[16]Baldwin M P,Dunkerton T J.Propagation of the Arctic Oscillation from the stratosphere to the troposphere[J]J.Geophys Res.1999,30:937-946.
[17]Baldwin M P,Dunkerton T J.Stratospheric harbingers of anomalous weather regimes[J].Science,2002,294:581-584.
[18]Ineson S,Scaife A A,Knight J R,et al.Solar forcing of winter climate variability in the Northern Hemisphere[J].Nat Geosci,2011,4:753-757.
[19]Scaife A A,Folland C K,Alexander L V,ea tl.European climate extremes and the North Atlantic Oscillation[J].Climate,2008,21:72-83.
[20]Alexander M A,Bhatt U S,Walsh J E,et al.The atmospheric response to realistic Arctic sea ice anomalies in an AGCM during winter[J].Climate,2004,17:890-905.
[21]Holland M M,Stroeve J C.Changing seasonal sea ice predictor relationships in a changing Arctic climate[J].Geophys Res Lett,2011,38:L18501.
[22]Weber N,Mass C F.Evaluating CFSv2 subseasonal forecast skill with an emphasis on tropical convection[J].Mon Wea Rev,2017,145:3795-3815.
[23]Horel J D,Wallace J M,Planetary-scale atmospheric phenomena associated with the Southern Oscillation[J].Mon Wea Rev,1981,109,813-829.
[24]Wallace J M,Gutzler D S.Teleconnections in the geopotential height field during the Northern Hemisphere winter[J].Mon Wea Rev,1981,109:784-812.
[25]Barnston A G,Livezey R E.Classifications,seasonality,and persistence of low-frequency atmospheric circulation patterns[J].Monthly Weather Review,1987,115:1083-1126.
[26]Thompson D W,Wallace J M.The Arctic Oscillation signature in the wintertime geopotential height and temperature fields[J].Geophys Res Lett,1998,25:1297-1300.
[27]Baxter S,Nigam S.A subseasonal teleconnection analysis:PNA development and its relationship to the NAO[J].Climate,2013,26(18):6733-6741.
[28]Annarita Mariotti,Paolo M.Ruti&Michel Rixen.Progress in subseasonal to seasonal prediction through a joint weather and climate community effort[J].Climate and Atmospheric Science,2018.https://doi.org/10.1038/s41612-018-0014-z.
[29]金荣花,马杰,毕宝贵.10-30天延伸期预报研究进展和业务现状[J].沙漠与绿洲气象,2010,4(2):1-5.
[30]Ferranti L,Corti S.New clustering products[J].ECMWFNewsletter No,2011,127:6-11.
[31]翟盘茂,倪允琪,陈阳.我国持续性重大天气异常成因与预报方法研究回顾与未来展望[J].地球科学进展,2013,28(11):1177-1188.
[32]Mc Govern A,Elmore K L,Gagne D J,ea al.Williams.Using artificial intelligence to improve real-time decisionmaking for high-impact weather[J].Bull Amer Meteor Soc,2017,98:2073-2090.
[33]王太微,陈德辉.数值预报发展新方向-集合数值预报[J].气象研究与应用,2007,28(1):6-12.
[34]矫梅燕,郑国光.现代天气业务[M].北京:气象出版社,2010.