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Three-dimensional Cloud Initial Field Created and Applied to GRAPES Numerical Weather Prediction Nowcasting
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摘要: 围绕GRAPES_Meso的云初始场形成，以ARPS模式云分析方案为基础，优化诊断后应用我国风云二号静止气象卫星云产品、多普勒天气雷达三维组网拼图产品等观测资料结合模式背景信息，根据云热力-动力学原理及观测试验经验关系等，对云初始场的信息进行分析并通过松弛逼近同化方法实现对云内微物理信息同化应用。GRAPES_Meso中采用优化后的云初始场方案，水平分辨率为0.03°×0.03°和0.1°×0.1°的1个月（2014年7月15日-8月14日）连续试验和个例分析结果显示：云初始场形成方案能够分析出飑线等天气系统的云系和云内微物理变量特征。从模拟云图看，包含云初始场信息的GRAPES_Meso的云系的形态特征和分布范围短时临近预报结果更为准确。云初始场信息同化应用后，在1 h的时间尺度上，即可预报出与实况更为接近的降水；0~12 h时间范围内对降水均有积极的影响，可满足短时临近预报的需求，降水量级略偏大。批量连续试验（水平分辨率为0.03°×0.03°和0.1°×0.1°）的各个量级降水ETS（equitable threat score）评分都显著提高。
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关键词:
- 云初始场形成;
- GRAPES_Meso;
- 短时临近预报
Abstract: In order to get more accurate cloud initial fields in GRAPES_Meso model, the ARPS cloud analysis scheme is introduced. With some modifications or improvements based on the rational law in cloud macro-characteristic and micro-characteristic, the cloud analysis scheme is used to set up a local cloud analysis scheme which suitable for domestic numerical weather prediction model and local synoptic observations. Based on the background field, it integrates data sources from Doppler weather radar three-dimensional mosaic reflectivity data, geostationary meteorological satellite data, and surface observation. The cloud initial information is analyzed based on cloud physical laws of thermodynamics and dynamics and the observed empirical relationship. After the cloud analysis, analyzed three dimensional fields which include information of cloud hydrometeors are introduced by nudging technique for initialization of GRAPES_Meso model. One-month (15 Jul 2014-14 Aug 2014) time serial of experiments in different horizontal resolution (0.03°×0.03°, 0.1°×0.1°) are designed to verify the performance of the cloud analysis scheme. Case study shows that cloud macro-characteristic and cloud initial hydrometeors of synoptic system, such as typhoon, squall line etc., could be represented better by using cloud analysis scheme. The satellite cloud simulation technology of university of Wisconsin is adopted to produce the satellite cloud simulation product, which is convenient to compare the cloud product of model output with FY-2 meteorological satellite cloud products. The comparing result shows that 1 h nowcasting cloud of GRAPES_Meso model with cloud information initialized is close to satellite measurment in cloud macro-characteristic and cloud spatial distribution, while the one without cloud information initialized is missing and the brightness temperature is higher than satellite measurment. Until 6 hours, the nowcasting cloud of cloud analysis scheme is more similar to satellite measurment than the one without cloud analysis scheme, and the brightness temperature simulation is reasonable. As for performance of precipitation forecast, it is found that forecast with the cloud analysis has a significant positive impact on short range precipitation forecast. The 1-hour precipitation forecast with cloud analysis is closer to observation, and the positive effect can last for over twelve hours, which meets the demand for the short time nowcasting operational system. Furthermore, the spin-up time is also shortened. In long time experiments, the statistical variable of equitable threat score (ETS) of the precipitation forecast is calculated. At the first 6 h forecast in horizontal resolution of both 0.03°×0.03° and 0.1°×0.1°, the ETS of the precipitation forecast with cloud analysis is obviously increased compared with the one without cloud analysis. In the following three 6 h forecast, the positive effects decrease as forecast time increasing.-
Key words:
- cloud initial field;
- GRAPES_Meso;
- nowcasting
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图 2 优化后的相对湿度与格点云量关系
Fig. 2 The relationship between relative humidity and grid cloud fraction
图 3 2014年7月18日12:00雷达组合反射率因子(a)和12:00-18:00 6 h实况降水(b)
Fig. 3 Radar composite reflectiveity at 1200 UTC 18 Jul 2014(a) and 6 h accumulate precipitation observation from 1200 UTC to 1800 UTC on 18 Jul 2014(b)
图 4 应用观测资料分布(a) FY-2E总云量, (b) FY-2E小时平均亮温, (c)雷达反射率因子
Fig. 4 Distribution of experiment data source (a) FY-2E total amount of cloud, (b) hourly TBB of FY-2E, (c) radar reflectivity factor
图 5 2014年7月18日12：00云初始场水物质垂直累积含量(单位：g·kg-1) (a)云水，(b)云冰，(c)雨水，(d)雪，(e)雹，(f)水汽
Fig. 5 Vertically integrated cloud hydrometer at 1200 UTC 18 Jul 2014(unit:g·kg-1) (a) cloud water, (b) cloud ice, (c) rain water, (d) snow, (e) graupel, (f) vapor
图 6 2014年7月18日FY-2E 6.8 μm水汽通道亮温(a)13：00观测，(b)13：00控制试验预报，(c)13：00敏感性试验预报，(d)15：00观测，(e)15：00控制试验预报，(f)15：00敏感性试验预报，(g)18：00观测，(h)18：00控制试验预报，(i)18：00敏感性试验预报
Fig. 6 TBB of FY-2E 6.8 μm vaper channel on 18 Jul 2014 (a) measurement at 1300 UTC, (b) control test forecast at 1300 UTC, (c) sensitivity test forecast at 1300 UTC, (d) measurement at 1500 UTC, (e) control test forecast at 1500 UTC, (f) sensitivity test forecast at 1500 UTC, (g) measurement at 1800 UTC, (h) control test forecast at 1800 UTC, (i) sensitivity test forecast at 1800 UTC
图 7 2014年7月18日FY-2E 11 μm红外通道亮温(a)13：00观测，(b)13：00控制试验预报，(c)13：00敏感性试验预报，(d)15：00观测，(e)15：00控制试验预报，(f)15：00敏感性试验预报，(g)18：00观测，(h)18：00控制试验预报，(i)18：00敏感性试验预报
Fig. 7 TBB of FY-2E 11 μm infrared channel 18 Jul 2014 (a) measurement at 1300 UTC, (b) control test forecast at 1300 UTC, (c) sensitivity test forecast at 1300 UTC, (d) measurement at 1500 UTC, (e) control test forecast at 1500 UTC, (f) sensitivity test forecast at 1500 UTC, (g) measurement at 1800 UTC, (h) control test forecast at 1800 UTC, (i) sensitivity test forecast at 1800 UTC
图 8 2014年7月18日雷达组合反射率因子(a)15：00观测，(b)15：00控制试验预报，(c)15：00敏感性试验预报，(d)17：00观测，(e)17：00控制试验预报，(f)17：00敏感性试验预报
Fig. 8 Radar composite reflectivity factor on 18 Jul 2014 (a) observation at 1500 UTC, (b) control test forecast at 1500 UTC, (c) sensitivity test forecast at 1500 UTC, (d) observation at 1700 UTC, (e) control test forecast at 1700 UTC, (f) sensitivity test forecast at 1700 UTC
图 9 2014年7月18日12:00-18:00模式预报6 h累积降水(单位：mm) (a)控制试验，(b)敏感性试验
Fig. 9 6 h accumulate precipitation from 1200 UTC to 1800 UTC on 18 Jul 2014 (a) control test forecast, (b) sensitivity test forecast
图 10 2014年7月15日-8月14日GRAPES_Meso (0.03°×0.03°)连续试验逐6 h累积降水ETS评分(a)0~6 h预报，(b)6~12 h预报，(c)12~18 h预报，(d)18~24 h预报
Fig. 10 ETS of GRAPES_Meso with 0.03°×0.03° batch experiments from 15 Jul to 14 Aug in 2014 (a) forecast of 0-6 h, (b) forecast of 6-12 h, (c) forecast of 12-18 h, (d) forecast of 18-24 h
图 11 2014年7月15日-8月14日GRAPES_Meso (0.1°×0.1°)连续试验逐6 h降水ETS评分(a) 0~6 h预报, (b)6~12 h预报, (c)12~18 h预报, (d)18~24 h预报
Fig. 11 ETS of GRAPES_Meso with 0.1°×0.1° batch experiments from 15 Jul to 14 Aug in 2014 (a) forecast of 0-6 h, (b) forecast of 6-12 h, (c) forecast of 12-18 h, (d) forecast of 18-24 h
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[1] 卞云.强对流天气为何频繁发生.生命与灾害, 2009(7):25-26. http://www.cnki.com.cn/Article/CJFDTOTAL-MFFF200907012.htm [2] 郑永光, 张小玲, 周庆亮, 等.强对流天气短时临近预报业务技术进展与挑战.气象, 2010, 36(7):33-42. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201007009.htm [3] Kalnay E.Atmospheric Modeling, Data Assimilation and Predictability.The Edinburgh Building, Cambridge CB2 2RU, UK.Cambridge:Cambridge University Press, 2003:69-90. [4] Wolcott S W, Warner T T.A humidity initialization utilizing surface and satellite data.Mon Wea Rev, 1981, 109:1989-1998. doi: 10.1175/1520-0493(1981)109<1989:AMAPUS>2.0.CO;2 [5] Krishnamurti T N, Ingles K, Coeke S, et al.Details of low latitude medium range numerical weather prediction using a global spectral model, PartⅡ:Effects of orography and physical initialization.J Meteor Soc Japan, 1984, 62:613-649. [6] Wu X, Diak G R, Hayden C M, et al.Short-range precipitation forecasts using assimilation of simulated satellite water vapor profiles and column liquid water amounts.Mon Wea Rev, 1995, 123:347-365. doi: 10.1175/1520-0493(1995)123<0347:SRPFUA>2.0.CO;2 [7] Kristjansson J E.Initialization of Cloud Water in a Weather Prediction Model//Preprints, 9th Conf on Numerical Weather Prediction.Amer Meteor Soc, 1991:823-824. [8] Xue M, Wang D, Hou D, et al.Prediction of the 7 May 1995 Squall Line Over the Central U S with Intermermittent Data Assimilation.12th Conf on Numerical Weather Prediction, Amer Mereor Soc, 1998:191-194. [9] 李永平, 朱国富, 薛纪善.应用雷达回波强度资料反演大气云微物理量.气象学报, 2004, 62(6):814-820. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200406009.htm [10] 李永平, 袁招洪, 王晓峰.用多普勒雷达反射率调整模式大气的云微物理变量.应用气象学报, 2004, 15(6):658-664. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040679&flag=1 [11] 杨毅.Doppler雷达资料同化技术研究.兰州:兰州大学, 2007. [12] 刘红亚, 徐海明, 薛纪善, 等.雷达反射率因子在中尺度云分辨率模式初始化中的应用.数值模拟试验Ⅰ:云微物理量和垂直速度的反演.气象学报, 2007, 65(6):896-905. [13] 刘红亚, 徐海明, 薛纪善, 等.雷达反射率因子在中尺度云分辨率模式初始化中的应用.数值模拟试验Ⅱ:数值模拟试验.气象学报, 2007, 65(6):906-918. [14] Xue M, Droegemeier K K, Wong V, et al.The advanced regional prediction system (ARPS)-Amultiscale nonhydrostatic atmospheric simulation and prediction tool.Part Ⅱ:Model physics and applications.Meteor Atmos Physics, 2001, 76:134-165. [15] 屈佑铭, 陆维松, 蔡荣辉, 等.GRAPES_Meso云分析系统的设计与试验.气象, 2010, 36(10):37-45. http://www.cnki.com.cn/Article/CJFDTotal-QXXX201010008.htm [16] 屈佑铭, 蔡荣辉, 朱立娟, 等.云分析系统在台风莫拉菲数值模拟中的应用.应用气象学报, 2012, 23(5):551-561. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120505&flag=1 [17] 朱立娟.GRAPES短临预报的云初始场形成与雷达VAD质控的关键技术研究.北京:中国气象科学研究院, 2012. [18] 薛纪善, 陈德辉.数值预报系统GRAPES的科学设计与应用.北京:科学出版社, 2008. [19] 陈德辉, 沈学顺.新一代数值预报系统GRAPES研究进展.应用气象学报, 2006, 17(6):773-777. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=200606125&flag=1 [20] 陈德辉, 杨学胜, 胡江林, 等.多尺度通用动力模式框架的设计策略.应用气象学报, 2003, 14(4):452-462. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030456&flag=1 [21] 胡江林, 沈学顺, 张红亮, 等.GRAPES模式动力框架的长期积分特征.应用气象学报, 2007, 18(3):276-284. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20070349&flag=1 [22] Koch S E, Aksakal A, McQueen J T.The influence of mesoscale humidity and evapotranspiration fields on a model forecast of a cold-frontal squall line.Mon Wea Rev, 1997, 125:384-409. doi: 10.1175/1520-0493(1997)125<0384:TIOMHA>2.0.CO;2 [23] Albers S C, McGinley J A, Birkenheuer D A, et al.The local analysis and prediction system (LAPS):Analysis of clouds, precipitation and temperature.Wea Forecasting, 1996, 11:273-287. doi: 10.1175/1520-0434(1996)011<0273:TLAAPS>2.0.CO;2 [24] Kessler E.On the distribution and continuity of water substance in atmospheric circulation.Meteor Monogr, 1969, 10(32):88. http://adsabs.harvard.edu/abs/1995AtmRe..38..109K [25] Tong M, Xue M.Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model:OSS Experiments.Mon Wea Rev, 2005, 133:1789-1807. doi: 10.1175/MWR2898.1 [26] 王红艳, 刘黎平, 王改利, 等.多普勒天气雷达三维数字组网系统开发及应用.应用气象学报, 2009, 20(2):214-224. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20090211&flag=1 [27] 肖艳娇, 刘黎平.新一代天气雷达组网资料的三维格点化及拼图方法研究.气象学报, 2006, 64(5):647-656. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGQX201009001013.htm [28] 许健民, 张文建, 杨军, 等.风云二号卫星业务产品与卫星数据格式实用手册.北京:气象出版社, 2008:359. [29] 中华人民共和国气象行业标准QX/T46-2007.地面气象观测规范.北京:气象出版社, 2007. [30] Jolliffe I T, Stephenson D B.Forecast Verification:A Practitioner's Guide in Atmospheric Science.John Wilrfgey & Sons Ltd, 2003:247. -
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