AMSU-A资料在台风模拟中的循环同化研究
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摘要
以WRF(V3. 7)中尺度模式及其三维变分同化系统(WRF-3Dvar)为平台,GFS(0. 5°×0. 5°)提供模式背景场,对AMSU-A微波辐射资料进行循环同化试验,研究台风数值模拟中AMSU-A资料的可用性及循环同化方法的可行性。以2014年第19号台风"黄蜂"为例,开展了控制试验和多组同化试验。同化试验主要包括循环同化常规观测资料和不同卫星测得的AMSU-A微波辐射资料。在对各试验初始场进行对比的基础上,比较了不同同化试验方案的模拟效果,结果表明:1) AMSU-A资料同化能有效地调整初始场,模拟的台风路径和强度都有一定的改善; 2)不同卫星探测的AMSU-A资料同化效果存在较大的差异,其中NOAA-15资料同化模拟台风"黄蜂"的路径和强度与实况最为接近; 3)循环同化是增加数值模式中资料使用量、改善台风模拟效果的有效方法。
Using mesoscale model WRF( V3. 7) and corresponding WRF-3 Dvar as platform,GFS( 0. 5° × 0. 5°) providing background field,cycle assimilation experiments are conducted on AMSU-A microwave data,to discuss the availability of AMSU-A data and the feasibility of cycle assimilation method in numerical simulation of typhoon. Typhoon No. 19"Vongfong"which happened in 2014 is simulated with a control experiment and several assimilation experiments. Assimilation experiments include cycle assimilation observation data and different satellite microwave radiation data( AMSU-A). On the basis of comparing the initial field,the simulation results of different assimilation tests are compared. The main conclusions are as follows. 1) AMSU-A data assimilation could effectively improve the initial field,and improve the track and intensity of typhoon simulation to some extent. 2) The assimilation effect of AMSUA data detected by different satellites are significantly different,the result of experiment with assimilation data detected by NOAA-15 is closest to reality. 3) The cycle assimilation is the effective method to increase data usage in numerical simulation and improve the typhoon simulation.
Using mesoscale model WRF( V3. 7) and corresponding WRF-3 Dvar as platform,GFS( 0. 5° × 0. 5°) providing background field,cycle assimilation experiments are conducted on AMSU-A microwave data,to discuss the availability of AMSU-A data and the feasibility of cycle assimilation method in numerical simulation of typhoon. Typhoon No. 19"Vongfong"which happened in 2014 is simulated with a control experiment and several assimilation experiments. Assimilation experiments include cycle assimilation observation data and different satellite microwave radiation data( AMSU-A). On the basis of comparing the initial field,the simulation results of different assimilation tests are compared. The main conclusions are as follows. 1) AMSU-A data assimilation could effectively improve the initial field,and improve the track and intensity of typhoon simulation to some extent. 2) The assimilation effect of AMSUA data detected by different satellites are significantly different,the result of experiment with assimilation data detected by NOAA-15 is closest to reality. 3) The cycle assimilation is the effective method to increase data usage in numerical simulation and improve the typhoon simulation.
引文
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[21]曹楚,王忠东,刘峰,等. 2013年“菲特”台风暴雨成因及湿位涡诊断分析[J].气象与环境科学,2016,39(4):86-92.
[22]陈联寿.热带气象灾害及其研究进展[J].气象,2010,36(7):101-110.
[23]刘武,李耀东,史小康. FY-2G地表温度反演产品改变模式初值对一次台风暴雨模拟的影响[J].气象与环境科学,2017,40(1):26-34.
[24]张雅斌,武麦凤,侯建忠,等.陕西4次台风远距离暴雨过程的水汽条件对比[J].干旱气象,2014,32(5):788-797.
[25]赵定池,陈俊,潘晓滨,等.一次暴雨过程的GPS掩星资料的同化应用[J].气象与环境科学,2015,38(3):87-93.
[2]陈联寿.西太平洋台风概论[M].北京:科学出版社,1979:8.
[3]陈联寿,许映龙.中国台风特大暴雨综述[J].气象与环境科学,2017,40(1):3-10.
[4]王业桂,张斌,蔡其发,等.不同卫星微波遥感资料同化对台风路径模拟的影响[J].大气科学,2018,42(2):398-410.
[5]English S J,Renshaw R J,Dibben P C,et al. A comparison of the impact of TOVS and ATOVS satellite sounding data on the accuracy of numerical weather forecasts[J]. Q J Roy Meteorol Soc,2000,126:2911-2931.
[6]薛纪善.气象卫星资料同化的科学问题与前景[J].气象学报,2009,67(6):903-911.
[7]Bouttier F,Kelly G. Observing-system experiments in the ECMWF4D-Var data assimilation system[J]. Q J Roy Meteorol Soc,2001,127:1469-1488.
[8]杨寅,韩威,董佩明. AMSU微波探测资料同化的质量控制方法概述[J].气象,2011,37(11):1395-1401.
[9]董佩明,薛纪善,黄兵,等.数值天气预报中卫星资料同化应用现状和发展[J].气象科技,2008,36(1):1-7.
[10]张利红,沈桐立,王洪利. AMSU资料变分同化及在暴雨数值模拟中的应用研究[J].高原气象,2007,26(5):1004-1012.
[11]朱国富,薛纪善,张华,等. GRAPES变分同化系统中卫星辐射率资料的直接同化[J].科学通报,2008,53(20):2424-2427.
[12]任强,董佩明,薛纪善.台风数值预报中受云影响微波卫星资料的同化试验[J].应用气象学报,2009,20(2):137-146.
[13]Zapotocny T H,Jung J A,Marshall J F L,et al. A two-season impact study of satellite and in situ data in the NCEP Global Data Assimilation System[J]. Weather and Forecasting,2007,22(4):887-909.
[14]Zapotocny T H,Jung J A,Marshall J F L,et al. A two-season impact study of four satellite data types and Rawinsonde Data in the NCEP Global Data Assimilation System[J]. Weather and Forecasting,2008,23(1):80-100.
[15]Jung J A,Zapotocny T H,Marshall J F L,et al. A two-season impact study of NOAA polar-orbiting satellites in the NCEP Global Data Assimilation System[J]. Weather Forecasting,2008,23(5):854-877.
[16]高山,江崇波,刘桂艳,等. WRF模式在大洋一号随船预报中的应用[J].海洋预报,2015,32(1):46-52.
[17]李兴武,董海萍,郭卫东,等. ATOVS不同卫星资料在台风模拟中的同化试验研究[J].热带气象学报,2012,28(2):157-166.
[18]黄昌兴,江郭双,李欣,等.影响山东半岛的两次台风暴雨对比分析[J].气象与环境科学,2015,38(3):70-77.
[19]葛战旗. 2013年8月沙澧河流域一次连续暴雨过程成因分析[J].气象与环境科学,2017,40(4):90-99.
[20]朱明,夏金.“苏拉”台风倒槽引发鄂西北特大暴雨的诊断分析[J].高原山地气象研究,2015,35(1):74-79.
[21]曹楚,王忠东,刘峰,等. 2013年“菲特”台风暴雨成因及湿位涡诊断分析[J].气象与环境科学,2016,39(4):86-92.
[22]陈联寿.热带气象灾害及其研究进展[J].气象,2010,36(7):101-110.
[23]刘武,李耀东,史小康. FY-2G地表温度反演产品改变模式初值对一次台风暴雨模拟的影响[J].气象与环境科学,2017,40(1):26-34.
[24]张雅斌,武麦凤,侯建忠,等.陕西4次台风远距离暴雨过程的水汽条件对比[J].干旱气象,2014,32(5):788-797.
[25]赵定池,陈俊,潘晓滨,等.一次暴雨过程的GPS掩星资料的同化应用[J].气象与环境科学,2015,38(3):87-93.